CALIBRATION OF MULTI-AXIS ACCELEROMETER IN VEHICLE NAVIGATION SYSTEM USING GPS DATA

Abstract: A navigation system includes a multi-axis accelerometer which can be installed in the vehicle in any unknown orientation. The orientation of the axes of the accelerometer is automatically determined by the navigation system based upon GPS data. The oriented signals from the accelerometer are then used to propagate the position of the vehicle.

Description: CALIBRATION OF MULTI-AXIS ACCELEROMETER IN VEHICLE NAVIGATION SYSTEM USING GPS DATA RArrecraoTTNn F TTT . TNV NTTQN [1] The present invention relates generally to vehicle navigation systems and more particularly to a system and method for calibrating accelerometers in the vehicle navigation system. [2] Some known vehicle navigation systems include sensors whose orientation relative to the vehicle must be known in order to provide dead-reckoning. One known navigation system includes an accelerometer suite comprising three orthogonally mounted accelerometers. The accelerometer provides acceleration signals in three mutually orthogonal axes. A first accelerometer is aligned vertically with the vehicle, i. e. , with gravity. A second accelerometer is aligned with the longitudinal axis of the vehicle to provide a signal indicating forward and rearward acceleration, from which speed can be determined. A third accelerometer, mounted orthogonally to the other two, provides lateral acceleration information, which is used to indicate change in heading. [3] In previous navigation system, the accelerometers had to be precisely aligned with the associated axes of the vehicle in order to provide accurate information. The accelerometer is mounted in a known orientation in the vehicle. Hardware, such as adjustment screws, is provided to fine tune the orientation of the three accelerometers to align the three accelerometers with the corresponding axes of the vehicle. Even after time-consuming adjustment, the accelerometers may not be properly aligned with the axes of the vehicle. The alignment may also change over time. [4] To eliminate these problems, a technique was developed in which there were methods to determine a linear transformation that can be applied to orthogonal 3 -axis accelerometer data and essentially ' rotate' (align) it to the vehicle body frame. This method has been very successful, but it requires the vehicle to stop and start several times. A particularly important application is witliin navigation products that are specifically designed to be semi-portable and/or temporarily installed within a vehicle. If the navigation system is moved to different vehicles frequently, the stop- start learn algorithm can become a little cumbersome. [5] Other sensors, such as angular rate sensors and compasses, also must be in a known orientation relative to the vehicle in order to provide dead reckoning information. STTMMAKV OF TTTF, TNVFNTTON [6] The present invention permits a sensor to be installed in the vehicle in any unknown orientation and be calibrated without starting and stopping the vehicle. The invention will be described with respect to a three-axis orthogonal accelerometer suite as an example, as other types of sensors would also benefit from this invention. The navigation system learns the orientation of the accelerometer suite based upon GPS data and propagates the position of the vehicle based upon the acceleration signals from the accelerometer after learning its orientation. [7] The navigation system determines the orientation of the accelerometer relative to the veliicle and generates a transformation matrix. The transformation matrix transforms signals from the accelerometer suite reference frame to the veliicle reference frame (which may be either the vehicle body reference frame or the vehicle' s wander-azimuth reference frame). The transformed acceleration signals are then used to provide dead reckoning. More particularly, a forward acceleration signal is used to determine the speed of the vehicle, while a lateral acceleration signal indicates change in the current heading of the vehicle. [8] One advantage to tins new method is that the system can detect any changes in the installation orientation. If the navigation system is placed on the vehicle floor or seat and if it moves a little for any reason, the new method will be able to detect and correct the misalignment while moving. This invention is a collection of new methods of determining the same transformation matrix and hence attaining correct alignment using GPS signals as opposed to stop-start maneuvers. The advantage of this invention is that the orientation can be learned even when the vehicle does not stop. [9] The accelerometer is installed into the vehicle in any orientation, unknown to the navigation system. Then, the navigation system compares changes in velocity (and/ or heading) as measured by the GPS data to the data from the accelerometers (or angular rate sensors). A transformation matrix of the sensor suite reference frame to the vehicle reference frame (the vehicle body or vehicle wander-azimuth frame) is generated. The transformation matrix converts the inertial sensor suite data to the desired reference frame. τraτF.F D srmPTTON OF TTTF DK AWTNC [10] Other advantages of the present invention will be readily appreciated as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings wherein: [11] Figure 1 is a schematic of the navigation system of the present invention installed in a vehicle; and [12] Figure 2 is a perspective view of the veliicle and accelerometer of Figure 1, with the accelerometer installed in an unknown orientation in the vehicle. DFTATT .n PFSCmPTTON OF A PKFFFRKFT) FMTtODTMFNT [13] The navigation system 20 of the present invention is shown schematically in Figure 1. The navigation system 20 includes a CPU 22 having RAM 23 and connected to a display 24, such as a high resolution LCD or flat panel display. The CPU 22 is also connected to an input device 26 such as a mouse, keyboard, key pad, remote device or microphone. Alternatively, the display 24 can be a touch screen display. The navigation system 20 further includes a storage device 28, such as a hard drive 28 or CD ROM, connected to the CPU 22. The storage device 28 contains a database 29 including a map of all the roads in the area to be traveled by the veliicle 32 as well as the locations of potential destinations, such as addresses, hotels, restaurants, or previously stored locations. The software for the CPU 22, including the graphical user interface, route guidance, operating system, position- determining software, etc may also be stored in storage device 28 or alternatively in ROM or flash memory. [14] The navigation system 20 preferably includes position and motion determining devices, such as a GPS receiver 34, a three-axis angular rate sensor suite 36, a three-axis compass 38, a wheel speed sensor 40 and a multi-axis accelerometer 42, all connected to the CPU 22 (connections not shown for simplicity). Such position and motion determining devices are well known and are commercially available. Based upon the particular needs of a particular system, any combination of these devices could be utilized. The navigation system also determines velocity from the GPS data from GSP receiver 34 generally utilizing the technique described in U.S. Patent No. 6,029, 111, which provides GPS velocity data, including heading, and delta velocity and delta heading information, and which is hereby incorporated by reference. [15] As is well known, the position and motion determining devices determine the position of the vehicle 32 relative to the database of roads utilizing dead-reckoning, map-matching, etc. Further, as is known in navigation systems, the user can select a destination relative to the database of roads utilizing the input device 26 and the display 24. The navigation system 20 then calculates and displays a recommended route directing the driver of the vehicle 32 to the desired destination. Preferably, the navigation system 20 displays turn-by-turn instructions on display 24, guiding the driver to the desired destination. [16] Figure 2 illustrates the multi-axis accelerometer 42 having axes jc, y, z installed in the vehicle 32 having a longitudinal axis a, a lateral axis b and a vertical axis c. The longitudinal axis a of the vehicle 32 is defined in the forward direction of the vehicle 32. The lateral axis b is orthogonal to both the longitudinal and vertical axes a, c and is defined to be in the right direction. The vertical axis c is orthogonal to the other axes a and b and is defined in the downward direction. The axes , y, z of the accelerometer 42 are mutually orthogonal, but are in an orientation which may be completely unknown relative to the vehicle axes a, b, c. [17] The navigation system 20 of the present invention automatically learns the orientation of the accelerometer 42 relative to the vehicle axes a, b, c and then utilizes signals from the accelerometer 42 to propagate the position of the veliicle 32. Although the invention will be described with respect to determining the orientation of an orthogonal axis accelerometer suite installed in the vehicle, the invention could also be used to determine the orientation of other inertial sensor suites, such as an angular rate sensor suite or a three-axis compass. [18] The methods for using GPS data to determine Sensor Suite orientation generally involve one or more orthogonal sensor systems such as a 3 -axis accelerometer or 3-axis compass or 3-axis angular rate sensor. These are three similar sensors mounted orthogonal to each other. [19] For all types of sensors, the algorithm is similar. In all cases, situations and events are used Jo accumulate and qualify data relevant to the type of sensor being oriented. If orientation is being determined for accelerometers, then GPS delta- Velocity (change in velocity) is used in conjunction with the three accelerometer data. Generally, all four of these values are accumulated during a qualification period. Qualification in this case will include the constraint that GPS delta-Heading is negligibly small. A recursive structure and post-processing algorithm are utilized to learn the required 3x3 orientation matrix from the qualified data. [20] In the case of an orthogonal 3-axis Angular Rate Sensor, qualified GPS delta- Heading and delta-delta-Altitude data are paired up with the three angular-rate sensor data streams. (In this case, since the rate-of-change of the rate-of- change of the GPS measured altitude is very noisy, then the system may converge on the correct result slowly.) [21] In the case of an orthogonal 3-axis magnetic sensor (compass), GPS heading is used in conjunction with a known model of the earth' s magnetic field (in three dimensions) along with the three magnetic flux values from the magnetic sensor elements. This is a special case where a known rotation matrix between the earth' s nominal magnetic field vector and the navigation reference frame (for example North, East, Down) is used. The model of the earth' s field must yield this transformation matrix as a function of the latitude and longitude. [22] In all cases, the three sensors generate data streams that correspond to a single reference stream. For this preferred embodiment, three accelerometers generate data streams and the GPS receiver generates a velocity data stream. The following steps are performed (under qualified conditions): [23] 1. Three Acceleration Values are Accumulated until: (a) A time limit is reached, or (b) An accumulation value is attained 2. The corresponding change in GPS velocity is computed: 3. The Four Data Values are used to update the Recursive Orientation Estimate [24] These steps are only performed under the qualified conditions when GPS is known to be of acceptable accuracy and if GPS delta-Heading is negligibly small. [25] There are actually three recursive updates to be performed, one for each sensor. Each further refines the estimate of that sensor' s gravity component and forward acceleration component. [26] There are various approaches to estimating these components. For this preferred embodiment, we will demonstrate a linear estimation technique that is common in the field of System Identification. (See State Variables for Engineers, 2nd. Ed.,DeRusso, Roy, Close, Desrochers, Wiley Interscience, Inc., 1998, chapter 8.) Note that the embodiment shown here does not include exponential forgetting, but that exponential forgetting can be added. [27] The following recursive equations are used: Initialization : Gravity Component Estimate θ = Re cursive Estimates Forward Component Estimate 1 h = New GPS Data GPS ΔHeading π(0) R 12 (0) ' P Initial Co var iance Estimates M 2 (o). Recursive Update Equations : K = Compute Gain P = (l - Kh τ )P <= Update Covariance θ = θ A- [K Accumulated Turn - (h τ θ))J <= Update Estimates [28] With the assumption of zero-mean-vehicle-pitch and zero-mean-vehicle-roll during the acquisition of data, these recursive equations produce estimates that can be used in the following section to generate a transformation matrix that will rotate the sensor reference frame data to the veliicle reference frame. With the assumption of constant-mean-vehicle-pitch and constant-mean- vehicle- roll during the acquisition of data, these recursive equations produce estimates that can be used in the following section to generate a transformation matrix that will rotate the sensor reference frame data to the wandering azmuth reference frame. (This is identical to the vehicle reference frame, but it is level on earth as opposed to level within the vehicle.) By way of example, in some locations, roads are contoured for water drainage. The wander-azimuth frame will orient the data exactly forward in the vehicle but the left and down axes are rotated so that down points nominally down on earth, not in the vehicle. This is our generally accepted mode of usage. the Orientation Matrix: There are six numbers generated by the above recursive equations. They represent two 3 -dimensional vectors: Gravity Component Estimate t Gravity = Gravity Component Estimate Gravity Component Estimate k Forward Component Estimate t Forward = Forward Component Estimate Forward Component Estimate k Vector Magnitudes are now computed: Gravity Component Estimate 2 + Gravity Gravity Component Estimate 2 + Gravity Component Estimate^. ΪForward Component Estimate 2 + \Forward\ = Forward Component Estimate 2 + ' Forward Component Unit Vectors are now computed: Gravity Component Estimate l 1 Unit Gravity = Gravity Component Estimate \Gravity\ Gravity Component Estimate k Forward Component Estimate t 1 Unit Forward : Forward Component Estimate \Forward\ Forward Component Estimate,. A Unit Left Vector is computed using a Cross Product: Unit Left = Unit Forward X Unit Gravity These nine numbers now form the Desired Transformation Matrix: Unit Forward ( Unit Forward Unit Forward k a = c\cι = Unit Left l Unit Left j Unit Left k Unit Gravity . Unit Gravity Unit Gravity k Example of Use: A. Sensor Reference Frame Acceleration A b = C s b A s <= Acceleration Transformed to Vehicle Body Frame — cT w A — r w A Acceleration Transformed to Wander Azimuth Frame Unit Forward, Unit Forward Unit Forward , Unit Left Unit Left Unit Left k Unit Gravity Unit Gravity Unit Gravity k Assuming Zero - Mean - Vehicle - Roll and Zero - Mean - Vehicle - Pitch : Then : C? = I c =ch A h = A„ [29] During normal operation of the vehicle, the orientation of the accelerometer 42 in the vehicle 32 may be continuously checked and updated. The navigation system 20 of the present invention provides an easy to install multi-axis accelerometer with improved accuracy. [30] In accordance with the provisions of the patent statutes and jurisprudence, exemplary configurations described above are considered to represent a preferred embodiment of the invention. However, it should be noted that the invention can be practiced otherwise than as specifically illustrated and described without departing from its spirit or scope.

Claims: CLAIMS 1. A vehicle navigation system comprising: a multiple-axis inertial sensor generating a plurality of sensor signals, each associated with one of said sensor axes; and a processor transforming said plurality of signals into motion information correlating to vehicle axes based upon GPS velocity data. 2. The vehicle navigation system of Claim 1 further comprising a transformation matrix relating said plurality of signals to said vehicle axes. 3. The vehicle navigation system of Claim 2 further comprising means for generating said transformation matrix. 4. A vehicle navigation system comprising: an inertial sensor generating a plurality of sensor signals each associated with a sensor axis of said sensor, said inertial sensor mounted in an unknown orientation in a vehicle; and means for propagating a position of a vehicle based upon said plurality of sensor signals. 5. The vehicle navigation system of Claim 4 further comprising means for transforming said plurality of sensor signals from said sensor axes to axes of said vehicle. 6. The vehicle navigation system of Claim 4 further comprising means for transforming said plurality of sensor signals from said sensor axes to an wander- azimuth reference frame of the vehicle. 7. The vehicle navigation system of Claims 4 or 5 further comprising a transformation matrix relating said plurality of signals to said vehicle axes. 8. The vehicle navigation system of Claim 4 or 6 further comprising a transformation matrix relating said plurality of signals to a wander azimuth frame of the vehicle. 9. The vehicle navigation system of Claims 7 or 8 further comprising a processor programmed to generate said transformation matrix. 10. A method for calibrating an inertial sensor in a vehicle navigation system including the steps of: a) receiving a plurality of signals from said inertial sensor; b) generating a transformation matrix based upon said plurality of signals and GPS data; and c) transforming said plurality of signals from said inertial sensor into a reference frame of the vehicle based upon said step b). 11. The method of Claim 10 further including the steps of: mounting said inertial sensor in the vehicle in an unknown orientation relative to said vehicle; and determining said orientation of said inertial sensor relative to the vehicle based upon said steps a) and b). 12. The method of Claims 10 or 11 further including the steps of: determining the orientation of the sensor relative to a longitudinal axis of the vehicle; determining the orientation of the sensor relative to gravity; and determining the orientation of the sensor relative to a lateral axis of the vehicle, generally orthogonal to the longitudinal axis and gravity. 13. The method according to any of Claims 10 to 13, further including the steps of propagating a position of the vehicle based upon the transformed signals

Inventors: BEGIN JOHN, CHEOK KA C, BEGIN, JOHN, CHEOK, KA, C.
Applicants: BEGIN JOHN, CHEOK KA C, MAGELLAN DIS INC, BEGIN, JOHN, CHEOK, KA, C., MAGELLAN DIS, INC.
Application year: 2001
Main classification: G01C
IPC codes: G01C21, G01C25