Patent Description:
Certain weather conditions pose hazards for navigating aircraft. Severe turbulence, thunderstorms, and others can be relatively readily detected with the aid of modern instrumentation and sensors and avoided by a pilot or automated navigation system. The buildup of ice on airfoils and rotor blades, however, persists as an aerodynamic-profile-modifying challenge. Ice buildup on aircraft can increase drag by increasing the area of the airfoil profile, can decrease the ability to generate lift by altering the airfoil camber, can add potentially significant weight, and can often engender vibration of the aircraft owing to imbalance based on ice accumulation on the structure being asymmetrical. Inflight-operative anti-icing systems to preclude ice accumulation can often require prohibitive amounts of power. Deicing systems to remove already-accumulated ice can prove inadequate or have unacceptable drawbacks.

Rotor icing poses a significant flight safety hazard to helicopters that do not have rotor deicing systems. In crewed helicopters, pilots are therefore careful to avoid or quickly exit icing conditions. Helicopter UAVs (unmanned aerial vehicles) are as prone to icing as manned helicopters, but do not have the human pilot's eyes or experience to avoid or quickly detect icing. Commercially available fixed-wing-type ice detectors are slow to sense icing on a helicopter where ice can accrete much faster on rotor blades than on other parts of the airframe. Even small accumulations of rotor ice can quickly lead to loss of control and/or loss of aircraft.

Machine learning (ML) is a subset of artificial intelligence (AI) in which a computer uses algorithms and statistical models to accurately perform tasks without using explicitly coded instructions after having analyzed a learning or training data set, in effect relying on patterns and inferences to generalize from past experience. Accordingly, ML-based systems can be capable of solving problems not previously seen or considered and for which it would not be possible to code for every individual case. Types of ML algorithms include, among others, supervised learning, unsupervised learning, and feature learning. Types of ML models that can be trained on the training data include artificial neural networks, decision trees, support vector machines, regression analysis models, Bayesian networks, genetic algorithms, principal components analysis, and cluster analysis.

From <CIT> a method and a system for predicting potential icing conditions is known. The system comprises a sensor interface configured to receive temperature and humidity measurement. A circuitry is configured to calculated an icing threat index based on the temperature and humidity measurement.

From <CIT> a system and method for aircraft flight control by recommending aircraft flight trajectories based on an estimated contamination risk is known. The system uses machine learning techniques for determining a risk of the aircraft flight's encounter with the ice water contents present in an atmosphere and for adjusting at least one trajectory of the aircraft based on the estimated ice water content risk.

One example includes a system for aircraft icing prediction according to claim <NUM>.

Another example includes a method, executed on an aircraft icing prediction computer system, of predictingicing according to claim <NUM>.

Yet another example includes an aircraft with an airframe to which are mounted an outside air temperature sensor and a dew point sensor to collect measurements of outside air temperature and dew point. The aircraft also includes computing circuitry configured to perform supervised learning comprising regression analysis of the collected outside air temperature and dew point measurements to determine a risk of icing of the aircraft.

Probable accretion of ice on an airframe, rotors, or engine inlets of an aircraft can be detected, for example, by sensor probes configured to detect present environmental icing conditions, based on which an alert can be generated to a pilot or other crewmember who can factor the alert into aircraft navigation decision-making. Continuing to fly in such environmental icing conditions may result in the build-up of significant ice load that could either impact aerodynamic performance of the aircraft or put an unacceptable amount of weight on the aircraft. The consequent additional drag and/or vibration, and/or reduced engine power, engine cooling, and/or lift could lead to an unacceptable level of fuel consumption or engine overheating, hindering accomplishment of mission objectives, or lead to loss of control and/or catastrophic loss of aircraft. However, the usefulness of the detection of present environmental icing conditions is based on the premise that the airframe can tolerate some level of icing. Uncrewed aircraft, which may be smaller, lighter, and/or rotor-based, including, for example, quadcopters or small helicopters such as the Northrop Grumman MQ-<NUM> Fire Scout, may not be equipped with systems for icing remediation and may be virtually totally intolerant of ice accretion, such that detection of present environmental icing conditions may only provide information that comes too late to save the aircraft or its mission objectives. Turbine engines are also vulnerable to foreign object damage (FOD) from ice. Accordingly, the systems and methods described herein provide predictive detection of icing conditions in advance of flight into such conditions, enabling preemptive navigation action and maneuvering out of the potential icing conditions, making it possible to fly without ice ever being accumulated on the aircraft.

Although advance detection of icing conditions may in some cases be aided with the use of a weather radar, a full weather radar system can require too much power and can weigh too much for a smaller aircraft to be equipped with such a radar system. The amount of time of advance warning of icing conditions can also be a critical factor in, for example, avoidance of obstacles or terrain or providing time to negotiate with air traffic control to de-conflict ice avoidance maneuvers with other aircraft traffic. The need to making navigation decisions and plan, negotiate, and execute maneuvers within a limited timeframe can place stress on an operating environment that can impact human pilots or remote operators and autonomous systems alike. Accordingly, the systems and methods described herein can advantageously provide icing prediction without the use of weather radar, at low cost, with minimal power consumption, and minimal weight, while still detecting icing conditions as far in advance of hazardous conditions as possible, thereby reducing the stress of the operating environment.

<FIG> illustrates an example aircraft system <NUM> implemented with a machine-learning-based icing predicting system. The system <NUM> includes an aircraft <NUM>, which is illustrated as a helicopter with a single main rotor, but can, in various examples, be a propeller plane, a jet plane, a quadcopter, or another form of aircraft or multi-mode-mobility vehicle. In some examples, aircraft <NUM> can be crewed with at least one human pilot or navigator aboard, or in other examples can be remotely piloted or semi- or fully autonomous.

The aircraft <NUM> includes one or more environmental sensors <NUM> configured to measure ambient conditions external to the aircraft. The aircraft further includes a computer system <NUM> communicatively coupled to the sensors <NUM> and configured to process environmental data from the sensors with an icing prediction decision engine. Data acquisition circuitry (not shown) can provide an appropriate interface between the sensors <NUM> and the computer system <NUM>, and as such, can be considered to be part of either or both of the environmental sensors <NUM> or the computer system <NUM>, to provide environmental data based on the measurements of the environmental sensors <NUM> to computer system <NUM>. Environmental sensors <NUM> can include, for example, one or more temperature sensors and one or more dew point sensors. In some examples, environmental sensors <NUM> do not include a weather radar, and aircraft <NUM> is not otherwise provided with an onboard weather radar. Examples of suitable dew point sensors include Michell Instruments Easidew transmitters and samplers and the Edgetech Instruments Model <NUM> Vigilant aircraft hygrometer.

The aircraft <NUM> can further include flight controls <NUM>, which in some examples can be communicatively coupled to the computer system <NUM> to receive flight control inputs from computer system <NUM>. The flight controls can be configured to control aerodynamic surfaces, engine power, drag system deployment (e.g., of a streamer or parachute), or other systems to provide effective navigation or emergency recovery of the aircraft <NUM>. Computer system <NUM> may also provide inputs to one or more icing remediation systems (not shown), such as heaters or vibrators configured to melt or shake off ice from critical control surfaces, such as rotor or propeller blades, or from engine air intakes. In some examples, however, aircraft <NUM> is not equipped with any icing remediation systems and will thus be required to rely on navigation to minimize or avoid icing. In various examples, an onboard autonomous control system can be configured to generate flight maneuver commands based on a recommended icing avoidance maneuver and to actuate flight controls <NUM> with the maneuver commands to execute the recommended icing avoidance maneuver, or to recommend such maneuvers to an onboard flight control computer.

In examples where aircraft <NUM> is crewed, aircraft <NUM> can further be equipped with a user interface (not shown), which can include a traditional aircraft instrumentation panel or can include or be limited to a video screen-based interface (e.g., a touch-screen interface) configured for navigating the aircraft. A user interface can additionally or alternatively be provided at a ground station <NUM>, which can provide supervisory control of aircraft <NUM> or can completely control aircraft <NUM> in examples where aircraft <NUM> is uncrewed and not autonomous. Information, including navigation information and icing prediction information, can be transmitted from aircraft <NUM> to ground station <NUM> via a wireless link <NUM> (e.g., a radiofrequency or free-space-optical link). Navigation controls or suggestions can be provided from the ground station <NUM> to the aircraft <NUM> over the wireless link <NUM>. In some examples, the ground station <NUM> may be only an air traffic control station with which the aircraft <NUM> is in communication and through which aircraft <NUM> may need to negotiate icing avoidance maneuvers that are de-conflicted with traffic patterns of other aircraft. In some examples, not illustrated, computer system <NUM> is not aboard aircraft <NUM> but is instead installed at ground station <NUM>, such that measurements from sensors <NUM> are transmitted over link <NUM> and processed at ground station <NUM> to supply navigation commands or other instructions back to aircraft <NUM> over link <NUM>. In still other examples, computer system <NUM> and its functions are distributed and/or redundantly duplicated between aircraft <NUM> and ground station <NUM>.

<FIG> illustrates a path of flight <NUM> of aircraft <NUM> through a series of air mass zones <NUM>, <NUM>, <NUM>, <NUM>, <NUM> of progressively increasing probability of icing. Although path <NUM> is illustrated for simplicity as being straight and horizontal, this path can take on any number of changes in altitude or heading, with any number of waypoints. Likewise, although the zones <NUM>, <NUM>, <NUM>, <NUM>, <NUM> are illustrated for simplicity as being arranged horizontally with respect to each other, in some examples the zones may be stacked vertically or in any other configuration. In the illustrated example, the aircraft <NUM> takes off in conditions of zero probability of icing <NUM> and will likely suffer catastrophic failure or mission endangerment if it progresses along initial flight path <NUM> to a zone of conditions of extremely high probability of icing <NUM>. The danger of zone <NUM> may not be known or detectable at the time of takeoff or otherwise at the time of planning of route <NUM>.

As it transitions through one or more air masses, aircraft <NUM> can continuously monitor and trend ambient conditions to provide an early indication of the potential for ice accumulation, e.g., on rotor blades. As examples, these ambient conditions can include outside air temperature (OAT) and dew point. Measurement of temperature can assist in defining a trend towards a freezing point. Ice forms under conditions where the temperature is conducive to the formation of ice, but aircraft <NUM> may fly through an air mass that is warmer than the freezing point and still accumulate ice, as where colder air above aircraft <NUM> permits frozen precipitation to fall into warmer air where aircraft <NUM> is operating. In contrast, flight in temperatures below freezing may pose only a negligible risk of icing provided there is adequate separation between the outside air temperature and the dew point. As the difference between outside air temperature and the dew point is reduced, and the outside air temperature is below or near freezing, the probability of ice accumulation increases. Dew point data collected by aircraft <NUM> can assist in trending a point where the ambient air reaches a saturation point where moisture is present. Thus, measurement and analytical combination of both temperature and dew point can provide a more accurate indication of icing potential than can be provided by either of these measurement s taken alone.

Computer system <NUM>, whether, as illustrated, aboard aircraft <NUM> or at ground station <NUM>, may process data inputs, including from continuously measured environmental data, with an icing prediction decision engine configured to develop an icing probability trend, and from this trend, a probability metric (e.g., in percent chance of icing) along with a time or distance to such probability metric may be reported. As the aircraft <NUM> travels into different weather situations, a computed percent chance of icing may change, such that at one point in time, computer system <NUM> may compute, and potentially report, as via a user interface, a <NUM> percent chance of icing, whereas at a later point in time, computer system <NUM> may compute an <NUM> percent chance of icing. As computer system <NUM> plots this information as the icing probability trend, based on the changing conditions, the computer system may predict, as examples, that a one hundred percent chance of entering icing conditions will develop in another <NUM> minutes of travel, or in another <NUM> miles of travel. Computer system <NUM> may therefore produce an alert to this effect and/or suggest a change in course to avoid icing conditions. Threshold limits dictating at what percentage probability an alarm is generated, or a maneuver is suggested or executed, may be set by operator or a developer.

If an icing probability trend derived from measured environmental data indicates a progressive increase risk of icing conditions, then icing avoidance or remediation action may be required. Aircraft <NUM> may therefore issue an alert to an onboard crewmember and/or a remote pilot, who may then take appropriate action, or, if aircraft <NUM> is autonomous, may automatically negotiate and initiate, at an action point <NUM>, an icing avoidance maneuver <NUM> and/or may activate icing remediation systems, if so equipped. When remotely controlled, aircraft <NUM> can also be configured to automatically initiate icing avoidance maneuver <NUM> and/or may activate icing remediation systems after some timeout period during which no response is received from a remote operator. The avoidance maneuver <NUM>, whether controlled by a human pilot or autonomously, may include an altitude change, a waypoint adjustment, or a return to base (RTB), and may involve any number or combination of control surface or vehicle power adjustments to effectuate the maneuver <NUM>. If the trend indicates a constant ice threat level within adequate safety margins, then flight may be continued along the initially intended path <NUM>. Likewise, if the trend indicates decreasing probability of icing conditions, then flight may be continued along the initially intended path <NUM> (a "no-issue" scenario). In addition to providing a probability value that aircraft <NUM> will enter icing conditions along its current flight path, the system <NUM> can provide a value of confidence in the probability value based on the maturity of the current data set. As actual data icing condition data is collected over time, from the aircraft <NUM> and/or from multiple like aircraft, the increase in confidence relative to the prediction produced by the system <NUM> can be realized.

Icing avoidance maneuver <NUM> may be initiated at an action point <NUM>, or at an earlier or later action point <NUM>, <NUM>, <NUM>. The multiple action points <NUM>, <NUM>, <NUM>, <NUM> may be defined off-set with respect to each other along initial path <NUM>. Action points <NUM>, <NUM>, <NUM> may be defined off-set with respect to probable point of icing <NUM> to accommodate an operator-defined amount of acceptable risk of icing and to provide a margin of safety or to account for pressure differences induced by rotor blades of the aircraft <NUM> that may cause the precipitation of moisture due to a change in vapor pressure. For example, taking action at point <NUM> after aircraft <NUM> is already in conditions of extremely high probability of icing <NUM> may be too late to save aircraft <NUM> from catastrophic icing, whereas taking action at earlier action point <NUM> may provide ample time to plan, negotiate, and execute an avoidance maneuver <NUM> such that aircraft <NUM> may enter elevated-probability icing conditions <NUM> but will not enter extremely-high-probability icing conditions <NUM>.

In still other instances, an operator may decide, owing, for example, to the relative importance of the mission and the relative expendability of the aircraft, that a higher risk of icing is acceptable, and, in accordance with user-configurable parameters setting this level of risk, the icing prediction decision engine may instead decide to execute the icing avoidance maneuver, if any, at a later action point <NUM>, whereas in instances calling for minimized aircraft loss tolerance and/or lesser mission criticality, the icing prediction decision engine may instead decide to execute the icing avoidance maneuver at an earlier action point <NUM>. The placement of action points can be done by the icing prediction decision engine executed by computer system <NUM> and can be designable through engineering configurable parameters used to adjust or tune the icing prediction decision engine executed by computer system <NUM>.

<FIG> illustrates an example icing prediction system <NUM> including an icing prediction decision engine <NUM>. Icing prediction decision engine <NUM> may, for example, comprise a Bayesian artificial neural network (BNN) configured to process inputs <NUM>, <NUM>, <NUM>, <NUM>, <NUM> to provide outputs <NUM>. In some examples, icing prediction decision engine <NUM> can be implemented in hardware as an application-specific integrated circuit (ASIC) or using a digital signal processor (DSP) or a field-programmable gate array (FPGA). In other examples, icing predication decision engine <NUM> may be implemented in software as machine-readable instructions stored on one or more non-transitory computer-readable media executable by one or more general-purpose computer processors, which may be included as part of a multi-function air vehicle computer or as a separate dedicated computer module the sole functions of which are to predict icing and generate alerts and/or avoidance maneuvers. In any case, icing prediction decision engine may be thought of as being comprised by computer system <NUM> of <FIG>, which may be onboard, ground-based, or distributed.

Icing prediction decision engine <NUM> may, for example, utilize machine learning algorithms to provide a probability that an aircraft is approaching icing conditions such that one or more icing avoidance maneuvers may be taken proactively before the aircraft accumulates an intolerable amount of ice. The icing prediction decision engine <NUM> can, for example, be configured to calculate the probability of entering icing conditions based on acquired real-time measurement data as supplied by a predictive ice detection sensor suite <NUM> as compared to historical data in database <NUM> informed by supervised learning <NUM> and/or unsupervised learning <NUM> over a number of epochs. Icing prediction decision engine <NUM> then may provide outputs <NUM> including probability of entering airspace with icing conditions and a metric of confidence regarding the determination of the probability of entering airspace with icing conditions.

In addition to receiving as inputs and considering measured data from sensor suite <NUM> and user-defined tuning of key decision parameters <NUM>, such as an acceptable level of risk of icing, icing prediction decision engine <NUM> can also consider other aircraft information <NUM> as may be available, such as current airspeed of the aircraft, current altitude of the aircraft, time of year, global positioning system (GPS) data, terrain or topographic map data, aircraft traffic data as may be reported by a control tower or home base, and current weather condition data as may be reported to the aircraft (such as fronts and air-mass changes). Icing prediction engine <NUM> can further consider information pertinent to the aircraft and its payload, as may be stored in configuration file <NUM>. Configuration file <NUM> may store information regarding the tolerance to icing of the aircraft platform. For example, the configuration file for a small helicopter drone may quantify that the drone has a zero percent icing tolerance, effectively indicating that any ice accumulation at all will result in loss of the drone. By contrast, a configuration file for a larger aircraft may quantify a greater icing tolerance. Configuration file <NUM> may also store information regarding the performance capabilities of the aircraft, such as information regarding how quickly a given maneuver can be executed, in view of the flight control systems with which the aircraft is equipped, the power available to the aircraft, the weight of the aircraft, and the weight of the aircraft's payload. Whereas the tuned parameters <NUM> are indicative of what the user finds acceptable in terms of icing conditions, the configuration file <NUM> is unique to the aircraft platform. Examples of a user-defined decision parameter <NUM> is a stored input indicative of the level of computed probability that an icing condition will be encountered that must be reached, or the level of required confidence in an icing prediction, for an icing risk alert or icing avoidance maneuver to be triggered. Outputs of icing prediction decision engine <NUM> can also be stored back to database <NUM> to inform future decision-making, thus enlarging the "library of learning" from which icing prediction decision engine <NUM> may draw.

In some examples, information from user-defined parameters <NUM> and configuration file <NUM> may conflict. For example, configuration file <NUM> may indicate that an aircraft has complete intolerance to icing, but, in view of the importance of a mission and/or the disposability of the aircraft, user-defined parameters <NUM> may indicate that some risk of icing should be tolerated. Thus, in some examples, prediction engine <NUM> can be configured to assign precedence to the user-defined parameters <NUM> over data from the configuration file <NUM> in making determinations about acceptable levels of icing risk. In some examples, the configuration file <NUM> can provide a baseline, and the library of learning in database <NUM> can adjust the confidence as learning data matures.

A local library of learning can be stored within an onboard memory storage (e.g., associated with computer <NUM>), continuously updated during flight, and drawn upon by aircraft <NUM> during flight to inform icing probability determinations. Additionally or alternatively, a centralized library of learning file can be stored at ground station <NUM>, periodically or continuously updated by aircraft <NUM> or other similar aircraft either wirelessly during flight or via a wired or wireless post-flight data transfer made upon return to base, and drawn upon by aircraft <NUM> and/or made be made accessible to other similar aircraft to inform icing probability determinations either made by aircraft <NUM> or other similar aircraft. For example, the centralized library of learning can be made available to multiple aircraft for data sharing through a general network at the operator's base <NUM>. The centralized library of learning can encompass all learning performed by all aircraft associated with the centralized library of learning, and can be continuously or periodically synchronized with one or more local libraries of learning as may be stored on these individual aircraft. In some examples, all similar aircraft operate off of the same centralized library of learning at all time. In other examples, each aircraft is provided with its own local library of learning that is only periodically updated with information from the centralized library of learning and/or itself used to update the centralized library of learning. Accordingly, the centralized library of learning can over time build up knowledge from multiple similar aircraft, which accumulated knowledge can be made available to any or all of these aircraft.

Icing prediction decision engine <NUM> can compute, among other outputs <NUM>, a probability of entering icing conditions and a percent confidence in this probability determination. Based on, among other factors, the computed probability exceeding a threshold determined by user-defined decision parameters <NUM>, such as an acceptable level of risk, decision engine <NUM> can take any of a variety of actions appropriate to the circumstances, aircraft configuration, and confidence level. As one example, where the aircraft is human-piloted, system <NUM> can be configured to issue an alert <NUM>, e.g., via a user interface, to an onboard pilot or other crew member or a remote pilot or operator of the aircraft. This alert can take on one of a number of alert levels, e.g., from moderate alert to severe alert, and may, in some cases, be accompanied by a suggested avoidance maneuver. As one example, a generated verbal message may inform a pilot that icing is predicted and tell a pilot to pitch up or down to increase or reduce altitude to a suggested safe level altitude. As another example, detailed control instructions and/or a map of a suggested new route may be displayed on a visual display available in the flight deck of a crewed aircraft or to a remote operator.

Where the aircraft is remotely piloted from a ground station <NUM> (so-called "human-in-loop" control), the alert and/or suggested maneuver may lead ground-station supervisory control to enter maneuver commands or approve suggested maneuver commands <NUM>, which may then be transmitted back to the aircraft for execution by the aircraft's flight control system <NUM>. As another example, where the aircraft is semi-autonomous and involves functions where a remote operator need not approve of an aircraft action beforehand but retains the authority to veto the action before the execution of the action or to abort the action once it has begun (so-called "human-on-loop" control), a ground station operator may cancel or modify a maneuver prior to or during execution by the flight control system <NUM>. As still another example, where the aircraft is fully autonomous (so-called "human-out-of-the-loop" control), an onboard autonomous control system <NUM>, communicatively coupled to and in receipt of icing probability information <NUM> from the icing prediction decision engine <NUM>, may formulate flight maneuver commands <NUM>, as from a recommended icing avoidance maneuver <NUM> provided from decision engine <NUM>, for execution by the flight control system <NUM>. In this human-out-of-the-loop scenario, the onboard autonomous control system <NUM> can formulate its flight maneuver commands <NUM> based also on fail-safe behavior rules <NUM>, designed, for example, to prevent attempted execution of a maneuver unsafe for the aircraft or its payload.

The icing prediction system <NUM> can be configured to provide different levels of action or alerts based, for example, on the confidence associated with the computed probability of the aircraft approaching icing conditions. The use of the confidence determination to decide on the level or action or alert to initiate or generate provides for a more accurate determination of icing conditions with a lower false alarm rate. For example, at a lower confidence, the system <NUM> can be configured to generate a weak alert <NUM> to a human pilot or remote operator, e.g., with a first color indicator light or display message (e.g., yellow) delivered to a user interface and/or with a lower-amplitude or lower-frequency audible alarm delivered by loudspeaker or headset, whereas at a higher confidence, the system <NUM> can be configured to generate a stronger alert <NUM> with a second color indicator light or display message (e.g., red) or a higher-amplitude or higher-frequency audible alarm. In other examples, at a lower confidence, the system <NUM> can be configured to suggest or execute a less drastic avoidance maneuver (e.g., one with a lesser course deviation) that still attempts to avoid icing conditions, whereas at a higher confidence the system <NUM> can be configured to suggest or execute a more drastic avoidance maneuver. An example of a less-drastic maneuver might be a pitch-up (climb) or pitch-down (dive) or a bank of less than <NUM> degrees, whereas an example of a more-drastic maneuver might be a <NUM>-degree turn-around or a return to base. In some examples, system <NUM> can also be designed with persistence logic, also referred to as de-bounce logic, (not shown) to increase confidence of a valid determination that the aircraft is approaching icing conditions.

<FIG> illustrates example functioning <NUM> of an icing prediction system including icing prediction decision engine <NUM> such as engine <NUM> of <FIG> and its associated supervised learning and unsupervised learning logic <NUM>, <NUM>. Predictive ice detection sensor suite <NUM>, which can correspond to suite <NUM> of <FIG>, can continuously measure environment parameters <NUM> and supply them to a supervised learning engine <NUM> configured to predict an icing condition. The sensor suite <NUM> can include, for example, outside air temperature and dew point sensors, and the environment parameters <NUM> can include temperature and dew point data. By "continuously," it is meant that the data is sampled with sufficient frequency to ensure useful icing-predictive functioning of the system. Depending on the circumstances, measurements can be repeatedly made by sensor suite <NUM> with a frequency on the order of milliseconds, seconds, tens of seconds, or minutes, either periodically or at randomized intervals.

The supervised learning engine <NUM>, which can correspond to supervised learning <NUM> in <FIG>, processes acquired environment parameters <NUM>, e.g., air mass temperature and dew point data, using, for example, linear regression (as illustrated), multi-layer regression (not shown), or a regression model that applies a set of weights to functions of extracted categorical or continuous features, such as linear functions, to provide a continuous result. Supervised learning <NUM> computes a metric indicating the probability that the aircraft will be a risk of icing (e.g., expressed as a percentage or log odds). Supervised learning <NUM> is "supervised" in that the environmental data is labeled, e.g., it is known to the system that measured dew point data represents environmental dew point and temperature data represents ambient air temperature. Algorithms for supervised learning include logistic regression, naive Bayes, support vector machines, artificial neural networks, and random forests. In some examples, the axes of the regression plot can be temperature and dew point. In other examples, different measured environmental parameters can be used as alternative or additional axes of the regression plot. For example, a regression plot may also include altitude and/or time as other axes. In any case, the supervised learning engine uses regression analysis to develop a trend based on measured data collected from the current flight, and uses the trend to generate a probability metric indicative of how likely it is that the aircraft will be at risk of icing. In some examples of icing prediction systems, this probability can be thresholded and used to generate an alarm or initiate an avoidance maneuver planning routine. Such examples may omit any reinforcement learning engine.

Results of predicted icing condition computations <NUM> are then provided to a reinforcement learning (RL) engine (decision engine) <NUM>. RL engine <NUM> is configured to account for user-defined tuning of key decision parameters (corresponding, e.g., to parameters <NUM> of <FIG>), information about the outcome of one or more previous maneuvers (indicative, e.g., of whether a maneuver successfully avoided icing, or whether the absence of a course-deviating maneuver successfully avoided icing), and the currently predicted icing condition <NUM> to determine <NUM> whether a planned avoidance maneuver or a decision not to make an avoidance maneuver is likely to have a successful outcome or not. Maneuvers with successful outcomes may be written back to database <NUM>, which can correspond to database <NUM> of <FIG>, providing learned information that can be used for future machine-learning data-processing and decision-making by decision engine <NUM>. Reinforcement learning engine <NUM> may provide, as outputs <NUM> (corresponding, e.g., to outputs <NUM> of <FIG>), potential icing conditions computations and/or their results, a recommended icing avoidance maneuver, a predictive time and/or distance before entering icing, and a percent confidence of entering icing. In some instances, it may become evident, through continuous iterative functioning of the icing prediction system, that a planned and executed icing avoidance maneuver is insufficient to avoid icing. A new or more drastic maneuver may then be planned, suggested, and/or executed, but also, the lack of success of the initially planned maneuver will be indicated in database <NUM> (or <NUM> of <FIG>) and will thus be available to inform future avoidance maneuver decisions.

<FIG> illustrates an example icing prediction process. An onboard computer (e.g., computer system <NUM> of <FIG>) of an aircraft interrogates an onboard ice detection sensor suite (e.g., sensors <NUM> of <FIG>, sensor suite <NUM> of <FIG>, or sensor suite <NUM> of <FIG>) to derive environmental data such as outside air temperature and dew point (corresponding to environment parameters <NUM> of <FIG>). The onboard computer system then performs computations <NUM> to predict future icing conditions. Such computations can be performed using, for example, supervised learning, unsupervised learning, or a combination of both. Computed predicted future icing conditions, among other data, can be provided <NUM> to a ground station (e.g., ground station <NUM> in <FIG>, e.g., via wireless link <NUM>) and/or to an onboard autonomous control system.

Based on the prediction not comporting with an acceptable level of safety <NUM>, as may be defined by user-defined parameters (e.g., parameters <NUM> of <FIG>), an action may be taken <NUM>, which action may include delivering an alert to a human operator (if any) about an impending danger of icing, and/or may include issuing of an icing-evasion maneuver command from either a ground station or an onboard autonomous system. The prediction and suggested maneuver can be added <NUM> to a library of learning, which can be stored, for example, as database <NUM> in <FIG> or <NUM> in <FIG>. Based on the prediction comporting with an acceptable level of safety <NUM>, it is thus determined that no action need be taken to avoid icing, and the prediction may be added to the library of learning <NUM>, which effectively provides a machine learning database that can be used to improve icing prediction determinations.

As indicated by feedback arrow <NUM>, the onboard computer may also interrogate the library of learning <NUM>. Ambient conditions may also be stored in the library of learning <NUM>. As the icing prediction system acquires learned experience, the data from the library of learning <NUM> can be used to assist in making a determination as to whether it is safe to continue along a current flight path or not. For example, an aircraft may take off and as it climbs, the aircraft may measure decreasing outside air temperature and dew point. This may be because the aircraft is entering a marine layer or entering a cloud deck. However, based on historical data from the library of learning <NUM>, the aircraft's onboard computer may determine that, based on the similarity of the present conditions to those encountered in the past, the estimated amount of time spent in the marine layer or cloud deck will result in a drop in temperature and an increase in moisture content (as determined from dew point data) not likely to cause icing within the amount of time spent in the marine layer or cloud deck. In such a case, the computer may decide, with an acceptable degree of confidence, that the marine layer or cloud deck may be safely traversed without risk of encountering icing conditions. Accordingly, in such case, there would be no need to generate an alarm or propose an avoidance maneuver ("no-issue").

The systems and methods described herein thus combine collected environmental data with trend plots to calculate the probability that the air vehicle is approaching icing conditions such that preemptive action can be taken in advance. The systems and methods may either issue an autonomous maneuver action (for autonomous uncrewed aircraft), or provide alerts to a ground station operator for remotely piloted unmanned aircraft. The systems and methods can also be used in crewed aircraft by providing both impending icing risk alerts and navigation prompting to a pilot as recommendations to avoid icing conditions. Configurable action points can be used to control the amount of risk that the operator is willing to take and the degree of confidence required to complete the mission. The systems and methods can make use of machine learning to increase the confidence level of predictive icing conditions, based on supervised and unsupervised learning from many epochs considering similar atmospheric conditions, weather patterns, and calendar time.

The machine-learning-based icing prediction systems and methods described herein can detect icing conditions at standoff distances greater than previously possible. For example, icing conditions can be detected using these systems and methods at a minimum of a half mile. The machine-learning-based icing prediction systems and methods described herein can detect icing conditions with aircraft power requirements much lower than may be required by, for example, systems using weather radar to detect icing conditions. For example, these systems and methods can operate by drawing power from a <NUM> volt DC power system and can operate with less than <NUM> kilowatt of power. The machine-learning-based icing prediction systems and methods described herein can be implemented with weight requirements lower than previously achievable. For example, these systems and methods can be implemented with a weight penalty of less than <NUM> pounds imposed upon an aircraft. The machine-learning-based icing prediction systems and methods described herein can also be implemented at lower costs than previously achievable.

The machine-learning-based icing prediction systems and methods described herein can predict ice conditions at a standoff distance much earlier than previously possible, and can use machine learning to increase the confidence of the predictions, with reduced weight and power draw requirements as compared to systems or methods requiring an onboard weather radar. Accordingly, the systems and methods described herein are especially useful for icing prediction and avoidance for smaller, uncrewed aircraft, including smaller helicopters, quadcopters, autonomous aerial drones, and remotely navigated aircraft, which may not be equipped with onboard weather radar or icing remediation systems and for which detection of and alert to icing after the aircraft has already entered potential icing conditions can be too late to avoid loss of aircraft. The systems and methods can provide reduced operating environment stress on an onboard or remote human pilot or an autonomous navigation system, and can be implemented at low cost.

Claim 1:
A system for icing prediction of an aircraft comprising:
an environmental sensor suite (<NUM>) configured to be installed on an aircraft and configured to measure at least two different kinds of environmental parameters corresponding to conditions external to the aircraft as the aircraft travels along a flight path,
data acquisition circuitry communicatively coupled to the environmental sensor suite (<NUM>) and configured to acquire and deliver real-time environmental data from the environmental sensor suite (<NUM>);
a computer system (<NUM>) configured to be installed onboard the aircraft, communicatively coupled to the data acquisition circuitry to receive real-time the environmental data, the computer system (<NUM>) comprising:
a supervised learning engine (<NUM>) configured to process the real-time environmental data to compute a probability that the aircraft will experience icing along the flight path,
a reinforcement learning engine (<NUM>) configured to generate an icing risk alert and/or a recommended icing avoidance maneuver based on user-defined parameters and the computed probability of icing, and
a database (<NUM>) storing a library of learning comprising information about the success or failure of previous icing avoidance maneuvers, and wherein the reinforcement learning engine (<NUM>) is configured to issue no icing risk alert and recommended no icing avoidance maneuver based on a determination of a tolerable risk of icing made based on the information in the library of learning.