Transverse runway deviation prediction method and device

Abstract

The invention relates to a transverse runway offset prediction model, and a method and a device thereof. The transverse runway deviation prediction method comprises the following steps: determining the coordinates of an observation point corresponding to an aircraft in a rectangular plane coordinate system so as to carry out acceleration analysis; mapping the coordinate of the observation point in the rectangular plane coordinate system to a complex plane coordinate system to carry out rotation analysis of a yaw angle; performing a non-linear energy estimation based at least on the acceleration and the yaw angle; and determining whether an alert needs to be issued based at least on the non-linear energy estimate.

Description

Technical Field

[0001] This application generally relates to the field of comprehensive surveillance of large aircraft, and particularly to lateral runway departure prediction and warning technologies. Background Art

[0002] The technology of preventing runway overruns is a key area of concern in aviation safety but is very complex. It aims to improve the flight crew's ability to perceive runway environment information and flight situation, thereby reducing the safety risk of runway overruns and ensuring the safe operation of aircraft.

[0003] On the one hand, currently, for aircraft in runway departure scenarios, it is mainly applied during aircraft landing. The specific technical means mainly include image recognition. By analyzing video images, the flight path of the target aircraft is monitored, and any deviation of the target aircraft relative to the runway centerline is determined.

[0004] On the other hand, lateral deviation prediction is mostly applied in the automotive field, and in this field, it is also mostly based on a front-view camera for lane line recognition. Visual algorithms are used to extract the edge features of lane lines, or based on lidar algorithms to filter the reflected echoes, fit the lane lines, and determine the position of the vehicle itself.

[0005] Existing runway overrun prevention systems do not include the prediction and warning of the lateral deviation of aircraft from the runway in their application scenarios, and there is a lack of relevant research. On the other hand, during the ground roll takeoff and / or landing phases of aircraft, accidents of deviating from the runway have not been eliminated. For example, when the flight crew is disturbed and loses effective control of the aircraft state, it may lead to deviating from the runway during the ground roll at the airport. Such accidents occur occasionally.

[0006] Therefore, there is a need in this field to provide a lateral runway departure prediction model to assist flight crew in effectively preventing runway departure accidents. Summary of the Invention

[0007] One aspect of the present disclosure relates to a method for predicting lateral runway departure, including determining the coordinates of an observation point corresponding to an aircraft in a rectangular coordinate system in a plane for acceleration analysis; mapping the coordinates of the observation point in the rectangular coordinate system in the plane to a complex plane coordinate system for yaw angle rotation analysis; performing energy estimation based on at least the acceleration and the yaw angle; and determining whether a warning needs to be issued based on at least the energy estimation.

[0008] According to some exemplary embodiments, performing energy estimation based on at least the acceleration and the yaw angle includes performing non-linear energy estimation based on at least time, the acceleration, the yaw angle, and the lateral offset distance.

[0009] According to some exemplary embodiments, the non-linear energy estimation includes non-dimensionalizing one or more of the time, the acceleration, the yaw angle, and the lateral offset distance.

[0010] According to some exemplary embodiments, determining the coordinates of the observation point corresponding to the aircraft in a rectangular coordinate system for acceleration analysis includes analyzing the acceleration of the aircraft based on at least one or more of the total thrust of the aircraft, the side wind force, the support forces of the front and rear wheels of the aircraft, the adhesion coefficients between the front and rear wheels of the aircraft and the runway, the air resistance coefficient of the aircraft, the lateral friction force of the aircraft wheels, the speed of the aircraft, and the mass of the aircraft.

[0011] According to some exemplary embodiments, analyzing the acceleration of the aircraft includes determining the longitudinal acceleration of the aircraft based on at least one or more of the total thrust of the aircraft, the support forces of the front and rear wheels of the aircraft, the adhesion coefficients between the front and rear wheels of the aircraft and the runway, the longitudinal air resistance coefficient of the aircraft, the longitudinal speed of the aircraft, and the mass of the aircraft; and determining the lateral acceleration of the aircraft based on at least one or more of the side wind force, the lateral air resistance coefficient of the aircraft, the lateral friction force of the aircraft wheels, the lateral speed of the aircraft, the lateral friction force of the aircraft wheels, and the mass of the aircraft.

[0012] According to some exemplary embodiments, the method further includes determining the longitudinal speed and the lateral speed of the aircraft, wherein determining the longitudinal speed of the aircraft includes directly obtaining the longitudinal speed of the aircraft from an airport database; and determining the lateral speed of the aircraft includes presetting speed estimators on both sides of the runway center line, where the speed estimators approach the true lateral speed as the aircraft taxis to obtain an estimated value of the lateral speed of the aircraft.

[0013] According to some exemplary embodiments, performing a rotation analysis of the yaw angle includes: performing a rotational accumulation of the yaw value of the observation point in the complex plane coordinate system.

[0014] According to some exemplary embodiments, where the aircraft is in takeoff taxi, and where determining whether to issue an alarm is at least based on the energy estimation includes issuing an alarm to prompt canceling takeoff if it is predicted that the aircraft cannot reach the minimum energy in a specified area.

[0015] According to some exemplary embodiments, issuing an alarm includes predicting the takeoff area of the aircraft based on the energy estimation; superimposing a display of the takeoff area on the runway display, where the longitudinal and lateral dimensions of the takeoff area are dynamically scaled or enlarged based on the energy estimation.

[0016] Another aspect of the present disclosure relates to a device for predicting lateral runway deviation, including a memory; and a processor coupled to the memory and configured to determine the coordinates of an observation point corresponding to an aircraft in a plane rectangular coordinate system for acceleration analysis; map the coordinates of the observation point in the plane rectangular coordinate system to a complex plane coordinate system for yaw angle rotation analysis; perform energy estimation based at least on the acceleration and the yaw angle; and determine whether an alarm needs to be issued based at least on the energy estimation.

[0017] Other aspects of the present disclosure further include corresponding devices, equipment, and computer-readable media, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram showing the force condition of an aircraft in a non-equilibrium state according to an exemplary aspect of the present disclosure is shown.

[0019] Figure 2 A schematic diagram showing the analysis in a plane rectangular coordinate system according to an exemplary aspect of the present disclosure is shown.

[0020] Figure 3 A schematic diagram showing the rotation analysis in a complex plane coordinate system according to an exemplary aspect of the present disclosure is shown.

[0021] Figure 4 A flowchart showing a method for lateral runway deviation warning according to an exemplary aspect of the present disclosure is shown.

[0022] Figure 5 A flowchart showing a method for predicting lateral runway deviation according to an exemplary aspect of the present disclosure is shown.

[0023] Figure 6 A block diagram showing a device for a lateral runway deviation prediction model based on non-equilibrium motion according to an exemplary aspect of the present disclosure is shown.

[0024] Figure 7 A schematic diagram showing a display scheme for lateral runway deviation warning according to an exemplary aspect of the present disclosure is shown. DETAILED DESCRIPTION

[0025] The prior art generally does not consider the influence of runway width and aircraft taxiing attitude, and ignores the possibility of the aircraft deviating laterally out of the runway due to attitude deflection during taxiing. Moreover, image processing is highly dependent on visual sensors, the determination means is relatively single, and the reliability decreases in bad weather. To improve the safety of aircraft takeoff and / or landing, a lateral runway deviation prediction model with comprehensive analysis is required.

[0026] Figure 1A schematic diagram of the force condition 100 of an aircraft in a non-equilibrium state according to an exemplary aspect of the present disclosure is shown. This model aims at the lateral deviation situation of the aircraft during the taxiing phase. For example, Figure 1 As shown, in the plane rectangular coordinate system, the longitudinal direction of the taxiway is taken as the Y-axis, the direction perpendicular to the runway is taken as the X-axis, and the direction perpendicular to the paper surface is taken as the Z-axis. Various physical parameters are introduced, including the aircraft thrust T0, the friction coefficient f of the front wheels, the friction coefficient b of the rear wheels, the tail chord steering force F δ and so on.

[0027] On this basis, a complex plane coordinate system is introduced. The observation points in the plane rectangular coordinate system can be mapped into the complex plane. Figure 1 In the schematic diagram of the force condition 100 of the aircraft in a non-equilibrium state, the argument θ of the observation point and the yaw angle φ in the complex plane are also marked. There is a functional relationship θ + φ = π / 2, where the yaw angle φ is the angle formed by the observation point vector and the Y-axis, and the argument θ is the angle formed by the observation point vector and the X-axis. By using the physical meaning of the rotation characteristics of the complex plane, after obtaining the X value and Y value of the observation point coordinates in the plane index coordinate system in the previous step, the argument θ, the yaw angle φ, and the angle change amount α between each time period can be effectively and quickly calculated.

[0028] Figure 2 A schematic diagram of the analysis 200 of the plane rectangular coordinate system according to an exemplary aspect of the present disclosure is shown. For example, Figure 2 As shown on the left side of, the complex plane coordinate system (C) and the plane rectangular coordinate system (R) are superimposed, and the three-axis directions are the same. The points in the plane rectangular coordinate system (R) show the moving points of the aircraft. According to the exemplary embodiment, the moving points of the aircraft can be used as the observation points. Figure 2 The right side of shows the coordinates of the moving points of the aircraft on the XY plane in the plane rectangular coordinate system (R). In Figure 2 the example of, two points A and B on the moving trajectory of the aircraft are shown, and their XY plane coordinates are A(x1, y1) and B(x2, y2) respectively.

[0029] Figure 3 A schematic diagram of the rotation analysis 300 of the complex plane coordinate system according to an exemplary aspect of the present disclosure is shown. For example, Figure 3 As shown on the left side of, the complex plane coordinate system (C) and the plane rectangular coordinate system (R) are superimposed, and the three-axis directions are the same. The points in the complex plane coordinate system (C) show the corresponding points A and B on the XY plane in the complex plane coordinate system (C) mapped from the moving points A and B of the aircraft in the plane rectangular coordinate system (R) respectively, which are expressed as A(x1 + y1i) and B(x2 + y2i) respectively. Figure 3 Also shown in are the respective yaw angles φ1 and φ2 of point A and point B, and the change of the argument θ from point A to point B.

[0030] Some aspects of the present disclosure provide a lateral runway deviation prediction model based on unbalanced motion. Specifically, according to some exemplary embodiments, the present disclosure provides a prediction model under three elements and two planes, where the three elements include lateral acceleration, longitudinal acceleration, and yaw angle, and the two planes include a rectangular coordinate system and a complex plane coordinate system.

[0031] According to an exemplary aspect, in the unbalanced state of the aircraft, by combining the dynamic model with the state estimation, during the process of calculating the three elements of each observation point, for the Y value of the coordinate, it can be directly obtained and input from the airport database; for the X value of the coordinate, which reflects the deviation of the aircraft on the runway, it generally cannot be directly obtained from the airport database. According to the present disclosure, two velocity estimators can be preset on each side of the runway center line, defined as v1 to v4. The velocity estimator includes a dynamic value. As the aircraft taxis, the velocity line of the velocity estimator approaches the true lateral velocity, and then in combination with the time t, the lateral distance X value deviating from the center line is determined, as Figure 2 shown.

[0032] According to some exemplary embodiments, the velocity estimator may include using, for example, Figure 2 the movement of the midpoint line for estimation, and the point line represents velocity. On the runway, the aircraft can be divided into left and right ends (left and right wings), and two point lines are virtually set at each end, for a total of 4. Figure 2 Taking the right side as an example, so 2 are set. For example, the two point lines on the right side are set as v x1 = 10 and v x2 = 20. Under the ideal assumption that the aircraft taxis for takeoff / landing on the runway without any lateral deviation, that is, v x = 0 and the lateral velocity is 0. All the preset yellow lines will quickly approach the central axis, then the distance between the yellow line and the central axis is also close to 0, and X = 0 can be obtained. If there is a lateral deviation, there must be a velocity component in the X direction. For example, if it is detected / observed that v x = 18, then the two yellow lines will approach this value. At this time, the 2 yellow lines are approximately 1 line, and the distance value of X can be approximately obtained. In other words, this R plane is the distance coordinate (X value and Y value) for the aircraft itself, and for the preset point line, the X value of this R plane represents velocity, such as the center point being 0.

[0033] According to an exemplary aspect, for the yaw angle φ, it is mapped from the observation point in the rectangular coordinate system (R) to the complex plane. Using the physical meaning of the rotation characteristics of the complex plane, after obtaining the X value and Y value based on the previous step, the amplitude angle θ and the yaw angle φ can be effectively and quickly calculated, as Figure 3 shown. The angle change amount of the yaw angle between each time period is denoted as α.

[0034] The above three elements can be processed by mathematical tools to obtain a non-linear state estimation model. According to an exemplary embodiment, the angle change amount α n can be the cumulative amount of the angle change amount α at each previous moment.

[0035] According to some exemplary embodiments, the increment of the yaw angle can be determined based on the difference between α m and α i at any two moments. According to some exemplary embodiments, the longitudinal acceleration and / or the lateral acceleration can be determined by using Newton's second law and combining the dynamic equations obtained from various mechanical parameters.

[0036] The non-linear state estimation model according to a further exemplary embodiment can be as follows:

[0037] State estimation equation

[0038] where α: rotation amount;

[0039] Yaw angle increment (during taxiing);

[0040] T0: total thrust of the aircraft;

[0041] μ f : coefficient of combination between the front wheel and the runway (friction coefficient);

[0042] μ b : coefficient of combination between the rear wheel and the runway (friction coefficient);

[0043] N f : front wheel support force;

[0044] N b : rear wheel support force;

[0045] Z: crosswind (can also be understood as the sum of all external forces causing the aircraft to deviate from the runway);

[0046] ρ δ : aircraft lateral air resistance coefficient;

[0047] ρ L : aircraft longitudinal air resistance coefficient;

[0048] F LGx : aircraft wheel hub lateral friction force;

[0049] m: aircraft weight;

[0050] Y-axis acceleration;

[0051] X-axis acceleration

[0052] In the above formula is the accumulation of the rotation amount α. For example, α i is the yaw angle change amount from time t + 1 to time t, which is α i the cumulative sum of.

[0053] According to an exemplary aspect of the present disclosure, the non-linear energy estimation can be obtained by non-dimensionalizing one or more of the time t, X-axis acceleration Y-axis acceleration lateral offset distance x, and yaw angle φ and then performing weighted summation.

[0054] For example, according to some exemplary embodiments, the non-linear energy estimation can be obtained through matrix operations.

[0055]

[0056] where K1 - K4 may include values without specific meanings, which are used for non-dimensionalizing and weighting the corresponding items in the previous matrix. Through the above matrix operations, the non-linear energy estimation E can be obtained.

[0057] The deviation process (e.g., deviating from the runway centerline) will cause energy loss (e.g., takeoff energy). Based on this, the present disclosure sets a threshold for this energy (e.g., takeoff energy) to determine whether an alarm needs to be issued. For example, according to an exemplary embodiment, through a designed display page, the deviation situation can be visually presented to the crew and corrective actions can be proposed. This will be further described below.

[0058] The present disclosure calculates the lateral and longitudinal speed changes and yaw angle of each observation point by introducing the complex plane and based on the aircraft state parameters and runway parameters, and constructs the attitude and takeoff energy equation to display the deviation situation of the aircraft during the taxiing phase in real time for the crew.

[0059] Figure 4 FIG. shows a flowchart of a lateral runway deviation warning method 400 according to an exemplary aspect of the present disclosure. Figure 4 The lateral runway deviation warning method 400 starts at block 402.

[0060] At block 404, input parameters. According to exemplary embodiments, the input parameters may include runway parameters, etc. For example, the input parameters may include at least one or more of the parameters described above in combination with Figures 1 to 3 the parameters that can be directly obtained from the airport database, etc.

[0061] At block 406, a lateral offset prediction is performed. According to some exemplary embodiments, the lateral offset prediction may be performed, for example, based on at least a portion of the techniques described above in connection with Figure 1 – Figure 3 the techniques described.

[0062] At block 408, it is determined whether to issue an alert display based on the lateral offset prediction. According to an exemplary embodiment, determining whether to issue an alert display based on the lateral offset prediction may include, for example, being performed based on the non-linear energy estimation described above. If it is determined to issue an alert display (“Yes”), process 400 proceeds to block 410. Otherwise, if it is determined not to issue an alert (“No”), process 400 proceeds to block 412.

[0063] At block 410, an alert is issued. According to an exemplary embodiment, the alert may include, but is not limited to, a display alert, a sound alert, a flashing alert, a beeping alert, etc. or any combination thereof.

[0064] At block 412, it is determined whether the aircraft is taxiing. If the aircraft is taxiing (“Yes”), process 400 proceeds to block 404 to update the parameter input. If the aircraft is not taxiing (“No”), process 400 ends (block 414).

[0065] Figure 5 A flowchart of a lateral runway departure prediction method 500 according to an exemplary aspect of the present disclosure is shown. Figure 5 The lateral runway departure prediction method 500 begins with initialization at block 502.

[0066] At block 504, the prediction and control steps are set. Real-time monitoring of the state of the aircraft during runway taxiing requires the computing power support of the processor. According to an exemplary embodiment, the prediction and control steps may be determined based on the computing power of the processor. For example, if the computing power processing speed is weak, it may be set to predict and calculate the energy once every 1 second during takeoff (about 30 seconds); while if the computing power processing speed is fast, it may be set to predict and calculate the energy once every 200 ms. The above values are only examples, and the present disclosure is not limited in this regard.

[0067] At block 506, a lateral runway offset prediction model is imported. The lateral runway offset prediction model may be constructed in various ways based on the techniques described above with reference to Figures 1 - 3 the techniques described. For example, the lateral runway offset prediction model may be implemented programmatically as an algorithm, a neural network, etc.

[0068] At blocks 508(1), 508(2), 508(3), the X-axis speed increment (lateral acceleration ), the Y-axis speed increment (longitudinal acceleration, ), and the Z-axis rotation increment

[0069] At block 510, state estimation is performed. According to an exemplary embodiment, the state estimation may include, for example, performing state estimation according to the non-linear state estimation model described above.

[0070] At block 512, energy calculation is performed. According to an exemplary embodiment, the energy calculation may include performing non-linear energy estimation as described above.

[0071] At block 514, it is determined whether the energy loss exceeds a threshold. If it is determined that the energy loss does not exceed the threshold ("No"), then process 500 returns to block 510 to continue state estimation according to the prediction and control steps set at block 504. If it is determined that the energy loss exceeds the threshold ("Yes"), indicating that energy may be lost due to deviation and it is possible that the aircraft cannot reach the minimum takeoff energy value in the takeoff area, then process 500 ends (block 516) (for example, it may go to Figure 4 block 408 of to determine whether to issue an alarm).

[0072] According to some embodiments, Figure 5 the lateral runway departure prediction method 500 may be used for, for example, Figure 4 the lateral runway departure warning method 400. For example, according to at least some exemplary embodiments, Figure 5 the lateral runway departure prediction method 500 may be used for or include Figure 4 the lateral offset prediction of block 406 of, or be included as Figure 4 at least a part of the lateral offset prediction of block 406 of.

[0073] In summary, the technical flowchart of the solution of the present disclosure is as shown in Figure 4 and / or Figure 5 shown. Before takeoff / landing taxiing, all parameters are input in place and the control parameters of the model are configured. Through the above disclosure, the model updates in real time and predicts the trajectory within the working time domain, so as to provide warnings (for example, visual suggestions) for the crew, achieving the purpose of smooth takeoff / landing of the aircraft.

[0074] Due to the adoption of the above technical solution, the present disclosure can provide a lateral anti-aircraft runway departure warning function, enabling the crew to timely master the taxiing state of the aircraft on the runway and reducing the probability of aircraft lateral runway departure accidents. Compared with the lane departure of automobiles, the present disclosure abandons the radar detection method and does not use visual algorithms. In view of the characteristics of airport runways, a pure data stream method is adopted to have higher safety.

[0075] Figure 6 FIG. shows a block diagram of a lateral runway departure prediction model device 600 based on non-equilibrium motion according to an exemplary aspect of the present disclosure. As shown, Figure 6The lateral runway deviation prediction model device 600 may include a parameter input module 602, a runway deviation prediction module 604, and an alarm module 606. As is known to those of ordinary skill in the art, although the lateral runway deviation prediction model device 600 is described as including modules 602 - 606, the present disclosure is not limited thereto, but may include more or fewer modules. For example, according to some exemplary embodiments, the functions of several modules may be combined into fewer modules, or the function of a single module may be split into multiple modules, and these and other combinations fall within the scope of the present disclosure. Figure 6 The lateral runway deviation prediction model device 600 is described as including modules 602 - 606, but the present disclosure is not limited thereto and may include more or fewer modules. For example, according to some exemplary embodiments, the functions of several modules may be combined into fewer modules, or the function of a single module may be split into multiple modules, and these and other combinations fall within the scope of the present disclosure.

[0076] According to an exemplary embodiment, the parameter input module 602 may input parameters. For example, according to some exemplary embodiments, the parameter input module 602 may perform the parameter input described above in connection with Figure 4 block 404. According to an exemplary embodiment, the input parameters may include runway parameters, etc. For example, the input parameters may include at least one or more of the parameters that can be directly obtained from an airport database, etc., among the parameters described above in connection with Figures 1 to 3 .

[0077] According to an exemplary embodiment, the runway deviation prediction module 604 may perform runway deviation prediction. The reported deviation prediction may include longitudinal deviation prediction and / or lateral deviation prediction, etc., or any combination thereof. For example, according to some exemplary embodiments, the runway deviation prediction module 604 may perform the lateral offset prediction described above in connection with Figure 4 block 406. According to some exemplary embodiments, the runway deviation prediction module 604 may perform the lateral runway deviation prediction method 500 described above in connection with Figure 5 .

[0078] According to an exemplary embodiment, the alarm module 606 may give an alarm. For example, according to some exemplary embodiments, the alarm module 606 may perform the alarm determination and alarm described above in connection with Figure 4 block 408 and / or block 410. According to an exemplary embodiment, the alarm determination may be based on the result of the runway deviation prediction performed by the runway deviation prediction module 604. According to an exemplary embodiment, the alarm may include, but is not limited to, a display alarm, a sound alarm, etc., or any combination thereof.

[0079] According to an exemplary aspect, a method for performing lateral runway deviation prediction based on a lateral runway deviation prediction model under non - equilibrium motion according to the present disclosure includes the following steps:

[0080] Step S1: Preset the prediction, control steps, and the bias estimator of the lateral speed of the model.

[0081] Step S2: Before the aircraft enters the taxi, it obtains runway friction coefficient (e.g., μ f , μ b , etc.), runway width, wind speed and other parameter information through a high-precision airport runway database, and inputs the status values such as the weight of the aircraft itself (e.g., m), configuration, thrust (e.g., T0), etc. The above various data can be obtained, for example, through corresponding sensors or systems. For example, the wind speed can be provided by the atmosphere system, the friction parameters of the runway can be provided by the runway database, and the thrust can be calculated based on the fuel storage minus the resistance such as the aircraft's own weight, and so on. Subsequently, the method enters Step S3.

[0082] Step S3: The model constructs a plane coordinate system (R) and a complex plane coordinate system (C), and comprehensively judges whether the aircraft deviates from the runway center line by combining the lateral acceleration (e.g., ), longitudinal acceleration (e.g., ) and the three elements of the yaw angle (lateral and longitudinal speed changes and yaw angle).

[0083] Step S4: If it is judged that the aircraft does not deviate from the runway center line, enter Step S5; if it is judged that the aircraft has a deviation, enter Step S6.

[0084] Step S5: The aircraft taxis and takes off / lands normally.

[0085] Step S6: Based on the plane coordinate system, determine the coordinates of points A and B corresponding to time t and t + 1. Among them, the Y values of the vertical coordinates of points A and B are directly obtained through the runway database; the X values of the horizontal coordinates of points A and B are estimated according to the four speed offset estimators preset in Step S1. The change of speed in the lateral direction is approximated by the estimated lines in the offsetters, and the estimated value of the lateral speed is intuitively obtained. According to the speed-time formula, the X value is determined for energy estimation.

[0086] Step S7: Map the observation points in the plane coordinate system to the complex plane, and the coordinate values are equivalently converted into the form of a + bi; let the intersection point of the aircraft's starting line and the right side of the runway be the reference point C, the yaw value of the aircraft's movement trajectory relative to the runway center line be φ, and the angle formed by each observation point relative to the reference point in the complex plane be θ, and there is a functional relationship θ + φ = π / 2.

[0087] According to the rotation amount of each observation point relative to the reference point, Combined with the rotation formula Finally, obtain the yaw value φ.

[0088] Similarly, the rotation amount difference between each observation point can be determined as To obtain the attitude update equation

[0089] Step S8: Combine and superimpose the two plane-related parameters, combine the data obtained in step S2, the velocity change obtained by the dynamic equation, and the angle change obtained by observation, combine the nonlinear state estimation model, and enter step S9. The specific model is as follows:

[0090] State Estimation Equation

[0091] Step S9: The state estimation model changes with time t, and the three elements of the real-time updated yaw angle, X-axis lateral velocity change rate, and Y-axis longitudinal velocity change rate are dimensionlessly processed, and the derived lateral deviation distance is obtained to obtain the energy estimation. A threshold is set during the judgment process as a judgment standard, and an alarm is issued when necessary, and then enters step S10.

[0092] Step S10: During the safe takeoff process, the overall performance of the aircraft must reach the threshold. During the lateral deviation process, parameters such as Vx, yaw angle, and boundary distance consume total energy. If the minimum energy cannot be reached in the designated area, the takeoff is canceled. The display screen provides information and suggestions for the crew. In the first stage, the aircraft starts the taxiing normally, and the energy model predicts the takeoff area of ​​the aircraft. In the second stage, the aircraft deviates laterally and loses some takeoff energy. The shrinking of the takeoff area is used to intuitively remind the crew of the risk of deviation. After the crew corrects the deviation, the takeoff energy is restored in the third stage, and the displayed takeoff area is expanded. However, due to the loss of time T in the process, the takeoff termination line changes compared to the first stage. This model ends until all the wheels of the aircraft leave the ground. For the landing of the aircraft, a similar solution can be adopted and corresponding adaptive modifications can be made.

[0093] Figure 7 A schematic diagram of a lateral runway deviation warning display scheme 700 according to an exemplary aspect of the present disclosure is shown.

[0094] As in Figure 7 As shown in the lateral runway deviation warning display scheme 700, the largest vertical rectangle shows a schematic diagram of the runway, and the smaller rectangle near the upper end of the runway shows the takeoff area. Figure 7 As can be seen in (a)-(c) of FIG. 1 , the size, length and / or width of the take-off area is variable. The dashed box at least partially overlapping the take-off area indicates the take-off threshold. The horizontal line above the take-off area indicates the termination line. The triangle on the runway below the take-off area indicates the aircraft, i.e., the target.

[0095] In the first phase (a), the aircraft starts rolling normally and the energy model predicts the take-off area of ​​the aircraft.

[0096] In the second stage (b), the aircraft experiences a lateral deviation and loses some takeoff energy. By reducing the takeoff area, the flight crew is visually alerted to the risk of deviation. According to an exemplary embodiment, the reduction of the takeoff area may include a reduction in the longitudinal (Y-axis) direction and / or a reduction in the lateral (X-axis) direction or a combination of both. As can be seen from Figure 7 the (b) of Figure 7 , the takeoff area is significantly reduced compared to (a). This can alert the flight crew and give them an intuitive understanding of the magnitude of the deviation risk. According to some exemplary embodiments, while the takeoff area is being reduced, it may be accompanied by an audible alert, a flashing alert, a buzzer alert, etc. or any combination thereof.

[0097] After the flight crew corrects the deviation, in the third stage (c), the takeoff energy is restored and the displayed takeoff area expands. However, due to the loss of time T during the process, the takeoff termination line changes compared to the first stage. The model ends when all the wheels of the aircraft leave the ground.

[0098] According to some exemplary embodiments, the reduction and expansion sizes of the takeoff area are determined based on energy estimation or based on a combination of energy estimation and other factors. The other factors may include, for example, one or more of lateral speed, lateral acceleration, longitudinal speed, longitudinal acceleration, lateral offset distance, yaw angle, etc. According to an exemplary embodiment, the lateral (X-axis) reduction and expansion of the takeoff area may be based on various algorithms. For example, the degree of lateral (X-axis) reduction and expansion of the takeoff area may be calculated based on an inverse proportional function of energy (and possibly in combination with one or more of the above other factors) or other linear or non-linear functions. Another example is that the amount of lateral (X-axis) reduction and expansion of the takeoff area may be calculated based on a linear or non-linear function of energy (and possibly in combination with one or more of the above other factors).

[0099] As described above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0100] The various illustrative logical blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0101] The steps of the methods or algorithms described in connection with the present disclosure may be implemented directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may reside in any form of storage medium known in the art. Some examples of storage media that may be used include random access memory (RAM), read only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, and the like. The software modules may include a single instruction, or many instructions, and may be distributed over several different code segments, among different programs, and across multiple storage media. The storage medium may be coupled to the processor such that the processor can read from, and write to, the storage medium. Alternatively, the storage medium may be integral to the processor.

[0102] The methods disclosed herein include one or more steps or acts for achieving the described methods. These method steps and / or acts may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or acts is specified, the order and / or use of specific steps and / or acts may be varied without departing from the scope of the claims.

[0103] The processor can execute software stored on a machine-readable medium. The processor can be implemented with one or more general-purpose and / or special-purpose processors. Examples include microprocessors, microcontrollers, DSP processors, and other circuitry capable of executing software. Software should be broadly construed to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. As an example, the machine-readable medium can include RAM (Random Access Memory), flash memory, ROM (Read-Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium, or any combination thereof. The machine-readable medium can be embodied in a computer program product. The computer program product can include packaging material.

[0104] In a hardware implementation, the machine-readable medium can be a part separate from the processor in a processing system. However, as will be readily appreciated by those skilled in the art, the machine-readable medium or any part thereof can be external to the processing system. As an example, the machine-readable medium can include transmission lines, carrier waves modulated with data, and / or computer products separate from a wireless node, all of which can be accessed by the processor via a bus interface. Alternatively or additionally, the machine-readable medium or any part thereof can be integrated into the processor, such as may be the case with a cache and / or a general register file.

[0105] The processing system can be configured as a general-purpose processing system having one or more microprocessors providing processor functionality and an external memory providing at least a portion of the machine-readable medium, all linked together via an external bus architecture with other support circuitry. Alternatively, the processing system can be implemented with an ASIC (Application Specific Integrated Circuit) having a processor, a bus interface, a user interface (in the case of an access terminal), support circuitry, and at least a portion of the machine-readable medium integrated on a single chip, or with one or more FPGAs (Field Programmable Gate Arrays), PLDs (Programmable Logic Devices), controllers, state machines, gated logic, discrete hardware components, or any other suitable circuitry, or any combination of circuits capable of performing the various functionalities described throughout this disclosure. Depending on the particular application and the overall design constraints imposed on the system, those skilled in the art will recognize how best to implement the functionality described with respect to the processing system.

[0106] A machine-readable medium may include several software modules. These software modules include instructions that cause a processing system to perform various functions when executed by a device such as a processor. These software modules may include a transmission module and a reception module. Each software module may reside in a single storage device or be distributed across multiple storage devices. As an example, when a triggering event occurs, the software modules may be loaded from a hard drive into RAM. During the execution of the software modules, the processor may load some instructions into the cache to improve access speed. One or more cache lines may then be loaded into the general register file for the processor to execute. When referring to the functionality of the software modules below, it will be understood that such functionality is implemented by the processor when the processor executes the instructions from the software module.

[0107] If implemented in software, the functions may be stored on or transmitted via a computer-readable medium as one or more instructions or code. The computer-readable medium includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. The storage media may be any available media that can be accessed by a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technology such as infrared (IR), radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technology such as infrared, radio, and microwave is included in the definition of the medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and diskette, where disk often magnetically reproduces data, while disc uses lasers to optically reproduce data. Thus, in some aspects, the computer-readable medium may include non-transitory computer-readable media (e.g., tangible media). Additionally, for other aspects, the computer-readable medium may include transitory computer-readable media (e.g., signals). Combinations of the above should also be included within the scope of computer-readable media.

[0108] Accordingly, some aspects may include a computer program product for performing the operations given herein. For example, such a computer program product may include a computer-readable medium having instructions stored (and / or encoded) thereon, which instructions, when executed by one or more processors, perform the operations described herein. In some aspects, the computer program product may include packaging material.

[0109] It will be understood that the claims are not limited to the exact configurations and components illustrated above. Various changes, substitutions, and alterations can be made in the arrangement, operation, and details of the methods and apparatuses described above without departing from the scope of the claims.

Claims

1. A method for predicting lateral runway deviation, comprising: Determine the coordinates of the observation point corresponding to the aircraft in the plane rectangular coordinate system to analyze the acceleration; Mapping the coordinates of the observation point in the plane rectangular coordinate system to the complex plane coordinate system to perform rotation analysis of the yaw angle; performing energy estimation based at least on the acceleration and the yaw angle; as well as A determination is made based at least on the energy estimate whether an alert needs to be issued.

2. The method of claim 1, wherein: Performing energy estimation based at least on the acceleration and the yaw angle includes performing non-linear energy estimation based at least on time, the acceleration, the yaw angle, and a lateral offset distance.

3. The method of claim 2, wherein: The nonlinear energy estimation includes dimensionally non-dimensionalizing one or more of the time, the acceleration, the yaw angle, and the lateral offset distance.

4. The method of claim 1, wherein: Determining the coordinates of the observation point corresponding to the aircraft in a plane rectangular coordinate system for analyzing the acceleration includes: The acceleration of the aircraft is analyzed based on at least one or more of the total thrust of the aircraft, the crosswind force, the support force of the front and rear wheels of the aircraft, the combination coefficient of the front and rear wheels of the aircraft with the runway, the air resistance coefficient of the aircraft, the lateral friction of the wheel hub of the aircraft, the speed of the aircraft, and the mass of the aircraft.

5. The method of claim 4, wherein: The acceleration analysis of the aircraft includes: determining the longitudinal acceleration of the aircraft based on at least one or more of the total thrust of the aircraft, the front and rear wheel support force of the aircraft, the combination coefficient of the front and rear wheels of the aircraft with the runway, the longitudinal air resistance coefficient of the aircraft, the longitudinal speed of the aircraft, and the mass of the aircraft; and The lateral acceleration of the aircraft is determined based on at least one or more of the crosswind force, an aircraft lateral air drag coefficient, the aircraft hub lateral friction, an aircraft lateral speed, an aircraft hub lateral friction, and an aircraft mass.

6. The method of claim 5, further comprising: Determine the aircraft longitudinal velocity and the aircraft lateral velocity, wherein Determining the longitudinal speed of the aircraft includes directly obtaining the longitudinal speed of the aircraft from an airport database; and Determining the lateral speed of the aircraft includes presetting speed estimators on both sides of the runway centerline, wherein the speed estimators approach the true lateral speed as the aircraft rolls to obtain an estimated value of the lateral speed of the aircraft.

7. The method of claim 1, wherein: The rotation analysis of the yaw angle includes: performing rotation accumulation of the yaw values ​​of the observation points in the complex plane coordinate system.

8. The method of claim 1 , wherein the aircraft is in a takeoff roll, and wherein determining whether an alert needs to be issued based at least on the energy estimate comprises: If it is predicted that the aircraft cannot reach the minimum energy in the designated area, an alert is issued to cancel the takeoff.

9. The method of claim 8, wherein: The warnings include: predicting a takeoff region for the aircraft based on the energy estimate; A display of the takeoff area is superimposed on the runway display, wherein The longitudinal and lateral dimensions of the take-off area are dynamically shrunk or enlarged based on the energy estimate.

10. A device for predicting lateral runway deviation, comprising: Memory; as well as a processor coupled to the memory and configured to: Determine the coordinates of the observation point corresponding to the aircraft in the plane rectangular coordinate system to analyze the acceleration; Mapping the coordinates of the observation point in the plane rectangular coordinate system to the complex plane coordinate system to perform rotation analysis of the yaw angle; performing energy estimation based at least on the acceleration and the yaw angle; as well as A determination is made based at least on the energy estimate whether an alert needs to be issued.

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