Source: http://www.google.com/patents/US20040193386?ie=ISO-8859-1
Timestamp: 2015-05-26 14:17:40
Document Index: 619619062

Matched Legal Cases: ['art 700', 'art 700', 'art 700', 'art 700', 'art 700', 'art 1400', 'art 1400', 'art 1400', 'art 1400']

Patent US20040193386 - Method of inferring rotorcraft gross weight - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsDescribed are systems and methods for determining the gross weight of an aircraft. A flight regime is determined based on one or more inputs. A neural net is selected based on a flight regime. The neural net inputs may include derived values. A first estimate of the gross weight is produced by the selected...http://www.google.com/patents/US20040193386?utm_source=gb-gplus-sharePatent US20040193386 - Method of inferring rotorcraft gross weightAdvanced Patent SearchPublication numberUS20040193386 A1Publication typeApplicationApplication numberUS 10/779,365Publication dateSep 30, 2004Filing dateFeb 13, 2004Priority dateNov 25, 2002Also published asUS7296006Publication number10779365, 779365, US 2004/0193386 A1, US 2004/193386 A1, US 20040193386 A1, US 20040193386A1, US 2004193386 A1, US 2004193386A1, US-A1-20040193386, US-A1-2004193386, US2004/0193386A1, US2004/193386A1, US20040193386 A1, US20040193386A1, US2004193386 A1, US2004193386A1InventorsTimothy Flynn, Robert Hess, Barbara NobleOriginal AssigneeFlynn Timothy D., Hess Robert Alan, Barbara NobleExport CitationBiBTeX, EndNote, RefManReferenced by (9), Classifications (10), Legal Events (3) External Links: USPTO, USPTO Assignment, EspacenetMethod of inferring rotorcraft gross weight
Density Ratio (σ) is represented as: Where OAT is outside air temperature (� C.); Hp is Barometric Altitude (ft). 17. The method of claim 16, wherein said neural net inputs include roll attitude and pitch attitude in accordance with the selected flight regime. 18. The method of claim 16, wherein one of said neural net inputs is a derived parameter based on at least one of roll attitude and pitch attitude in accordance with the selected flight regime. 19. The method of claim 1, wherein the neural net is included in a gross weight processor. 20. The method of claim 1, wherein the gross weight processor is included on the aircraft for which said weight is determined. 21. The method of claim 1, wherein the gross weight processor is included at a ground location and communicates with said aircraft. 22. The method of claim 1, wherein the one or more inputs include at least one of: a sensor measurement, manual input, data from a storage location. 23. The method of claim 1, further comprising: determining said flight regime as a hover flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, yaw rate, rate of climb, pitch attitude, roll attitude, drift velocity, ground speed, airspeed, and control reversal flag, wherein said landing flag indicates whether said aircraft is landing, said takeoff flag indicates whether said aircraft is in takeoff mode, and said control reversal flag indicates whether said aircraft is in a reversal mode. 24. The method of claim 23, wherein said landing flag indicates no landing, said takeoff flag indicates no takeoff, said weight on wheels indicates no weight on wheels, said control reversal flag indicates that said aircraft is not in reversal mode, said yaw rate has an approximate value within the inclusive range of: −2.5≦yaw rate 2.5 degrees/second, said pitch attitude is approximately 10 degrees, said rate of climb is approximately within the inclusive range of: −200≦rate of climb≦200 feet/minute, said roll attitude approximates a value within the inclusive range of: −6≦roll attitude≦3 degrees, said drift velocity approximates a value within the inclusive range of: −7≦drift velocity≦7 knots said ground speed approximates a value within the inclusive range of: −7≦ground speed≦7 knots, said airspeed is an approximate value less than or equal to 38 knots. 25. The method of claim 24, further comprising: determining that said aircraft is in a hover flight regime at a first point in time; and determining that said aircraft remains in said hover flight regime at a second later point in time if said airspeed at said second later point in time does not exceed 43 knots. 26. The method of claim 1, further comprising: determining said flight regime as a forward flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, yaw rate, rate of climb, pitch attitude, roll attitude, airspeed, control reversal flag, and sideslip, wherein said landing flag indicates whether said aircraft is landing, said takeoff flag indicates whether said aircraft is in takeoff mode, and said control reversal flag indicates whether said aircraft is in a reversal mode. 27. The method of claim 26, wherein said landing flag indicates no landing, said takeoff flag indicates no takeoff, said weight on wheels indicates no weight on wheels, said control reversal flag indicates that said aircraft is not in reversal mode, said yaw rate has an approximate value within the inclusive range of: −5≦yaw rate≦5 degrees/second, said pitch attitude is within the inclusive range of: −10≦pitch attitude<10 degrees, said rate of climb is approximately within the inclusive range of: −500≦rate of climb≦500 feet/minute, said roll attitude approximates a value within the inclusive range of: −10≦roll attitude≦10 degrees, said side slip approximates a value within the inclusive range of: −0.05≦side slip≦0, said airspeed is an approximate value greater than 38 knots. 28. The method of claim 27, further comprising: determining that said aircraft is in a forward flight regime at a first point in time; and determining that said aircraft remains in said forward flight regime at a second later point in time if said airspeed at said second later point in time is greater than 33 knots. 29. The method of claim 1, further comprising: determining said flight regime as a turn flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, roll attitude, airspeed, and rate of climb, wherein said landing flag indicates whether said aircraft is landing and said takeoff flag indicates whether said aircraft is in takeoff mode. 30. The method of claim 29, wherein said landing flag indicates no landing, said takeoff flag indicates no takeoff, said weight on wheels indicates no weight on wheels, said rate of climb is approximately within the inclusive range of: −500≦rate of climb≦500 feet/minute, said roll attitude approximates a value within the inclusive range of: −10≦roll attitude≦10 degrees, said airspeed is an approximate value greater than 38 knots. 31. The method of claim 30, further comprising: determining that said aircraft is in a turn flight regime at a first point in time; and determining that said aircraft remains in said turn flight regime at a second later point in time unless at least one of the following is true: roll attitude is outside of the range −7,+13, and said airspeed is less than 36. 32. The method of claim 1, wherein said one or more inputs are scaled within a predetermined range. 33. The method of claim 1, further comprising: determining a sensitivity of said weight with respect to a parameter used in determining said weight. 34. The method of claim 33, wherein said sensitivity of said weight with respect to said parameter is determined in accordance with a partial derivative of said weight with respect to said parameter. 35. The method of claim 34, wherein said weight is determined using a neural network and represented as: W
Where OAT is outside air temperature (� C.); Hp is Barometric Altitude (ft). 73. The computer program product of claim 72, wherein said neural net inputs include roll attitude and pitch attitude in accordance with the selected flight regime. 74. The computer program product of claim 72, wherein one of said neural net inputs is a derived parameter based on at least one of roll attitude and pitch attitude in accordance with the selected flight regime. 75. The computer program product of claim 57, wherein the neural net is included in a gross weight processor. 76. The computer program product of claim 57, wherein the gross weight processor is included on the aircraft for which said weight is determined. 77. The computer program product of claim 57, wherein the gross weight processor is included at a ground location and communicates with said aircraft. 78. The computer program product of claim 57, wherein the one or more inputs include at least one of: a sensor measurement, manual input, data from a storage location. 79. The computer program product of claim 57, further comprising code that: determines said flight regime as a hover flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, yaw rate, rate of climb, pitch attitude, roll attitude, drift velocity, ground speed, airspeed, and control reversal flag, wherein said landing flag indicates whether said aircraft is landing, said takeoff flag indicates whether said aircraft is in takeoff mode, and said control reversal flag indicates whether said aircraft is in a reversal mode. 80. The computer program product of claim 79, wherein said landing flag indicates no landing, said takeoff flag indicates no takeoff, said weight on wheels indicates no weight on wheels, said control reversal flag indicates that said aircraft is not in reversal mode, said yaw rate has an approximate value within the inclusive range of: −2.5≦yaw rate 2.5 degrees/second, said pitch attitude is approximately 10 degrees, said rate of climb is approximately within the inclusive range of: −200≦rate of climb≦200 feet/minute, said roll attitude approximates a value within the inclusive range of: −6≦roll attitude≦3 degrees, said drift velocity approximates a value within the inclusive range of: −7≦drift velocity≦7 knots said ground speed approximates a value within the inclusive range of: −7≦ground speed≦7 knots, said airspeed is an approximate value less than or equal to 38 knots. 81. The computer program product of claim 80, further comprising code that: determines said aircraft is in a hover flight regime at a first point in time; and determines said aircraft remains in said hover flight regime at a second later point in time if said airspeed at said second later point in time does not exceed 43 knots. 82. The computer program product of claim 57, further comprising: code that determines said flight regime as a forward flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, yaw rate, rate of climb, pitch attitude, roll attitude, airspeed, control reversal flag, and sideslip, wherein said landing flag indicates whether said aircraft is landing, said takeoff flag indicates whether said aircraft is in takeoff mode, and said control reversal flag indicates whether said aircraft is in a reversal mode. 83. The computer program product of claim 82, wherein said landing flag indicates no landing, said takeoff flag indicates no takeoff, said weight on wheels indicates no weight on wheels, said control reversal flag indicates that said aircraft is not in reversal mode, said yaw rate has an approximate value within the inclusive range of: −5≦yaw rate≦5 degrees/second, said pitch attitude is within the inclusive range of: −10≦pitch attitude<10 degrees, said rate of climb is approximately within the inclusive range of: −500≦rate of climb≦500 feet/minute, said roll attitude approximates a value within the inclusive range of: −10≦roll attitude≦10 degrees, said side slip approximates a value within the inclusive range of: −0.05≦side slip≦0, said airspeed is an approximate value greater than 38 knots. 84. The computer program product of claim 83, further comprising code that: determines said aircraft is in a forward flight regime at a first point in time; and determines said aircraft remains in said forward flight regime at a second later point in time if said airspeed at said second later point in time is greater than 33 knots. 85. The computer program product of claim 57, further comprising code that: determines said flight regime as a turn flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, roll attitude, airspeed, and rate of climb, wherein said landing flag indicates whether said aircraft is landing and said takeoff flag indicates whether said aircraft is in takeoff mode. 86. The computer program product of claim 85, wherein said landing flag indicates no landing, said takeoff flag indicates no takeoff, said weight on wheels indicates no weight on wheels, said rate of climb is approximately within the inclusive range of: −500≦rate of climb≦500 feet/minute, said roll attitude approximates a value within the inclusive range of: −10≦roll attitude≦10 degrees, said airspeed is an approximate value greater than 38 knots. 87. The computer program product of claim 86, further comprising code that: determines said aircraft is in a turn flight regime at a first point in time; and determines said aircraft remains in said turn flight regime at a second later point in time unless at least one of the following is true: roll attitude is outside of the range −7,+13, and said airspeed is less than 36. 88. The computer program product of claim 57, wherein said one or more inputs are scaled within a predetermined range. 89. The computer program product of claim 57, further comprising code that: determines a sensitivity of said weight with respect to a parameter used in determining said weight. 90. The computer program product of claim 89, wherein said sensitivity of said weight with respect to said parameter is determined in accordance with a partial derivative of said weight with respect to said parameter. 91. The computer program product of claim 90, wherein said weight is determined using a neural network and represented as: W
BACKGROUND OF THE INVENTION [0002] 1. Technical Field [0003] This application relates to estimating flight data, and more particularly to estimating the weight of an aircraft. [0004] 2. DESCRIPTION OF RELATED ART [0005] Flight parameters, and estimates thereof, may be used in connection with a variety of different applications. One such application is related to aircraft parts. Systems exist that may be used in assessing the health of aircraft parts as well as detection and prediction of part failure. Such systems may use a variety of different inputs, including flight parameters, in making determinations and predictions in connection with the aircraft parts. In order to produce accurate and reliable results, the systems may rely on the accuracy of the inputs. Inputs may include, for example, the gross weight of the aircraft. [0006] In connection with the aircraft gross weight, existing systems and techniques may use data gathered from sensors as well as crew entered values, such as weights of the cargo and passenger(s). It may also not be possible for the crew to enter these values given particular flight conditions. Such human entered values introduce a possibility of human error. Additionally, this human error may be difficult to quantify or bound. [0007] Thus, it may be desirable to provide for an efficient technique for determining the gross weight of an aircraft. It may be desirable that this technique provide an estimation of the gross aircraft weight in a deterministic and automatic fashion while minimizing the amount of human error. SUMMARY OF THE INVENTION [0008] In accordance with one aspect of the invention is a method for determining a weight of an aircraft comprising: determining a flight regime in accordance with one or more inputs; selecting a neural net in accordance with said flight regime; and determining said weight using said neural net. The neural net may be trained offline prior to determining said weight of said aircraft. The step of determining said weight of said aircraft may be performed during operation of said aircraft. The neural net may be one of a plurality of neural nets. The neural net may be a feedforward neural net. The neural net may include a single hidden layer. The neural net may have a same set of interconnections between each neuron in said hidden layer and an input layer, and a same set of interconnection between said each neuron and an output layer. Each of said neurons in said hidden layer may utilize a same sigmoidal activation function. The neural net may include between 20 and 35 neurons in said hidden layer. The weight may be used as an input to another process. The flight regime may be one of a plurality of flight regimes that are mutually exclusive from one another. The flight regime may be manually selected. The flight regime may be an effective flight regime including one or more actual flight regimes using the same set of one or more neural nets. One or more neural net inputs may be used as inputs to said neural net selected, and the one or more neural net inputs may include at least one derived parameter that is determined based on mathematical and physical relationships of measured data. The one or more neural net inputs may be a first number of derived parameters determined using a second number of raw data values, the second number being greater than said first number. One or more neural net inputs may include at least one of the following: [0009] Corrected Vertical Acceleration (cNz) represented as: c   N z = 1 + N z - ( 1 cos  [ φ ] ) [0010] Where [0011] NZ is Vertical Acceleration; [0012] φ is Roll Attitude; [0013] Torque Coefficient (Cq) represented as: C   q = Q ρ   A  ( Ω   R ) 2   = 412.0 / 1.00  .0 * ( Eng1Q + Eng2Q ) / 2.0 .0023769 * σ * π   R 2 * ( 2 * π * Nr 100 * 257.887 60 * R ) 2 [0014] Where Q is total torque (RPM); [0015] ρ is density (lb-sec2/ft4); [0016] A is the area of the main rotor disc (ft2); [0017] Ω is the rotation speed of the rotor (rad/s); [0018] R is the radius of the main rotor disc (ft); [0019] Nr is the main rotor speed (%); [0020] σ is the density ratio; [0021] Advance Ratio (μ) is represented as: μ = V Ω   R = KIAS * 1.6890 2 * π * Nr 100 * 257.887 60 [0022] Where KIAS is indicated airspeed in knots; [0023] Climb rate over tip speed (μc) is represented as: μ c = V c Ω   R = ROC / 60 2 * π * Nr 100 * 257.887 60 [0024] Where ROC is rate of climb (ft/min); [0025] Density Ratio (σ) is represented as: σ = 0.0023769 * ( 288.15 OAT + 273.15 ) *   ( 1 - ( 0.0019812 * Hp 288.15 ) ) 5.256 [0026] Where OAT is outside air temperature (� C.); [0027] Hp is Barometric Altitude (ft). [0028] The neural net inputs may include roll attitude and pitch attitude in accordance with the selected flight regime. One of said neural net inputs may be a derived parameter based on at least one of roll attitude and pitch attitude in accordance with the selected flight regime. The neural net may be included in a gross weight processor. The gross weight processor may be included on the aircraft for which said weight is determined. The gross weight processor may be included at a ground location and communicates with said aircraft. The one or more inputs may include at least one of: a sensor measurement, manual input, data from a storage location. The method may further comprise: determining said flight regime as a hover flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, yaw rate, rate of climb, pitch attitude, roll attitude, drift velocity, ground speed, airspeed, and control reversal flag, wherein said landing flag indicates whether said aircraft is landing, said takeoff flag indicates whether said aircraft is in takeoff mode, and said control reversal flag indicates whether said aircraft is in a reversal mode. The landing flag may indicate no landing, said takeoff flag may indicate no takeoff, said weight on wheels may indicate no weight on wheels, said control reversal flag may indicate that said aircraft is not in reversal mode, said yaw rate may have an approximate value within the inclusive range of: −2.5≦yaw rate 2.5 degrees/second, said pitch attitude is approximately 10 degrees, said rate of climb may be approximately within the inclusive range of: −200≦rate of climb≦200 feet/minute, said roll attitude may approximate a value within the inclusive range of: −6≦roll attitude≦3 degrees, said drift velocity may approximate a value within the inclusive range of: −7≦drift velocity≦7 knots, said ground speed may approximate a value within the inclusive range of: −7≦ground speed≦7 knots, said airspeed may be an approximate value less than or equal to 38 knots. The method may also include: determining that said aircraft is in a hover flight regime at a first point in time; and determining that said aircraft remains in said hover flight regime at a second later point in time if said airspeed at said second later point in time does not exceed 43 knots. The method may also include: determining said flight regime as a forward flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, yaw rate, rate of climb, pitch attitude, roll attitude, airspeed, control reversal flag, and sideslip, wherein said landing flag indicates whether said aircraft is landing, said takeoff flag indicates whether said aircraft is in takeoff mode, and said control reversal flag indicates whether said aircraft is in a reversal mode. The landing flag may indicate no landing, said takeoff flag may indicate no takeoff, said weight on wheels may indicate no weight on wheels, said control reversal flag may indicate that said aircraft is not in reversal mode, said yaw rate may have an approximate value within the inclusive range of: −5≦yaw rate≦5 degrees/second, said pitch attitude may be within the inclusive range of: −10≦pitch attitude≦10 degrees, said rate of climb may be approximately within the inclusive range of: −500≦rate of climb≦500 feet/minute, said roll attitude may approximate a value within the inclusive range of: −10≦roll attitude≦10 degrees, said side slip may approximate a value within the inclusive range of: −0.05≦side slip≦0, said airspeed may be an approximate value greater than 38 knots. The method may also include determining that said aircraft is in a forward flight regime at a first point in time; and determining that said aircraft remains in said forward flight regime at a second later point in time if said airspeed at said second later point in time is greater than 33 knots. The method may include determining said flight regime as a turn flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, roll attitude, airspeed, and rate of climb, wherein said landing flag indicates whether said aircraft is landing and said takeoff flag indicates whether said aircraft is in takeoff mode. The landing flag may indicate no landing, said takeoff flag may indicate no takeoff, said weight on wheels may indicate no weight on wheels, said rate of climb may be approximately within the inclusive range of: −500≦rate of climb≦500 feet/minute, said roll attitude may approximate a value within the inclusive range of: −10≦roll attitude≦10 degrees, said airspeed may be an approximate value greater than 38 knots. The method may include: determining that said aircraft is in a turn flight regime at a first point in time; and determining that said aircraft remains in said turn flight regime at a second later point in time unless at least one of the following is true: roll attitude is outside of the range −7,+13, and said airspeed is less than 36. One or more inputs may be scaled within a predetermined range. The method may include determining a sensitivity of said weight with respect to a parameter used in determining said weight. The sensitivity of said weight with respect to said parameter may determined in accordance with a partial derivative of said weight with respect to said parameter. The weight may be determined using a neural network and represented as: W ^ g  ( z ) = γ  [ b2 + ∑ i = 1 P   W2 i * γ  ( b1 i + ∑ j = 1 m   W1 i , j * z j ) ] [0029] where z is a vector of inputs, p is a number of neurons in the hidden layer, m is a number of inputs, W1ij is a weight of the jth input to the ith neuron in the hidden layer, b1i is a bias added to the ith neuron, W2i is a weight of the ith neuron to the output neuron, b2 is a bias added to an output neuron, and γ is the tanh function. The neural network may be a feedforward neural net with one hidden layer containing p sigmoidal neurons, and the sensitivity is represented as: δ   W ^ g  ( z ) / δ   z k = γ ′  [ b2 + ∑ i = 1 P  W2 i * γ  ( b1 i + ∑ j = 1 m   W1 i   j * z j ) ] *   ∑ i = 1 P  W2 i * W1 i , k * γ ′  ( b1 i + ∑ j = 1 m   W1 i   j * z j ) [0030] where γ′ is cos h−2. The sensitivity with respect to an input vector z having said parameter that is a kth parameter, zk, may be determined as a partial derivative of said weight with respect to the kth parameter evaluated in accordance with the input vector. [0031] In accordance with another aspect of the invention is a method of determining a weight of an aircraft comprising:receiving one or more values; and determining said weight using a Kalman filter wherein said one or more values are used as inputs to said Kalman filter. One or more measurements may be input to said Kalman filter, and the method may include: determining a flight regime in accordance with one or more regime measurements; selecting a function based on said flight regime; and determining a covariance associated with one of said measurements in accordance with said function. [0032] The flight regime may be the hover flight regime, and said function may determine said covariance associated with a weight estimate. The function may determine the covariance in accordance with body accelerations of said aircraft along x and z axes, roll attitude, pitch attitude, airspeed and altitude. One or more measurements may be input to said Kalman filter, said one or more measurements including at least one of: a weight estimate, and engine fuel flow rate. The weight estimate may be a predetermined value based on vehicle flight and performance data. The weight estimate may be based on manually entered data representing a sum gross weight of said aircraft. The flight regime may be manually determined. The flight regime may be determined in accordance with a predetermined mapping that maps one or more values to a particular flight regime, wherein a given set of one or more inputs values uniquely maps to a flight regime. The Kalman filter may produce an output used as an input to another component. [0033] In accordance with another aspect of the invention is a system for determining a weight of an aircraft comprising: a regime recognizer that determines a regime indicator in accordance with a portion of said one or more inputs; and a gross weight estimator that determines said weight of said aircraft, said gross weight estimator including at least one of: a Kalman filter, and one or more neural nets, and using at least one of said Kalman filter and a first of said one or more neural nets in determining said weight. The system may further comprise: an input processor that processes one or more inputs producing one or more processed inputs, said one or more inputs including at least one sensor measurement; and a portion of said one or more processed inputs are neural net inputs used by said one or more neural nets, and said gross weight estimator including: a neural net selector that selects a neural net in accordance with said regime indicator. The regime recognizer may be included in said input processor. The gross weight estimator may include one or more neural nets whose output, when said one or more neural nets is selected in accordance with said flight regime indicator, is an input to said Kalman filter. [0034] In accordance with another aspect of the invention is a method for determining an aircraft parameter comprising: determining a flight regime in accordance with one or more inputs; selecting a neural net in accordance with said flight regime; and determining said aircraft parameter using said neural net. The neural net may use at least one derived parameter determined from a relationship between one or more raw input values. [0035] In accordance with yet another aspect of the invention is a method of determining an aircraft parameter comprising: receiving one or more values; and determining said aircraft parameter using a Kalman filter wherein said one or more values are used as inputs to said Kalman filter. The method may include determining a flight regime in accordance with one or more regime measurements; selecting a function based on said flight regime; and determining a covariance associated with one of said measurements in accordance with said function. [0036] In accordance with another aspect of the invention is a system for determining an aircraft parameter comprising: a regime recognizer that determines a regime indicator in accordance with a portion of said one or more inputs; and an aircraft parameter generator that determines said aircraft parameter, said aircraft parameter generator including at least one of: a Kalman filter, and one or more neural nets, and using at least one of said Kalman filter and a first of said one or more neural nets in determining said aircraft parameter. [0037] In accordance with another aspect of the invention is a computer program product for determine a weight of an aircraft comprising code that: determines a flight regime in accordance with one or more inputs; selects a neural net in accordance with said flight regime; and determines said weight using said neural net. The neural net may be trained offline prior to determining said weight of said aircraft. The code that determines said weight of said aircraft is executed during operation of said aircraft. The neural net may be one of a plurality of neural nets. The neural net may be a feedforward neural net. The neural net may include a single hidden layer. The neural net may have a same set of interconnections between each neuron in said hidden layer and an input layer, and a same set of interconnection between said each neuron and an output layer. Each of the neurons in said hidden layer may utilize a same sigmoidal activation function. The neural net may include between 20 and 35 neurons in said hidden layer. The weight may be used as an input to another process. The flight regime may be one of a plurality of flight regimes that are mutually exclusive from one another. The flight regime may be manually selected. The flight regime may be an effective flight regime including one or more actual flight regimes using the same set of one or more neural nets. One or more neural net inputs may be used as inputs to said neural net selected, and the one or more neural net inputs may include at least one derived parameter that is determined based on mathematical and physical relationships of measured data. The one or more neural net inputs may be a first number of derived parameters determined using a second number of raw data values, the second number being greater than said first number. One or more neural net inputs may include at least one of the following: [0038] Corrected Vertical Acceleration (cNz) represented as: c   N z = 1 + N z - ( 1 cos  [ φ ] ) [0039] Where [0040] NZ is Vertical Acceleration; [0041] φ is Roll Attitude; [0042] Torque Coefficient (Cq) represented as: C   q = Q ρ   A  ( Ω   R ) 2   = 412.0 / 1.00  .0 * ( Eng1Q + Eng2Q ) / 2.0 .0023769 * σ * π   R 2 * ( 2 * π * Nr 100 * 257.887 60 * R ) 2 [0043] Where Q is total torque (RPM); [0044] ρ is density (lb-sec2/ft4); [0045] A is the area of the main rotor disc (ft2); [0046] φ is the rotation speed of the rotor (rad/s); [0047] R is the radius of the main rotor disc (ft); [0048] Nr is the main rotor speed (%); [0049] σ is the density ratio; [0050] Advance Ratio (μ) is represented as: μ = V Ω   R = KIAS * 1.6890 2 * π * Nr 100 * 257.887 60 [0051] Where KIAS is indicated airspeed in knots; [0052] Climb rate over tip speed (μc) is represented as: μ c = V c Ω   R = ROC / 60 2 * π * Nr 100 * 257.887 60 [0053] Where ROC is rate of climb (ft/min); [0054] Density Ratio (σ) is represented as: σ = 0.0023769 * ( 288.15 OAT + 273.15 ) * ( 1 - ( 0.0019812 * Hp 288.15 ) ) 5.256 [0055] Where OAT is outside air temperature (� C.); [0056] Hp is Barometric Altitude (ft). [0057] The neural net inputs may include roll attitude and pitch attitude in accordance with the selected flight regime. One of said neural net inputs may be a derived parameter based on at least one of roll attitude and pitch attitude in accordance with the selected flight regime. The neural net may be included in a gross weight processor. The gross weight processor may be included on the aircraft for which said weight is determined. The gross weight processor may be included at a ground location and communicates with said aircraft. The one or more inputs may include at least one of: a sensor measurement, manual input, data from a storage location. The computer program product may also include code that: determines said flight regime as a hover flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, yaw rate, rate of climb, pitch attitude, roll attitude, drift velocity, ground speed, airspeed, and control reversal flag, wherein said landing flag indicates whether said aircraft is landing, said takeoff flag indicates whether said aircraft is in takeoff mode, and said control reversal flag indicates whether said aircraft is in a reversal mode. The landing flag may indicate no landing, said takeoff flag may indicate no takeoff, said weight on wheels may indicate no weight on wheels, said control reversal flag may indicate that said aircraft is not in reversal mode, said yaw rate may have an approximate value within the inclusive range of: −2.5≦yaw rate 2.5 degrees/second, said pitch attitude may be approximately 10 degrees, said rate of climb may be approximately within the inclusive range of: −200≦rate of climb≦200 feet/minute, said roll attitude may approximate a value within the inclusive range of: −6≦roll attitude≦3 degrees, said drift velocity may approximate a value within the inclusive range of: −7≦drift velocity≦7 knots, said ground speed may approximate a value within the inclusive range of: −7≦ground speed≦7 knots, said airspeed may be an approximate value less than or equal to 38 knots. The computer program product may further comprises code that: determines said aircraft is in a hover flight regime at a first point in time; and determines said aircraft remains in said hover flight regime at a second later point in time if said airspeed at said second later point in time does not exceed 43 knots. The computer program product may also include: code that determines said flight regime as a forward flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, yaw rate, rate of climb, pitch attitude, roll attitude, airspeed, control reversal flag, and sideslip, wherein said landing flag indicates whether said aircraft is landing, said takeoff flag indicates whether said aircraft is in takeoff mode, and said control reversal flag indicates whether said aircraft is in a reversal mode. The landing flag may indicate no landing, said takeoff flag may indicate no takeoff, said weight on wheels may indicate no weight on wheels, said control reversal flag may indicate that said aircraft is not in reversal mode, said yaw rate may have an approximate value within the inclusive range of: −5≦yaw rate≦5 degrees/second, said pitch attitude may be within the inclusive range of: −10≦pitch attitude≦10 degrees, said rate of climb may be approximately within the inclusive range of: −500≦rate of climb≦500 feet/minute, said roll attitude approximates a value within the inclusive range of: −10≦roll attitude≦10 degrees, said side slip may approximate a value within the inclusive range of: −0.05≦side slip≦0, said airspeed may be an approximate value greater than 38 knots. The computer program product may further comprise code that: determines said aircraft is in a forward flight regime at a first point in time; and determines said aircraft remains in said forward flight regime at a second later point in time if said airspeed at said second later point in time is greater than 33 knots. The computer program product may also include code that: determines said flight regime as a turn flight regime in accordance with the following input parameters: landing flag, takeoff flag, weight on wheels, roll attitude, airspeed, and rate of climb, wherein said landing flag indicates whether said aircraft is landing and said takeoff flag indicates whether said aircraft is in takeoff mode. The landing flag may indicate no landing, said takeoff flag may indicate no takeoff, said weight on wheels may indicate no weight on wheels, said rate of climb may be approximately within the inclusive range of: −500≦rate of climb≦500 feet/minute, said roll attitude may approximate a value within the inclusive range of: −10≦roll attitude≦10 degrees, said airspeed may be an approximate value greater than 38 knots. The computer program product may also include code that: determines said aircraft is in a turn flight regime at a first point in time; and determines said aircraft remains in said turn flight regime at a second later point in time unless at least one of the following is true: roll attitude is outside of the range −7,+13, and said airspeed is less than 36. One or more inputs may be scaled within a predetermined range. The computer program product may also include code that: determines a sensitivity of said weight with respect to a parameter used in determining said weight. The sensitivity of said weight with respect to said parameter may be determined in accordance with a partial derivative of said weight with respect to said parameter. The weight may be determined using a neural network and represented as: W ^ g  ( z ) = γ  [ b2 + ∑ i = 1 P   W2 i * γ  ( b1 i + ∑ j = 1 m   W1 i , j * z j ) ] [0058] where z is a vector of inputs, p is a number of neurons in the hidden layer, m is a number of inputs, W1ij is a weight of the jth input to the ith neuron in the hidden layer, b1i is a bias added to the ith neuron, W2i is a weight of the ith neuron to the output neuron, b2 is a bias added to an output neuron, and γ is the tanh function. The neural network may be a feedforward neural net with one hidden layer containing p sigmoidal neurons, and the sensitivity is represented as: δ   W ^ g  ( z ) / δ   z k = γ ′  [ b2 + ∑ i = 1 P   W2 i * γ  ( b1 i + ∑ j = 1 m   W1 i , j * z j ) ] * ∑ i = 1 P   W2 i * W1 i , k * γ ′  ( b1 i + ∑ j = 1 m   W1 i , j * z j ) [0059] where γ′ is cos h−2. [0060] The sensitivity with respect to an input vector z having said parameter that is a kth parameter, zk, may be determined as a partial derivative of said weight with respect to the kth parameter evaluated in accordance with the input vector. [0061] In accordance with another aspect of the invention is a computer program product that determines a weight of an aircraft comprising code that: receives one or more values; and determines said weight using a Kalman filter wherein said one or more values are used as inputs to said Kalman filter. One or more measurements may be input to said Kalman filter, and the computer program product may further comprise code that: determines a flight regime in accordance with one or more regime measurements; selects a function based on said flight regime; and determines a covariance associated with one of said measurements in accordance with said function. The flight regime may be the hover flight regime, and said function may determine said covariance associated with a weight estimate. The function may determine said covariance in accordance with body accelerations of said aircraft along x and z axes, roll attitude, pitch attitude, airspeed and altitude. One or more measurements may be input to said Kalman filter, said one or more measurements including at least one of: a weight estimate, and engine fuel flow rate. The weight estimate may be a predetermined value based on vehicle flight and performance data. The weight estimate may be based on manually entered data representing a sum gross weight of said aircraft. The flight regime is manually determined. The flight regime may be determined in accordance with a predetermined mapping that maps one or more values to a particular flight regime, wherein a given set of one or more inputs values uniquely maps to a flight regime. The Kalman filter produces an output used as an input to another component. [0062] In accordance with another aspect is a computer program product for determining an aircraft parameter comprising code that: determines a flight regime in accordance with one or more inputs; selects a neural net in accordance with said flight regime; and determines said aircraft parameter using said neural net. The neural net may use at least one derived parameter determined from a relationship between one or more raw input values. [0063] In accordance with another aspect of the invention is a computer program product that determines an aircraft parameter comprising code that: receives one or more values; and determines said aircraft parameter using a Kalman filter wherein said one or more values are used as inputs to said Kalman filter. The computer program product may also include code that determines a flight regime in accordance with one or more regime measurements; selects a function based on said flight regime; and determines a covariance associated with one of said measurements in accordance with said function. BRIEF DESCRIPTION OF DRAWINGS [0064] Features and advantages of the present invention will become more apparent from the following detailed description of exemplary embodiments thereof taken in conjunction with the accompanying drawings in which: [0065]FIGS. 1-2 are steps of a flowchart of one method for performing interpolation. [0066]FIG. 3 is an example of an embodiment of a computer system in which the method steps of FIGS. 1-2 may be performed. [0067]FIGS. 4-7 are graphical representations in connection with an example in which the method steps of FIGS. 1-2 are performed. [0068]FIG. 8 is a graphical representation of a selected simplex for a point when the Delaunay technique is performed for the same point of interest as selected in the example of FIGS. 4-7. [0069]FIG. 9 is an example of a embodiment of a system that may be used in operation of an aircraft. [0070]FIG. 10 is a more detailed description of one embodiment of the input processor included in the system of FIG. 9. [0071]FIG. 11 is a more detailed description of one embodiment of gross weight estimator included in the system of FIG. 9. [0072]FIG. 12 is an example of one embodiment of a neural net that may be included in the gross weight estimator of FIG. 11. [0073]FIG. 13 is a flowchart of processing steps of one embodiment used in training, verification, and selecting one or more neural nets included in the gross weight estimator of FIG. 11. [0074]FIGS. 14-16 are examples of embodiments of effective regimes that may be included in an embodiment. [0075]FIG. 17 is an example illustration of actual regimes that may be included in a single effective hover regime in one embodiment. [0076]FIGS. 18-27 are examples of actual regimes that may be included in a single effective fast forward regime in one embodiment. [0077]FIGS. 28-29 are examples of actual regimes that may be included in a single effective turn regime in one embodiment. [0078]FIG. 30 is an example graphical illustration of sample results of the testing error distribution with neural nets. [0079]FIGS. 31-32 are example graphical illustrations of sample results of the Kalman filter performance. [0080]FIG. 33 is a flowchart of processing steps of one embodiment for estimating the gross weight of an aircraft using a Kalman filter. DESCRIPTION OF EMBODIMENT(S) [0081] Neural networks may be used to model various conditions, such as those described herein in connection with estimation of aircraft weight as well as a wide variety of other conditions. Part of effectively utilizing a neural network includes properly training the neural network using a good set of training data. It may be desirable to have the training data cover an entire range of expected inputs. However, it may not be possible to generate such data sets. In practice, it may be the case that only sparse experimental data is available making it difficult to properly train and verify the neural network. The sparse experimental data may not cover the desired range of input conditions. Interpolation techniques may be used to generate additional data. However, the particular interpolation techniques available for use may be dependent on the characteristics of the actual experimental data from which additional data is generated. For example, the experimental data may be characterized as not uniform and scattered limiting the use of certain interpolation techniques that may be better suited for use with more uniform data. The available interpolation techniques may be further limited by the data dimensionality of the model since particular interpolation techniques may not be practically used with data dimensions as described elsewhere herein in more detail. [0082] A majority of interpolation methods may be characterized as primarily suited for use with data points arranged in a regular rectangular grid, or data that may be characterized as generally uniform and regularly structured. That is, for example, suppose that each data point consists of an n-dimensional vector x and a scalar y. For the i-th coordinate of a vector x, the measurements available for each of mi discrete values are: xi(1), xi(2), . . . , xi(ni−1), xi(mi). Then, measurement or experimental data is also available for each combination of all discrete values x(k1, k2, . . . , kn)=[x1(k1), x2(k2), . . . , xn (kn)] making the total number of available measurements in the experimental data equal to the product of numbers mi. For example, if the data were two-dimensional (n=2), for uniform data sets there are m1 distinct values for the first coordinate of x, m2 distinct values for the second coordinate of x, a total number of data points represented as m=m1m2 , and all measurement values may be arranged into a two-dimensional array y(i,j), with i=1, . . . , m1, j=1, . . . , m2. Similarly, if the data were three-dimensional (n=3), for uniform data sets there are m1 distinct values for the first coordinate of x, m2 distinct values for the second coordinate of x, m3 distinct values for the third coordinate of x, a total number of data points represented as m=m1m2m3, and all measurement values may be arranged into a three-dimensional array y(i, j, k), with i=1, . . . , m1, j=1, . . . , m2, k=1, . . . , m3. More generally, for an n-dimensional case, there are m=m1m2 . . . , m1 measurement values arranged into an n-dimensional array y(j1, j2, . . . , jn), with ji=1, . . . , m1. Interpolation techniques may be used to estimate a value of the function for some intermediate value of x within the bounds given by limits xi(1) and xi(mi), for example, assuming that the discrete values for each coordinate of x are increasing. [0083] Different existing techniques used for interpolation of data described as above that may be characterized as regularly structured data include, for example, the multi-linear method, cubic method, and cubic spline method. Such methods are based on finding a hyper-rectangular cell of the grid containing the point x and then fitting a local approximating function whose values at the cell vertices are given by tabulated values of y. It should be noted that the size of an n-dimensional data table may grow quickly with respect to n, which may limit applicability of these interpolation approaches. Moreover, the use of these particular interpolation approaches may be better suited for use with regular or uniform experimental data set generation. [0084] It may be desirable to use interpolation techniques over data, as described above, that does not display a regular, uniform or rectangular structure as may be the case when utilizing experimental data. For example, assume that variables x may be measured but cannot be set to arbitrary values. Let the collected data set consist of m pairs (x(k), y(k)), k=1, . . . , m, such that the particular coordinate values xi(k) need not be the same for any two measurement points. Interpolation of scattered data may be more difficult in that certain interpolation techniques may not be suitable for use with such data. For example, one embodiment of a software product called Matlab provides only two such methods within its griddatan function for use with such non-uniform scattered data. These two such methods are known as the nearest neighbor interpolation and linear interpolation based on Delaunay tessellation. [0085] Advantages of the nearest neighbor approach are its speed and modest memory requirements and it scales quite easily to higher data dimensions. However, the nearest neighbor method may generate data points that are not sufficiently accurate and may result in a piecewise-constant interpolated function that is discontinuous along lines equidistant from two nearest neighboring points. Therefore applicability of nearest neighbor interpolation may be limited to instances in which collected data is relatively dense to limit the size of any discontinuity. Additionally, the nearest neighbor technique is sensitive with respect to scaling of the coordinates of x such that two different nearest neighbors may be generated for a same data point x in instances each having different scaling. [0086] Linear interpolation on Delaunay simplices results in an interpolation function that is everywhere continuous. The method is based on tessellation of the data set into disjoint simplices (n-dimensional polyhedra with n+1 vertices) such that a hypersphere circumscribed on vertices of any one simplex does not contain any other data point. Such tessellation is unique. Within each simplex, a linear function is fit such that its values at vertices are equal to the corresponding measurement values. In other words, linear functions fitted within neighboring simplices coincide on their boundaries, thus resulting in the overall surface being continuous. Therefore, the Delaunay-based technique may be used if accuracy and consistency of generated interpolation data are concerns. However, a limitation of the Delaunay technique is that the values at which the function is interpolated must fall within the convex hull of all the available data points. If it does not, an alternate interpolation technique, such as the nearest neighbor, may be used for nearest neighbor interpolation outside of the convex hull. [0087] Another drawback with the Delaunay technique is the marked increase in growth of the computational and storage requirements as the data dimensionality n increases. For example, the available implementations of Delaunay tessellation may not be practical for n>10. The inventor determined that using the Delaunay technique with a Matlab implementation on a personal computer with 4 GB of RAM was adequate storage for values of n<9, and additionally, limiting the data set size. For example, for n=6, approximately 2500 data points may be used, and for n=8, approximately 250 data points may be used. Such data storage requirements may not be well suited for use with higher values of n with the Delaunay technique. A user of Matlab may, for example, use a less accurate nearest neighbor interpolation technique with larger data dimensionality and larger data sets. Additionally, because of the combinatorial nature of the tesselation problem, the computation complexity increases rapidly with the dimension of the data set size making data sets with larger dimensions, such as more than n=11 or n=12, nonscalable for use with existing computer systems. [0088] What will now be described are techniques that may be used in generating data sets based on existing data that may be characterized as non-uniform and scattered in which the generated data is more accurate than that of the nearest neighbor technique, and the storage and computational costs are more scalable than those of the Delaunay technique as the data dimensionality increases. [0089] Described in following paragraphs are techniques in which some advantages of the Delaunay tessellation-based method are retained while simultaneously relaxing excessive computational requirements. As described elsewhere herein, one advantage of the Delaunay tessellation-based interpolation method over the nearest neighbor method is that the Delaunay tessellation-based utilizes information about the local trends contained in the data points that surround the point of interest. In contrast, this information is not utilized within the nearest neighbor method, which uses only one—a single value at the nearest data point. The technique described in following paragraphs utilizes this advantage such that described is an interpolation technique in which the interpolated value calculated is based on linear interpolation between the data points surrounding the point of interest. That is, a simplex is determined from selected data points such that the point of interest lies within the interior of the simplex. [0090] The following interpolation technique also relaxes the strict requirement that the surface of the overall interpolated function must be continuous, as in the case of the Delaunay tessellation. The following interpolation technique may select any simplex such that the point of interest x lies within its interior and is close to its vertices. Such simplices may be formed using n+K nearest neighbors of the point of interest. K may be characterized as a parameter selected to limit the number of candidates or points considered herein, K>1. The particular value for K used may vary in accordance with the particular considerations of each embodiment. Among those nearest neighbors, different sets of n+1 points may be used to form candidate simplices. Then a first simplex such that the given point x lies within its interior may be selected to perform linear interpolation. Determining if x lies within a simplex's interior may be performed by expressing x as a linear combination of n vertices of the simplex if the origin of the coordinate system is translated to the remaining n+1th vertex. If this linear combination is convex such that all coefficients are positive and summing to less then 1, then the point lies within the vertices forming the selected simplex. [0091] Referring now to FIGS. 1 and 2, shown is a flowchart of steps of an embodiment for performing interpolation. At step 12, a set of experimental data points and corresponding scalar values representing values of the unknown function relating the data points are obtained. This may be represented as a set of data points (x(k), y(k)) k=1, . . . , m where x(k) are n-dimensional vectors, and y(k) are the scalars representing values of the unknown function at points x(k). At step 14, a first data point x(k) is selected from the set of experimental data points, and a point {tilde over (x)} is selected as the interpolation point and represents the point at which an interpolated function value {tilde over (y)} is approximated. At step 16, a determination is made as to whether all data points x(k) in the experimental data set have been processed. If not, control proceeds to step 26 where a distance dk between {tilde over (x)} and every x(k) is determined as:
[0096] Control proceeds to step 30 where an n�n matrix A is formed from the shifted vertices. The matrix A may be represented as a series of column vectors where each column vector is one of the shifted vertices as:
[0210] Referring back to FIG. 12, the neural network 650 may be characterized as a feedforward neural network with a single hidden layer. The neural network 650 includes an input layer in which each input is connected to each neuron of the hidden layer. One embodiment of the neural net 650 uses as its activation function associated with each of the neurons of the hidden layer the hyperbolic tangent (tanh) function. It should be noted that although the embodiment 650 shown in FIG. 12 contains a single hidden layer, an embodiment of a neural network used in the gross weight estimator 510 may include a different number of hidden layers as well as a different number of neurons than as shown in the embodiment 650. Similarly, an embodiment may also include other variations of a neural network than as described herein such as, for example, using a different activation function, providing for different interconnections between the input layer and the hidden layer as well as in between one or more hidden layers. The particular neural network shown in the embodiment 650 of FIG. 12 should not be construed as a limitation of the techniques described here in connection with determining the gross weight of an aircraft. [0211] The neural nets 602 used within an embodiment of the gross weight estimator 510 may be selected from a pool of pre-trained neural networks. The particular neural nets 602 included in an embodiment of the gross estimator 510 may be determined as those neural networks providing the best estimate of gross weight for one or more particular flight regimes. Training of one or more static feedforward neural networks may be used to represent deterministic mappings for which a gross weight estimation by itself may be determined as an output value of the gross weight estimator 510. The neural nets 602 used in the gross weight estimator 510 may be trained off line prior to use. Subsequently, the one or more neural nets 602 may be operated and used on line, such as when an aircraft is in flight, to determine the gross weight of the aircraft. In other words, the particular neural nets 602 included in one embodiment of the gross weight estimator 510 are not trained on line and do not use any adaptive technique. The neural net weights determined during the offline training phase are fixed and used in subsequent on line operations of the gross weight estimator 510 such that if the same input vector is used with a neural network multiple times, the neural network produces the same output exhibiting deterministic behavior. [0212] Referring now to FIG. 13, shown is a flowchart 700 of steps that may be performed off line prior to operation of the gross weight estimator 510 in determining the aircraft's gross weight. The steps of flowchart 700 may be performed for one or more neural nets associated with one or more particular flight regimes. At step 702, compiled data representative of the actual system may be obtained. This compiled data may be, for example, from previous data acquisitions during operation of the aircraft in one or more different flight regimes. The compiled data may represent only a portion of the data actually used to train and or verify a neural net in subsequent processing steps. The compiled data may also come from experimentally generated data, such as in connection with a model. At step 704, the compiled data may be used in connection with creating training data. Any one of a variety of different techniques may be used in connection with creating the training data including, for example, one or more interpolation techniques as described elsewhere herein. At step 706, the one or more training parameters and appropriate neural network configurations may be determined. In one embodiment, multiple neural networks may be determined for one or more particular flight regimes, and one or more training parameters selected. At step 708, the one or more neural networks may be trained. At step 709, a determination is made as to whether the neural network training is done. This determination may be performed, for example, with a reserved portion of the training data as described elsewhere herein in more detail. Step 709 may be characterized as a first level determination as to whether the neural net training has been successful. If not, control proceeds to step 708 for further training. Otherwise, control processed to step 710 for verification. Step 709 processing may include, for example, determining if the neural net results for a predetermined number of iterations appear to be converging within a predetermined threshold. From step 710, control proceeds to step 712 where a determination is made as whether the neural net results of the one or more known networks for each particular flight regime are determined to be satisfactory. If so, control proceeds to 714 where one or more of the neural networks may be evaluated and selected as the ones to be used in connection with the gross weight estimator 510. For example, at step 714, one or more of the neural networks trained for a particular flight regime may be evaluated as “best” in accordance with various criteria. The criteria may include, for example, minimizing the root mean square (RMS) error between desired and actual output. At step 712, results may be determined as “satisfactory”, for example, if one or more neural nets produce results that converge within predetermined limits for the verification data testing. Additionally, an embodiment may determine that results are “satisfactory” if all of the error indicators associated with each net are clustered together within a certain amount of one another such that the overall variance of all results is within a predetermined amount. The particular criteria with a determination of “satisfactory” used at step 712 may vary with each embodiment. In one embodiment, the RMS error may be determined using the actual gross weight as determined by the neural network and the actual gross weight. It should be noted that the actual gross weight may be determined, for example, by adding together known values of helicopter weight, fuel, and the like. [0213] If it is determined at step 712 that the results are not satisfactory as produced by the verification step, control may return to either step 706 or 704 in an embodiment. Any one or more different elements used in determining the neural nets of step 714 may be adjusted. For example, the particular training parameters may be varied. The actual architecture of the neural network as well as activation functions used therein may be varied. Additional training data may be generated or checked for bad data in accordance with one or more verification techniques. An embodiment may perform any one or more of the foregoing as well as other well known variations to produce the one or more trained nets of step 714. Once one or more adjustments have been made in connection with the data, parameters and/or network configurations associated with the neural networks, the neural networks may be retrained and re-evaluated until the results are satisfactory as determined at step 712. Once the neural networks that have been trained in accordance with one or more particular flight regimes produce satisfactory results, the weight and bias vectors associated with a particular neural network may be fixed as a result of training and verification, and may be used in connection with the gross weight estimator 510 for a particular associated flight regime. Additional details of the processing steps are described in more detail elsewhere herein. [0214] In one embodiment, data used with the neural net training and/or verification of flowchart 700 may be gathered from an existing database of an SH-60B helicopter flight test data representing a 20,000 lb max gross weight (Wgmax) twin engine helicopter with various pilots. To reduce the number of neural net inputs, mathematical and physical relationships in the measured data may also be used to generate data reduction functions. In one embodiment, information from 9 sensors may be gathered and used to derive 5 neural net inputs. Sensed data may include both engine torques (Q1, Q2), Outside Air Temperature (OAT), pressure altitude (Hp), Indicated Airspeed (IAS), Rate of Climb (ROC), vertical acceleration (az), pitch attitude (Θ)), and roll attitude (Φ). [0215] It should be noted that the particular neural net inputs used in producing a gross weight estimation may vary in accordance with a particular regime definition. For example, for the steady state forward flight regime, attitudes (Roll and Pitch) may not be used as neural net inputs due to the nature of the regime criteria, reducing the number of neural net inputs for this regime to five (5). However, an embodiment may use these as neural net inputs directly or in connection with producing a derived parameter which is a neural net input as described elsewhere herein in more detail. [0216] The regime recognizer 554 illustrated in the on-line system of FIG. 10 may also be used in identifying what sets or vectors of training data may be used with a particular regime. In other words, the regime recognizer 554 may be used in both the off-line processing to produce the trained neural nets, and the on-line processing system 500 of FIG. 9. In connection with the off-line processing of step 706 of the flowchart 700, a particular set of data may be input to the regime recognizer 554 to determine the regime corresponding to the input data. One embodiment of the regime recognizer takes “v” aircraft parameters, such as attitudes, accelerations, velocities, Weight On Wheels (WOW), and the like, and divides the input space formed by these “v” parameters into a matrix of subspaces assigning a regime to each subspace. [0217] Referring now to FIGS. 14-16, shown are examples of the regimes that may be defined in one embodiment as used by a regime recognizer. It should be noted that in one embodiment described herein in connection with FIG. 14-16, the regime definitions may be characterized as “effective regimes” in which each “effective regime” includes one or more “actual regimes”. This may occur in an embodiment, for example, if each of the actual regimes included in an effective regime may use the same one or more trained neural nets. In an embodiment as described herein, an effective regime is a set union of all the designated actual regimes. [0218] Referring now to FIG. 14, an embodiment of a definition of the steady state hover regime is illustrated in accordance with the flight parameters of the table 820. The WOW (weight on wheels) flag with a value of true or 1 indicates that the aircraft is on the ground. In arrangement, the WOW may be a switch set in accordance with a deflection in wheel struts. The particular technique used in determining the WOW flag may vary in accordance with the particular aircraft. With reference to the drift velocity and ground airspeed in table 820 and elsewhere herein, the units are in knots. The control reversal flag indicates whether the aircraft has made a movement from one direction to another returning to the original position. Such a movement may be made, for example, by a pilot in reaction to a gust loading. [0219] It should be noted that in reading the data contained in tables of FIGS. 14-16, the some parameters appear in two rows of a table. This is to specify that the parameter range of values is formed by logically ORing the ranges specified with each row in the table. For example, in table 820, the Yaw rate for the hover regime is defined as those values which are both less than or equal to 2.5 and also greater than or equal to −2.5, inclusively. [0220] The hysteresis column of data in each of the tables of FIGS. 14-16 includes a value of NONE or a numeric value. The hysteresis value may be used in the actual on-line system in determining a flight regime indicator in real time, such as when used in the on-line processing. Values of flight parameters obtained in real time may not behave in a consistent fashion once a particular regime has been entered. For example, a parameter value may increase in accordance with one mathematical representation. However, as the value decreases, the same function or representation may not apply. As such, a parameter value which changes in small amounts close to a regime border condition may cause the regime definition to constantly change if hysteresis of the appropriate parameter(s) is not taken into consideration. Referring to the hover regime of table 820, one embodiment determines that hysteresis is taken into account for only the calibrated air speed. The value, 5, included in the table 820 for the calibrated airspeed hysteresis factor specifies that once the regime indicator determines that at a time t=n the hover regime is entered, the regime indicator value does not change for values of threshold+the hysteresis value (38+5, or 43) in this instance. In order to change regimes based on the calibrated airspeed parameter value once in the hover regime, the calibrated airspeed must be more than 43 knots. [0221] It should be noted that in this embodiment, the attitudes are in degree units, the airspeed is in knots, the yaw rate is in degrees/second, and the ROC is in feet/minute. [0222] Referring now to FIG. 15, an embodiment of a definition of the steady state forward flight regime is illustrated in accordance with the flight parameters of the table 840. [0223] Referring now to FIG. 16, an embodiment of a definition of the turn regime is illustrated in accordance with the flight parameters of the table 860. [0224] Other embodiments may use other regime definitions than as described herein. The foregoing regimes may be implemented in an embodiment of a regime recognizer as sets of conditionals, i.e. set of discrete and floating point comparisons combined with logical conditions. At any single point in time, only one regime is valid. Another way of stating this is that each regime is mutually exclusive from the other regimes so that, any given input vector of flight parameters maps to only a single regime. The parameters used to define a regime space may vary based on the platform. How the entire input space is broken into subspaces, the regimes to be detected, may vary with each embodiment and particulars associated therewith. For example, the regimes may vary with aircraft manufacturer. Additional parameters used to define an actual or effective regime may be added, or also varied from those specified for a particular platform by a particular embodiment. The parameters described herein, for example, may be used with the Navy SH-60B helicopter and an embodiment of a HUMS system as illustrated in FIG. 9. [0225] What will now be described are the “actual regimes” comprising each of the “effective regimes” used in one embodiment. In one embodiment, three effective regimes may be used: hover, forward flight, and turn, as illustrated and described elsewhere herein in connection with FIGS. 14-16. In one embodiment, it was determined that no more than five neural net inputs for any regime may be used. The particular number of neural net inputs may vary as described elsewhere herein. In one embodiment, for example, the number of neural net inputs considered was limited by the Neural Net toolbox of Matlab. Other embodiments may have other limitations and considerations in connection with selection of the number of neural net inputs as well as the neural net inputs themselves. In this embodiment, the flight envelop of possible regimes may be partitioned so that the neural nets may successfully converge on a gross weight estimate with a reasonable confidence. An embodiment for an SH-60B helicopter platform may do this by reducing the existing regime set (of “actual” regimes) into higher level (“effective”) regimes. The neural net inputs that may be included in an embodiment are described in more detail elsewhere herein. [0226] Referring now to FIG. 17, shown is an example 860 illustrating how multiple actual hover regimes are mapped to a single effective hover regime that may be included in one embodiment. It should be noted that the hover effective regime is described in connection with FIG. 14 elsewhere herein. The particular actual regimes and effective regimes included in an embodiment may vary with platform and other particulars of each embodiment. [0227] Regarding the effective hover regime defined herein, two actual regimes, the inground effect (IGE) and out of ground effect (OGE), may be combined into a single effective regime in an embodiment. In this embodiment, the neural net for the effective hover regime may take into account for any effects between the IGE and OGE by introducing radar altitude, such as height above ground level, as a parameter. As known to those of ordinary skill in the art, ground effect is an aerodynamic and rotordynamic concept. The outcome of IGE is a reduction of power required for net lift. Whether an aircraft is in the state of IGE or OGE may be estimated based on height above ground level, such as altitude, and may also vary with the diameter of the main rotor of the aircraft. [0228] Referring now to FIGS. 18-27, shown are examples of multiple actual fast forward regimes that may be mapped to a single effective fast forward flight regime that may be included in one embodiment. It should be noted that the fast forward effective regime is described in connection with FIG. 15 elsewhere herein. [0229] Referring now to FIGS. 28-29, shown are examples of multiple actual turn regimes that may be mapped to a single effective turn flight regime that may be included in one embodiment. It should be noted that the turn effective regime is described in connection with FIG. 16 elsewhere herein. It should be noted as used herein, AOB refers to “angle of bank”. [0230] The foregoing definitions of actual and effective regimes may be used in an embodiment of the regime recognizer 554 to output a regime recognizer indicator 508. It should be noted that an embodiment may include a default or catchall regime which is the selected regime in the event that no other regime criteria are met. The regime recognizer in an embodiment may include any type of regime mapping. However, a regime needs to be defined for the range of possible inputs. An embodiment may also combine a manual selection technique with the use of an automated or predefined mapping function for regime selection. [0231] It should also be noted that the foregoing regime definitions may be mapped to other regime definitions when the gross weight output, as produced using techniques described herein, is used as an input to other systems and/or components, such as an HUMS. [0232] In one embodiment to reduce the number of neural net inputs, the following derived parameters are calculated from mathematical and physical relationships in the measured data: Corrected   Vertical   Acceleration   ( cN z )   NN   INPUT   1 cN z = 1 + N z - ( 1 cos  [ φ ] ) [0233] Where [0234] NZ is Vertical Acceleration [0235] φ is Roll Attitude Torque   Coefficient   ( Cq )   Cq =  Q ρ   A  ( Ω   R ) 2 =  412.0 / 100.0 * ( Eng   1  Q + Eng   2  Q ) / 2.0 .00237769 * σ * π   R 2 * ( 2 * π * Nr 100 * 257.887 60 * R ) 2  NN   INPUT   2 [0236] Where Q is total torque (RPM) [0237] ρ is density (lb-sec2/ft4) [0238] A is the area of the main rotor disc (ft2) [0239] Ω is the rotation speed of the rotor (rad/s) [0240] R is the radius of the main rotor disc (ft) [0241] Nr is the main rotor speed (%) [0242] σ is the density ratio Advance   Ratio   ( μ )   NN   INPUT   3 μ = V Ω   R = KIAS * 1.6890 2 * π * Nr 100 * 257.887 60 [0243] Where KIAS is indicated airspeed in knots Climb   rate   over   tip   speed   ( μ c )   NN   INPUT   4 μ c = V c Ω   R = ROC / 60 2 * π * Nr 100 * 257.887 60 [0244] Where ROC is rate of climb (ft/min) Density   Ratio   ( σ )   σ = 0.0023769 * ( 288.15 OAT + 273.15 ) * ( 1 - ( 0.0019812 * Hp 288.15 ) ) 5.256 NN   INPUT   5 [0245] Where OAT is outside air temperature (� C.) [0246] Hp is Barometric Altitude (ft) [0247] The foregoing parameters, together with Roll Attitude and Pitch Attitude (when attitude is a factor) may be used as inputs to the appropriate neural net in accordance with the selected flight regime. [0248] In connection with obtaining the data in step 702 of FIG. 13, an embodiment may also remove invalid data using data quality checks. These data quality checks may include, for example, discarding “stale data” not received from a bus within a predetermined time period, such as 3 seconds, data out of a predefined range or rate of change, data which may be indicated as invalid from another source, and the like. In one embodiment, the data may be aligned to a once per second frame. [0249] In connection with step 704 of FIG. 13, compiled or gathered data may be used to create training data. In one embodiment, training data is generated for each of the foregoing defined parameters. Generation of data may include, for example, determining derived neural network inputs based on raw input data, such as sensor values. Generation of data may also include using, for example, an interpolation or other technique as described elsewhere herein and known to one of ordinary skill in the art. In one embodiment to create one training vector, a random selection for each variable, except Cq, is made from within its permitted range. An embodiment may scale the input values within a range, such as −1 to +1, and select a value randomly based on this scaled input value range for each parameter. Cq depends partially on σ. Given the value of σ, the permitted range of Cq is calculated. Then, the value of Cq is chosen randomly from within that range. This process is repeated as many times as needed to create sufficient training data. Sufficiency of training data may be determined in connection with later processing steps such as, for example, when increasing the number of data points between subsequent iterations fails to improve the neural network result, such as with respect to the RMS error rate associated with the subsequent iteration. As described elsewhere herein, different adjustments may be made when the neural networks do not produce satisfactory results. These adjustments may include, for example, increasing the amount of training data, changing the type of training data, altering the architecture of the neural network, and the like. [0250] In connection with generation of training data of step 704 based on a smaller amount of gathered data using the foregoing defined neural net inputs, any one or more different data generation techniques may be used. For example, an embodiment may use the interpolation technique described elsewhere herein. An embodiment may also use other multivariable interpolation techniques, such as, for example, Matlab's routines for Delaunay tessellation, and then using nearest neighbor interpolation. In one embodiment, the basis for the interpolation is the original scaled data in which the original input data is scaled or normalized to a value in the range from −1 to +1, inclusively. It should be noted that such techniques for data generation are well-known. For example, Delaunay tessellation as described in Aurenhammer, F., “Voronoi diagrams—A survey of a fundamental geometric data structure,” ACM Computing Surveys, 1991, 23:345-405. [0251] In one embodiment, the neural net architecture for each neural net trained at step 708 is illustrated in FIG. 12 with a single hidden layer of 35 neurons. In arriving at the choice of neural network in an embodiment, several different variations may be tested. For example, the inventors used several different feedforward neural nets with the single hidden layer and other particulars described elsewhere herein varying the number of neurons in the hidden layer. Based on experience, the inventors started with 20 neurons and tried varying the number of neurons in increments of 5. It was found that a neural net with 35 neurons converged for the fast forward flight regime with the least amount of error. An embodiment may use any one of a variety of different tools in connection with training neural networks. For example, creation and training of neural nets may be performed using Matlab's Neural Networks Toolbox. The neural network giving the least RMS testing error may be selected. In one embodiment, the RMS testing error is that obtained on the entire set of training data generated at step 704. [0252] In connection with the actual training of the neural nets at step 708, a percentage of the original data from step 702, such as 10%of the data, may be reserved and used in connection with verification at step 710. The remaining 90%may be used to generate training data used to train the nets in step 708. In one embodiment, the verification in step 710 may be performed by cross-validation of the neural net results. In one embodiment, the original input data obtained at step 702 may be split randomly into ten partitions, divided as evenly as possible. For each of the ten partitions, 10%of the original input data may be reserved for use in the verification step 710. The remaining 90% of the data of each partition may be used to create the training data in step 704 for training the five nets for the fast forward steady state flight regime as described above in step 708. [0253] It should be noted that an embodiment may use more than 10 partitions and different techniques in connection with selection of a neural net for each regime. The particular number of partitions may vary in accordance with the amount of data available. [0254] In connection with the outputs of the neural nets, the values in one embodiment are scaled outputs between −1 and 1, inclusively. In this embodiment, a neural net is said to converge with a threshold value of 0.001, i.e., if the weight updates reach an equilibrium such that the difference between two successive weight values for two successive iterations is less than 0.001. Other embodiments may use other criteria in connection with determining convergence. [0255] It should be noted that in connection with the processing steps of flowchart 700 of FIG. 13, there are many possible sources of error, including, but not limited to one or more of the following:
[0297] The corresponding transition matrix (φ) (of size n�n) which relates the states at a future time to the current states is given by: ϕ = ( 1 - Δ   t - Δ   t 0 0 1 0 0 0 0 1 0 0 0 0 1 ) KALMAN   EQTN   9 [0298] H represents the m�n measurement transformation matrix. It relates the measurements to the states given by: H = ( 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 ) KALMAN   EQTN   10 [0299] The measurements are also affected by a random measurement error ν, which is assumed to have covariance R, which is an m�m diagonal matrix. Q, the process noise, is an n�n diagonal matrix. Note that the elements of Q and R do not need to be constant. For example, since the ‘pseudo-measurement’ of WH is only accurate at hover, the element of R associated with WH may be varied as the vehicle maneuvers from hover to non-hover flight conditions. As confidence in the measurement decreases, the measurement's corresponding element in R increases. The matrix Q confidence corresponds to the confidence in the mathematical model as represented by the state equations of the Kalman filter. For the element of Q, it should be noted that as the confidence in the mathematical state equation model decreases, a corresponding element in Q increases. [0300] An embodiment may use a function to vary the element of R associated with WH:
[0328] wherein B and Bs represent the values for the hover weight measurement noise, Bs being the smoothed value generated using the first order lag filter. For time = i (i > 0): RWH (i) = (k3*RWH (i−1) + k4) / (1+k3); if Bs(i−1) < k5 then RWH (i) = (k3*RWH (i−1) + k6) / (1+k3); B(i) = k2; if h > hmin & h < hmax & V < Vmin /* If hover conditions exist */ B(i) = |(ax − G sin(θ)) (az − G − cos(θ) sin(φ)))| Bs(i) = ( k7 gs(i−1) + k8 g(i) ) / (k7 + k8) [0329] The foregoing function B may be characterized as continuous function producing a peak and valley as specific predetermined values for a given flight regime. As described elsewhere herein, the function B represents the level of confidence in the value associated with the value, the weight pseudo-measurement, in the hover regime. Although the foregoing function B is for use with the hover weight measurement RWH, other functions may be determined for use with other regimes and other values of R varying inversely with the level of confidence in the particular value at a point in time. For example, an embodiment may include as an input to the Kalman filter the neural network estimated weight in the hover regime. In determining the neural net estimate weight's corresponding element in the R matrix, RMS error or standard deviation information may be obtained as determined from the neural network training. As the confidence in the neural network estimated weight for the hover regime decreases, its corresponding element in the R matrix should increase. [0330] The constants k1-k8 are determined by trial and error such that the estimated and actual gross weight track correctly and have a reasonable dynamic response. Their values are all positive and non-zero. If properly defined, the value of RWH should increase as Bs increases and the level of confidence in the corresponding WH measurement value decreases. (e.g., as the value of the smoothed blending function increases, confidence decreases that the aircraft is in a hover condition). Likewise, RWH should decrease as Bs decreases. [0331] The foregoing definition for RWH may be used in an embodiment in connection with a weight value in the hover regime. Other values of R may be determined and used in other regimes and/or for other values. [0332] In connection with the Q and R matrices, Q may be characterized as process noise or model error. Q values may be determined empirically using known data from previous runs as well as on a trial and error. Q may be tuned or selected for each filter for each particular application. R may be characterized as representing the model noise used to blend out or appropriately weight corresponding measurements. The matrix R is recomputed each frame. WH may be determined for each frame based on other values related to one another as defined, for example, in a flight manual. The fuel flow inputs wf1 and wj2 may be measured directly or indirectly in an embodiment. The bias weight term, bw, in the foregoing is determined by the Kalman filter processing steps. In summary, for each new set of inputs to the Kalman filter, the following are updated: all elements of the measurement vector z, elements of the vector x (including the bias), some entries of the transition matrix involving the difference in time elements (in the event that the time steps are not constant), selection of a particular R function based on regime, updating of terms/variables of the selected R function, the Kalman gain, the updated state vector, and the filter error covariance matrix. [0333] Using the foregoing, a recursive filter scheme can be utilized in which the initial state estimates are blended with the measurements using known physical relationships, such as fuel depletion and hover performance. In one embodiment, a discrete, linear Kalman filter may be used. [0334] The inventors used flight data from a twin-engine helicopter to evaluate the performance of the Kalman filter for estimating vehicle gross weight. A series of runs were performed for various flight operations and for a series of simulated weight errors. In the flight data, fuel flow rate was not directly measurable. Engine performance data was used to develop a model of fuel flow rate as a function of engine torque, altitude, and outside air temperature. In addition, the helicopter airdata transducer only gave an airspeed measurement down to 30 kts. As airspeed is used to set the element of R associated with the hover gross weight measurement error, the associated calculation had to account for the rather broad range of airspeed between 0 and 30 kts. [0335] As illustrated in FIGS. 31 and 32 based on results obtained by the inventors, the Kalman filter is able to estimate the weight bias within 44 lbs. for a 600 lb. weight offset error, and 32 lbs. for a −1200 lb. weight offset error. Note that in these two illustrations of FIGS. 31 and 32, the performance of the Kalman filter is represented by the ESTIMATED curve. These results may be characterized as illustrating favorable performance of the Kalman filter in determining aircraft gross weight given the lack of fuel flow rate and low airspeed information in the particular runs performed by the inventors. Note that the filter would not provide any improvement in the foregoing gross weight estimation if there were no hover maneuvers during the flight. [0336] An embodiment may use one aspect of the Kalman filter formulation in determination of a lower bound for the state estimates. The well-known Cramer Rao lower bound may be determined from the system Fisher Information matrix, as described, for example, in T. M. Cover and J. A. Thomas, Elements of Information Theory, John Wiley & Sons, Inc, 1991; L. L. Scharf, Statistical Signal Processing, Addison-Wesley, 1991 and B. Noble and J. W. Daniel, Applied Linear Algebra, Prentice-Hall, 1988. As is also well-known, the Fisher Information matrix is a measure of the information content of the measured signal relative to a particular parameter. The Cramer-Rao bound is a lower bound on the error variance of the best estimator for the given system. The Cramer Rao bounds can be determined and used to assess the quality of the gross weight estimates, such as in connection with the weight bias, bW. [0337] In an embodiment utilizing both the neural net(s) and the Kalman filter, as described in connection with FIG. 11 for example, the neural net output may be used as a ‘pseudo-measurement’ in the filter's measurement vector, z. The corresponding element of the measurement covariance matrix, R, may then be updated to reflect the relative accuracy of the neural net output. Additionally, use of a corresponding value in the R matrix for a particular pseudo-measurement may be used as a weighting or confidence factor to use a pseudo-measurement from one regime to be used in determining the weight for a second different regime. Use of the Kalman filter in an embodiment not only allows for the estimate to be propagated into regime space unknown by the neural net, but the use of Cramer-Rao lower bounds calculation provides a continual measure of confidence. This confidence information may be used as a qualifier, forcing the structural usage calculations to assign worst case damage when confidence falls below predetermined limits. The stochastic nature of the Kalman filter enables calculation of the Cramer-Rao lower bounds. [0338] It should be noted that an embodiment may use the same set of aircraft sensor data in connection with the neural net formulation and the Kalman filter formulation. [0339] The foregoing describes techniques used to determine gross weight using sensed data. A neural net may use empirical relationships existing in the data to make a gross weight determination. A recursive state filter, the Kalman filter, may also be used separately, or in combination with, the neural net(s) in making a gross weight determination. In one embodiment described herein, the Kalman filter may be used to blend the neural net gross weight determination with other inputs to make final gross weight determination. [0340] Referring now to FIG. 33, shown is a flowchart of processing steps that may be performed in an embodiment to estimate the gross weight of an aircraft. The processing steps of flowchart 1400 summarizes the steps of one embodiment in which the Kalman filter and the neural net techniques as described herein are included. Additionally, the flowchart 1400 uses an output of the neural net, a first gross weight estimate, as an input to the Kalman filter in determining a final gross weight estimate, such as output 512 included in FIG. 11. [0341] At step 1402, one or more neural nets are trained and selected for inclusion in the gross weight estimator 510. It should be noted that step 1402 may be performed offline prior to execution of the gross weight estimator, such as during online flight operation of the aircraft. The remaining steps of flowchart 1400 may be performed when the gross weight estimator is running online in determining the gross weight of the helicopter or other aircraft. At step 1404, a set of sensor measurements and other inputs are obtained. At step 1406, the flight regime is determined based on sensor measurements and the other inputs. At step 1408, one or more neural net inputs are determined based on the flight regime determined at step 1406. At step 1410, one or more neural nets are selected based on the flight regime. At step 1412, the neural net(s) generate output(s) providing a first estimate of the gross weight. At step 1414, the Kalman filter measurements are determined including the first gross weight estimate. At step 1416, the appropriate R function(s) are selected for the first gross weight, and possibly other weight estimates, based on the flight regime. At step 1418, the final gross weight estimate is determined using the Kalman filter. At step 1420, the Kalman filter state values are updated for the next iteration using the next set of inputs. At step 1422, a determination is made as to whether processing is complete. In this embodiment, the gross weight estimator may keep running as long as the unit is enabled for processing. If processing is complete, processing stops. Otherwise, control proceeds to step 1404 to obtain the next set of data. [0342] It should be noted that the processing steps of flowchart 1400 are an example steps that may be performed in one embodiment of the gross weight estimator using both neural nets and a Kalman filter. Other embodiments may include and/or utilize just one of the foregoing components. An embodiment may also use other inputs to the Kalman filter than as described herein and other variations of the neural net, parameters, and the like than as described herein. The specific examples and illustrations should not be construed as a limitation of the techniques described herein. [0343] It should be noted that the foregoing techniques used for gross weight estimation may be used as a cross-checking of other gross weight data or as a replacement for other gross weight data. Although the foregoing embodiments illustrate the use of techniques with neural networks and/or Kalman filters for determining aircraft weight, techniques described herein may be used in connection with determining other aircraft parameters. [0344] While the invention has been disclosed in connection with various embodiments, modifications thereon will be readily apparent to those skilled in the art. Accordingly, the spirit and scope of the invention is set forth in the following claims. Referenced byCiting PatentFiling datePublication dateApplicantTitleUS7623066 *Nov 12, 2004Nov 24, 2009Motorola, Inc.Satellite positioning system receiver time determination in minimum satellite coverageUS8311970Apr 19, 2007Nov 13, 2012Cmte Development LimitedPayload estimation of weight bearing machinery using multiple model adaptive estimator system and methodUS8630767 *Dec 3, 2007Jan 14, 2014Nira Dynamics AbEstimation of the load of a vehicleUS8744651 *Feb 18, 2011Jun 3, 2014Sikorsky Aircraft CorporationMethod of determining a maneuver performed by an aircraftUS8768556 *Nov 12, 2010Jul 1, 2014Elbit Systems Ltd.Protection envelope switchingUS8909453Dec 20, 2012Dec 9, 2014Bell-Helicopter Textron Inc.System and method of measuring and monitoring torque in a rotorcraft drive systemUS20110066322 *Dec 3, 2007Mar 17, 2011Rickard KarlssonEstimation of the load of a vehicleUS20110125346 *Nov 12, 2010May 26, 2011Elbit Systems Ltd.Protection envelope switchingUS20110264310 *Feb 18, 2011Oct 27, 2011Sikorsky Aircraft CorporationMethod Of Determining A Maneuver Performed By An Aircraft* Cited by examinerClassifications U.S. Classification702/173, 706/16International ClassificationG06F17/17, G01G19/07Cooperative ClassificationG06F17/175, G06F17/17, G01G19/07European ClassificationG06F17/17M, G06F17/17, G01G19/07Legal EventsDateCodeEventDescriptionApr 28, 2015FPAYFee paymentYear of fee payment: 8May 13, 2011FPAYFee paymentYear of fee payment: 4May 24, 2004ASAssignmentOwner name: SIMMONDS PRECISION PRODUCTS, INC., VERMONTFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:FLYNN, TIMOTHY D.;HESS, ROBERT ALAN;NOBLE, BARBARA;REEL/FRAME:015359/0514;SIGNING DATES FROM 20040330 TO 20040405RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services