A target orientation tracking filtering method based on multi-dimensional motion features

Abstract

The application discloses a target direction tracking filtering method based on multi-dimensional motion characteristics. The application combines the actual measurement angle of the target by the directional antenna, the alpha-beta filtering smooth angle, the flight speed of the target, the relative distance of the target, the relative speed vector, the native maneuvering condition and other parameters, and finally comprehensively evaluates the stable target direction. The application is a target direction tracking filtering method based on multi-dimensional motion characteristics. The method can effectively reduce the influence of the interference signal on the target measurement angle without changing the existing TCAS equipment hardware, ensures the accuracy and stability of the target tracking, improves the use satisfaction of the user, and reduces the post-maintenance cost of the equipment after installation.

Description

Technical Field

[0001] This invention relates to the field of aircraft collision avoidance technology, and more specifically to a target orientation tracking and filtering method based on multi-dimensional motion characteristics. Background Technology

[0002] Airborne Collision Avoidance System (ACAS, also known as Traffic Alert and Collision Avoidance System TCAS) is defined by the Federal Aviation Administration (FAA). Currently, the TCAS II system is commonly used in both military and civilian aviation, providing Traffic Alert (TA) and Decision Alert (RA). TCAS is an essential device for preventing dangerous approach and collision accidents between aircraft, and it can operate independently of ground traffic control systems. Its primary function is to provide airborne safety separation for aircraft. The system uses secondary radar to detect approaching aircraft in the vicinity and, when necessary, alerts the pilot to take evasive action and maintain an appropriate safe distance from other aircraft, thus achieving collision avoidance. Recent flight practice has proven that this system is the last line of defense against mid-air collisions and one of the most effective means currently available. It overcomes the limitations of ground-based air traffic control, providing flight safety assurance capabilities beyond those of ground traffic control, and plays a significant role in responding to sudden dangerous approach and preventing mid-air collisions.

[0003] The ACAS transceiver is crucial for achieving collision avoidance. It scans and interrogates the aircraft in four areas—front, rear, left, and right—by controlling the antenna beam direction. Nearby aircraft equipped with air traffic control transponders (Mode S / ATCRBS transponders) (hereinafter referred to as target aircraft) will respond. Based on the received response signals, the ACAS transceiver obtains information such as the target aircraft's altitude, relative distance, and bearing. It then calculates the rate of change of altitude and relative distance, and, combined with its own position and motion information, assesses the target aircraft's threat level (OT: Other Aircraft, PT: Approaching Aircraft, TA: Traffic Alert, RA: Decision Alert), and displays different target aircraft graphically.

[0004] In the TCASII collision avoidance system, due to the limitations of the aircraft's installation location and electromagnetic compatibility with other systems, the directional antenna of the airborne collision avoidance system cannot be designed to be too large. It consists of only four vertically polarized monopole arrays. The target's azimuth is calculated by simultaneously receiving and phase-shifting the four arrays. Due to the small number of antenna arrays and the large number of aircraft in the airspace near the airport, the spatial electromagnetic signals are complex. Synchronous, asynchronous, and reflected interference signals superimposed on the response signals can all cause deviations in the target azimuth calculation, resulting in drifting and jumping of the target azimuth angle on the display, which interferes with the pilot's control judgment. Summary of the Invention

[0005] To address the aforementioned shortcomings in the existing technology, this invention provides a target azimuth tracking filtering method based on multi-dimensional motion features, which solves the problem of target azimuth calculation deviation caused by interference signals superimposed on the response signal, resulting in sudden jumps in the target tracking angle.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: a target orientation tracking filtering method based on multi-dimensional motion features, comprising the following steps:

[0007] S1. Calculate the azimuth angle B of the target response signal using the antenna pattern OBA lookup table. meas (t), the target measurement distance R is calculated from the time interval of the response signal. meas (t), the predicted position X along the X-axis at time t is derived using the Cartesian α-β filtering algorithm. pre (t), Predicted position Y along the Y-axis pre (t), Measurement position along the X-axis X meas (t), Measurement position along the Y-axis Y meas (t), Tracking speed XD along the X-axis est (t), Tracking speed YD along the Y axis est (t), Predicted azimuth angle B pre (t) and smooth azimuth B α-β (t);

[0008] S2. Based on the target's track distance, altitude, rate of change of distance, and vertical velocity, calculate the predicted distance R of the target at time t. pre (t), Predicted height Z pre (t) and tracking distance R est (t);

[0009] S3, Tracking height Z via the machine own (t) minus the target predicted height Z pre (t) Obtain the relative height difference of the target, diffZ;

[0010] S4. If the predicted distance R of the target pre If (t) is less than |diffZ|, or the target's pitch angle relative to the machine exceeds 70°, then let the target tracking angle B at time t be... est (t)=B α-β (t), otherwise proceed to step S5;

[0011] S5. If this is an extrapolation of the trajectory calculation, proceed to step S11; if this is an update of the trajectory calculation, proceed to step S6.

[0012] S6. If the target's predicted azimuth angle B pre (t), smooth azimuth angle B α-β (t) Target tracking angle B relative to time t-tp est If (t-tp) are all in the same direction of the turn, calculate the target azimuth turning coefficient count and proceed to step S7; otherwise, proceed to step S8.

[0013] S7. If count < 0, then let count = 0; otherwise, let count = min(count++, 3) and proceed to step S9.

[0014] S8. If count > 0, then let count = -1; otherwise, let count = max(count--, -3), and proceed to step S9.

[0015] S9. Calculate the offset angle δ of the target's relative velocity vector relative to the front of the machine;

[0016] S10. Obtain the target's maximum relative approach speed V based on the offset angle δ. max_rate ;

[0017] S11. Calculate the maximum permissible flight angle B of the target within the time period tp. max_rate (t);

[0018] S12. If the local navigation data is normal, calculate the local turning angle diffHead within the tp time period. own (t) and the turning direction, and proceed to step S13; otherwise, proceed to step S14.

[0019] S13. If the target's turning direction is the same as the machine's turning direction, then the maximum permissible change in the target tracking angle is B. max_est (t)=B max_rate (t)-ε×diffHead own (t), where ε is the local turning factor, proceed to step S15; otherwise, the target and the local machine are turning in opposite directions, and the maximum allowable change in the target tracking angle is B. max_est (t)=B max_rate (t)+ε×diffHead own (t), proceed to step S15;

[0020] S14, Maximum permissible change in target tracking angle (B) max_est (t)=B max_rate (t)+θ×tp, where θ is the machine's default turning speed;

[0021] S15, Calculate B within the time period tp. α-β (t) relative to B est(t-tp) turns the angle diffB, proceed to step S16;

[0022] S16. If the turning angle diffB exceeds the maximum permissible change in azimuth B of the target tracking angle. max_est (t), then the target tracking angle B est (t)=B est (t-tp)+B max_est (t), otherwise the target tracking angle uses the α-β filtered angle, i.e., B. est (t)=B α-β (t).

[0023] Furthermore: the predicted position X along the X-axis pre (t), Predicted position Y along the Y-axis pre (t), Measurement position along the X-axis X meas (t), Measurement position along the Y-axis Y meas (t), Tracking speed XD along the X-axis est (t), Tracking speed YD along the Y axis est (t), Predicted azimuth angle B pre (t) and smooth azimuth B α-β The formula for calculating (t) is:

[0024] X pre (t)=R est (t-tp)×sin(B est (t-tp))+XD est (t-tp)×tp

[0025] Y pre (t)=R est (t-tp)×cos(B est (t-tp))+YD est (t-tp)×tp

[0026] X meas (t)=R meas (t)×sin(B meas (t))

[0027] Y meas (t)=R meas (t)×cos(B meas (t))

[0028] XD est (t)=XD est (t-tp)+(beta / tp)×(X meas (t)-X pre (t))

[0029] YD est (t)=YD est (t-tp)+(beta / tp)×(Y meas (t)-Y pre (t))

[0030] B pre (t)=arctan(X pre (t) / Y pre (t))

[0031] B α-β (t)=arctan(X pre (t)+alpha×(X meas (t)-X pre (t)) / Y pre (t)+alpha×(Y meas (t)-Y pre (t)))

[0032] In the above formula, t is the current time, tp is the time difference between the current measurement and the previous measurement, and R est (t-tp) represents the tracking distance at time t-tp, B est (t-tp) represents the tracking angle at time t-tp, XD est (t-tp) represents the tracking velocity along the X-axis at time t-tp, and YD est (t-tp) represents the tracking velocity along the Y-axis at time t-tp, R meas (t) represents the measured distance at time t, B meas (t) represents the measured angle at time t, beta represents the angular velocity filter gain, and alpha represents the angle filter gain.

[0033] Furthermore: the predicted distance R pre (t), Predicted height Z pre (t) and tracking distance R est The formula for calculating (t) is:

[0034] R pre (t)=R est (t-tp)+RD est (t-tp)×tp

[0035] Z pre (t)=Z est (t-tp)+ZD est (t-tp)×tp

[0036] R est (t)=R pre (t)+α r ×(R meas(t)-R pre (t))

[0037] In the above formula, R est (t-tp) represents the target distance tracked at time t-tp, Z est (t-tp) represents the target height tracked at time t-tp, RD est (t-tp) represents the tracking rate at time t-tp, ZD est (t-tp) represents the vertical velocity tracked at time t-tp, α r This is the distance filter gain.

[0038] Further: The formula for calculating the relative velocity offset angle δ in step S9 is:

[0039]

[0040] Further: Step S10 specifically involves: in the flight path area, when the relative speed offset angle δ of the target is in front of the aircraft, the maximum relative approach speed V max_rate The maximum relative approach speed V is 600 knots when the target δ is to the side of the aircraft. max_rate The maximum relative approach speed V is 566 knots, with the target δ positioned behind the aircraft. max_rate The maximum relative approach speed is 400 knots; in the terminal area, when the relative velocity deviation angle δ indicates that the target is in front of the aircraft, the maximum relative approach speed V is... max_rate The maximum relative approach speed V is 250 knots when the target δ is to the side of the aircraft. max_rate The maximum relative approach speed V is 229 knots, with the target δ positioned behind the aircraft. max_rate It consists of 150 sections.

[0041] Further: the maximum permissible flight angle B in step S11 max_rate The formula for calculating (t) is:

[0042] B max_rate (t)=(1+0.1×count)×arcsin(V max_rate ×tp / R est (t-tp)).

[0043] The beneficial effects of this invention are as follows: By combining the actual measured angle of the target and the α-β filtered smoothed angle using a directional antenna, along with parameters such as the target's flight speed, relative distance, relative velocity vector, and the aircraft's maneuverability, a stable target bearing is ultimately determined through comprehensive evaluation. This invention is a target bearing tracking filtering method based on multi-dimensional motion characteristics. This method effectively reduces the impact of interference signals on the target measurement angle without modifying existing TCAS equipment hardware, ensuring the accuracy and stability of target tracking, improving user satisfaction, and reducing post-installation maintenance costs. Detailed Implementation

[0044] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0045] A target orientation tracking filtering method based on multidimensional motion features includes the following steps:

[0046] S1. Calculate the azimuth angle B of the target response signal using the antenna pattern OBA lookup table. meas (t), the target measurement distance R is calculated from the time interval of the response signal. meas (t), the predicted position X along the X-axis at time t is derived using the Cartesian α-β filtering algorithm. pre (t), Predicted position Y along the Y-axis pre (t), Measurement position along the X-axis X meas (t), Measurement position along the Y-axis Y meas (t), Tracking speed XD along the X-axis est (t), Tracking speed YD along the Y axis est (t), Predicted azimuth angle B pre (t) and smooth azimuth B α-β (t);

[0047] X pre (t)=R est (t-tp)×sin(B est (t-tp))+XD est (t-tp)×tp

[0048] Y pre (t)=R est (t-tp)×cos(B est (t-tp))+YD est(t-tp)×tp

[0049] X meas (t)=R meas (t)×sin(B meas (t))

[0050] Y meas (t)=R meas (t)×cos(B meas (t))

[0051] XD est (t)=XD est (t-tp)+(beta / tp)×(X meas (t)-X pre (t))

[0052] YD est (t)=YD est (t-tp)+(beta / tp)×(Y meas (t)-Y pre (t))

[0053] B pre (t)=arctan(X pre (t) / Y pre (t))

[0054] B α-β (t)=arctan(X pre (t)+alpha×(X meas (t)-X pre (t)) / Y pre (t)+alpha×(Y meas (t)-Y pre (t)))

[0055] In the above formula, t is the current time, tp is the time difference between the current measurement and the previous measurement, which is usually 1 ± 0.1 seconds, and R est (t-tp) represents the tracking distance at time t-tp, B est (t-tp) represents the tracking angle at time t-tp, XD est (t-tp) represents the tracking velocity along the X-axis at time t-tp, and YD est (t-tp) represents the tracking velocity along the Y-axis at time t-tp, R meas (t) represents the measured distance at time t, B meas(t) represents the measured angle at time t, beta represents the angular velocity filter gain, and alpha represents the angle filter gain. The values ​​of beta and alpha depend on the duration of track tracking establishment. If the target track establishment time exceeds 15 seconds, alpha is preferably set to 0.242 and beta is preferably set to 0.025.

[0056] S2. Based on the target's track distance, altitude, rate of change of distance, and vertical velocity, calculate the predicted distance R of the target at time t. pre (t), Predicted height Z pre (t) and tracking distance R est (t);

[0057] R pre (t)=R est (t-tp)+RD est (t-tp)×tp

[0058] Z pre (t)=Z est (t-tp)+ZD est (t-tp)×tp

[0059] R est (t)=R pre (t)+α r ×(R meas (t)-R pre (t))

[0060] In the above formula, R est (t-tp) represents the target distance tracked at time t-tp, Z est (t-tp) represents the target height tracked at time t-tp, RD est (t-tp) represents the tracking rate at time t-tp, ZD est (t-tp) represents the vertical velocity tracked at time t-tp, α r The distance filtering gain is preferably set to 0.67.

[0061] S3, Tracking height Z via the machine own (t) minus the target predicted height Z pre (t) Obtain the relative height difference of the target, diffZ;

[0062] S4. If the predicted distance R of the target pre If (t) is less than |diffZ|, or the target's pitch angle relative to the machine exceeds 70°, then let the target tracking angle B at time t be... est (t)=B α-β (t);

[0063] S5. If this is an extrapolation of the trajectory calculation, proceed to step S11; if this is an update of the trajectory calculation, proceed to step S6.

[0064] S6. If the target's predicted azimuth angle B pre (t), smooth azimuth angle B α-β (t) Target tracking angle B relative to time t-tp est If (t-tp) are all in the same direction of the turn, calculate the target azimuth turning coefficient count and proceed to step S7; otherwise, proceed to step S8.

[0065] S7. If count < 0, then let count = 0; otherwise, let count = min(count++, 3) and proceed to step S9.

[0066] S8. If count > 0, then let count = -1; otherwise, let count = max(count--, -3), and proceed to step S9.

[0067] S9. Calculate the offset angle δ of the target's relative velocity vector relative to the front of the machine;

[0068]

[0069] S10. Obtain the target's maximum relative approach speed V based on the offset angle δ. max_rate In the flight path area, when the relative speed deviation angle δ indicates that the target is ahead of the aircraft, the maximum relative approach speed V is... max_rate The maximum relative approach speed V is 600 knots when the target δ is to the side of the aircraft. max_rate The maximum relative approach speed V is 566 knots when the target δ is behind the aircraft. max_rate The maximum relative approach speed is 400 knots; in the terminal area, when the relative velocity deviation angle δ indicates that the target is in front of the aircraft, the maximum relative approach speed V is... max_rate The maximum relative approach speed V is 250 knots when the target δ is to the side of the aircraft. max_rate The maximum relative approach speed V is 229 knots, with the target δ positioned behind the aircraft. max_rate It consists of 150 sections.

[0070] S11. Calculate the maximum permissible flight angle B of the target within the time period tp. max_rate (t); B max_rate (t)=(1+0.1×count)×arcsin(V max_rate ×tp / R est (t-tp))

[0071] S12. If the local navigation data is normal, calculate the local turning angle diffHead within the tp time period.own (t) and the turning direction, and proceed to step S13; otherwise, proceed to step S14.

[0072] S13. If the target's turning direction is the same as the machine's turning direction, then the maximum permissible change in the target tracking angle is B. max_est (t)=B max_rate (t)-ε×diffHead own (t), proceed to step S15, otherwise the target and the machine are turning in opposite directions, and the maximum allowable change in the target tracking angle is B. max_est (t)=B max_rate (t)+ε×diffHead own (t), proceed to step S15; ε is the turning factor of the machine, which can be adjusted according to the performance of the carrier aircraft, and is usually between [0.5,1].

[0073] S14, Maximum permissible change in target tracking angle (B) max_est (t)=B max_rate (t)+θ×tp, where θ is the default turning speed of the machine, which can be adjusted according to the performance of the carrier aircraft, and is usually set to 3° / s;

[0074] S15, Calculate B within the time period tp. α-β (t) relative to B est (t-tp) turns the angle diffB, proceed to step S16;

[0075] S16. If the turning angle diffB exceeds the maximum permissible change in azimuth B of the target tracking angle. max_est (t), then the target tracking angle B est (t)=B est (t-tp)+B max_est (t), otherwise the target tracking angle uses the α-β filtered angle, i.e., B. est (t)=B α-β (t).

[0076] This invention utilizes the actual measured angle of the target using a directional antenna and the α-β filtered smoothed angle, combined with parameters such as the target's flight speed, relative distance, relative velocity vector, and the aircraft's maneuverability, to comprehensively evaluate and ultimately determine a stable target bearing. This invention is a target bearing tracking filtering method based on multi-dimensional motion characteristics. This method effectively reduces the impact of interference signals on the target measurement angle without modifying existing TCAS equipment hardware, ensuring the accuracy and stability of target tracking, improving user satisfaction, and reducing post-installation maintenance costs.

Claims

1. A target orientation tracking filtering method based on multi-dimensional motion features, characterized in that, Includes the following steps: S1. Calculate the azimuth angle B of the target response signal using the antenna pattern OBA lookup table. meas (t), the target measurement distance R is calculated from the time interval of the response signal. meas (t), the predicted position X along the X-axis at time t is derived using the Cartesian α-β filtering algorithm. pre (t), Predicted position Y along the Y-axis pre (t), Measurement position along the X-axis X meas (t), Measurement position along the Y-axis Y meas (t), Tracking speed XD along the X-axis est (t), Tracking speed YD along the Y axis est (t), Predicted azimuth angle B pre (t) and smooth azimuth B α-β (t); S2. Based on the target's track distance, altitude, rate of change of distance, and vertical velocity, calculate the predicted distance R of the target at time t. pre (t), Predicted height Z pre (t) and tracking distance R est (t); S3, Tracking height Z via the machine own (t) minus the target predicted height Z pre (t) Obtain the relative height difference of the target, diffZ; S4. If the predicted distance R of the target pre If (t) is less than diffZ, or the target's pitch angle relative to the machine exceeds 70°, then let the target tracking angle B at time t be... est (t)=B α-β (t), otherwise proceed to step S5; S5. If this is an extrapolation of the trajectory calculation, proceed to step S11; if this is an update of the trajectory calculation, proceed to step S6. S6. If the target's predicted azimuth angle B pre (t), smooth azimuth angle B α-β (t) Target tracking angle B relative to time t-tp est If (t-tp) are all in the same direction of the turn, calculate the target azimuth turning coefficient count and proceed to step S7; otherwise, proceed to step S8. S7. If count < 0, then let count = 0; otherwise, let count = min(count++, 3) and proceed to step S9. S8. If count > 0, then let count = -1; otherwise, let count = max(count--, -3), and proceed to step S9. S9. Calculate the offset angle δ of the target's relative velocity vector relative to the front of the machine; S10. Obtain the target's maximum relative approach speed V based on the offset angle δ. max_rate ; S11. Calculate the maximum permissible flight angle B of the target within the time period tp. max_rate (t); S12. If the local navigation data is normal, calculate the local turning angle diffHead within the tp time period. own (t) and the turning direction, and proceed to step S13; otherwise, proceed to step S14. S13. If the target's turning direction is the same as the machine's turning direction, then the maximum permissible change in the target tracking angle is B. max_est (t)=B max_rate (t)-ε×diffHead own (t), where ε is the local turning factor, proceed to step S15; otherwise, the target and the local machine are turning in opposite directions, and the maximum allowable change in the target tracking angle is B. max_est (t)=B max_rate (t)+ε×diffHead own (t), proceed to step S15; S14, Maximum permissible change in target tracking angle (B) max_est (t)=B max_rate (t)+θ×tp, where θ is the machine's default turning speed, proceed to step S15; S15, Calculate B within the time period tp. α-β (t) relative to B est (t-tp) turns the angle diffB, proceed to step S16; S16. If the turning angle diffB exceeds the maximum permissible change in azimuth B of the target tracking angle. max_est (t), then the target tracking angle B est (t)=B est (t-tp)+B max_est (t), otherwise the target tracking angle uses the α-β filtered angle, i.e., B. est (t)=B α-β (t).

2. The target orientation tracking filtering method based on multi-dimensional motion features according to claim 1, characterized in that, The predicted position X along the X-axis pre (t), Predicted position Y along the Y-axis pre (t), Measurement position along the X-axis X meas (t), Measurement position along the Y-axis Y meas (t), Tracking speed XD along the X-axis est (t), Tracking speed YD along the Y axis est (t), Predicted azimuth angle B pre (t) and smooth azimuth angle B α-β The formula for calculating (t) is: X pre (t)=R est (t-tp)×sin(B est (t-tp))+XD est (t-tp)×tp Y pre (t)=R est (t-tp)×cos(B est (t-tp))+YD est (t-tp)×tp X meas (t)=R meas (t)×sin(B meas (t)) Y meas (t)=R meas (t)×cos(B meas (t)) XD est (t)=XD est (t-tp)+(beta / tp)×(X meas (t)-X pre (t)) YD est (t)=YD est (t-tp)+(beta / tp)×(Y meas (t)-Y pre (t)) B pre (t)=arctan(X pre (t) / Y pre (t)) B α-β (t)=arctan(X pre (t)+alpha×(X meas (t)-X pre (t)) / Y pre (t)+alpha×(Y meas (t)-Y pre (t))) In the above formula, t is the current time, tp is the time difference between the current measurement and the previous measurement, and R est (t-tp) represents the tracking distance at time t-tp, B est (t-tp) represents the tracking angle at time t-tp, XD est (t-tp) represents the tracking velocity along the X-axis at time t-tp, and YD est (t-tp) represents the tracking velocity along the Y-axis at time t-tp, R meas (t) represents the measured distance at time t, B meas (t) represents the measured angle at time t, beta represents the angular velocity filter gain, and alpha represents the angle filter gain.

3. The target orientation tracking filtering method based on multi-dimensional motion features according to claim 1, characterized in that, The predicted distance R pre (t), Predicted height Z pre (t) and tracking distance R est The formula for calculating (t) is: R pre (t)=R est (t-tp)+RD est (t-tp)×tp Z pre (t)=Z est (t-tp)+ZD est (t-tp)×tp R est (t)=R pre (t)+α r ×(R meas (t)-R pre (t)) In the above formula, R est (t-tp) represents the target distance tracked at time t-tp, Z est (t-tp) represents the target height tracked at time t-tp, RD est (t-tp) represents the tracking rate at time t-tp, ZD est (t-tp) represents the vertical velocity tracked at time t-tp, α r This is the distance filter gain.

4. The target orientation tracking filtering method based on multi-dimensional motion features according to claim 1, characterized in that, The formula for calculating the relative velocity offset angle δ in step S9 is as follows:

5. The target orientation tracking filtering method based on multi-dimensional motion features according to claim 1, characterized in that, Specifically, step S10 involves the following: In the flight path area, when the relative speed offset angle δ of the target is in front of the aircraft, the maximum relative approach speed V max_rate The maximum relative approach speed V is 600 knots when the target δ is to the side of the aircraft. max_rate The maximum relative approach speed V is 566 knots, with the target δ positioned behind the aircraft. max_rate The maximum relative approach speed is 400 knots; in the terminal area, when the relative velocity deviation angle δ indicates that the target is in front of the aircraft, the maximum relative approach speed V is... max_rate The maximum relative approach speed V is 250 knots when the target δ is to the side of the aircraft. max_rate The maximum relative approach speed V is 229 knots, with the target δ positioned behind the aircraft. max_rate It consists of 150 sections.

6. The target orientation tracking filtering method based on multi-dimensional motion features according to claim 1, characterized in that, The maximum permissible flight angle B in step S11 max_rate The formula for calculating (t) is: B max_rate (t)=(1+0.1×count)×arcsin(V max_rate ×tp / R est (t-tp))。

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