CN111816005A

CN111816005A - Remote piloted aircraft environment monitoring optimization method based on ADS-B

Info

Publication number: CN111816005A
Application number: CN201910288490.2A
Authority: CN
Inventors: 吴瑀倩; 肖刚; 赵文浩; 许佳炜; 王彦然; 薛鄹涛
Original assignee: Shanghai Jiaotong University
Current assignee: Shanghai Jiaotong University
Priority date: 2019-04-11
Filing date: 2019-04-11
Publication date: 2020-10-23

Abstract

A method for monitoring and optimizing the environment of a remotely piloted aircraft based on ADS-B includes using statistical model to carry out Kalman filtering, carrying out local optimization on ADS-B navigation data and TCAS data respectively, carrying out optimal information fusion according to matrix weighted linear minimum variance criterion to obtain a fusion track, improving flight safety and flight quality through the fusion track, optimizing flight environment monitoring, analyzing traffic environment information from ADS-B signals, determining a traffic situation organization mode according to a flight plan and a current flight stage of a flight management system, and displaying and constructing corresponding flight traffic situations through an in-cabin display system to realize air anti-collision enhanced monitoring and collision situation perception and monitoring. The invention effectively reduces the false alarm rate/false alarm rate of the TCAS by fusing the data of the ADS-B and the TCAS, and provides a monitoring capability improving scheme aiming at different flight scenes and flight stages based on the ADS-B technology so as to improve the traffic situation perception capability of remote driving and further improve the flight safety under the remote driving condition.

Description

Remote piloted aircraft environment monitoring optimization method based on ADS-B

Technical Field

The invention relates to a technology in the field of aviation control, in particular to a remote piloting aircraft environment monitoring optimization method based on broadcast automatic dependent surveillance (ADS-B).

Background

The existing Automatic Dependent Surveillance (ADS) includes several modes such as ADS-Addressing (ADS-A) and ADS-Contract (ADS-C), wherein ADS-B (ADS-Broadcast), i.e. Broadcast type automatic dependent surveillance, precise positioning information generated by airborne navigation equipment and GNSS positioning system, ground equipment and other aircrafts receive the information through an air datA link, and the aircrafts and the ground system perform air-to-air, air-to-ground and ground integrated cooperative surveillance through A high-speed datA link. ADS-B is different from ADS-A/C in that it does not use point-to-point communication but broadcast. Thus, the monitoring of the ground facing aircraft can be realized, and the mutual monitoring between the aircraft and the aircraft can also be realized. ADS-B is an applicable relatively accurate airspace monitoring technology and is considered as an important component for realizing free flight in the future by FAA. The system can be used for collision avoidance, monitoring and proximity assistance, plays a large role, and has obvious advantages in real-time performance, accuracy and economy compared with a primary monitoring radar system and a secondary monitoring radar system. ADS-B and related technologies are necessary directions for developing airspace situation monitoring in the future, the implementation of the ADS-B is matched with future air traffic modes including free flight, and the ADS-B and related technologies have good reference effects on civil aircraft manufacturing.

The ADS-B system data can improve the prediction precision of a Traffic Collision Avoidance System (TCAS), improve the probability of long-distance real alarm and reduce the false alarm rate and the false alarm rate. After the TCAS II system is combined with the ADS-B system, great benefits can be obtained in the aspects of air alarm accuracy and flight safety. After the flight path fusion and decision optimization are adopted, the precision of the system is improved, even if certain key information of a TCAS or ADS-B single party is lost, the normal operation of the system can be still ensured, and the probability of failure of the alarm system is reduced.

Disclosure of Invention

The invention provides a remote piloting aircraft environment monitoring optimization method based on ADS-B (automatic dependent surveillance-broadcast system) aiming at the defects of the traditional monitoring equipment such as TCAS (traffic collision avoidance system), TAWS (traffic accident prevention system), WXR (WX-ray diffraction) and the like of the traditional monitoring system, the false alarm rate/false alarm rate of the TCAS is effectively reduced by fusing the data of the ADS-B and the TCAS, and a monitoring capacity improving scheme is provided aiming at different flight scenes and flight stages based on the ADS-B technology so as to improve the traffic situation perception capacity of remote piloting and improve the flight safety under the remote piloting condition.

The invention is realized by the following technical scheme:

the invention relates to a remote piloting aircraft environment monitoring optimization method based on ADS-B, which comprises the steps of utilizing a statistical model to carry out Kalman filtering, respectively carrying out local optimization on ADS-B navigation data and TCAS data, carrying out optimal information fusion according to a matrix weighted linear minimum variance criterion to obtain a fusion track, improving flight safety and flight quality through the fusion track, optimizing flight environment monitoring, analyzing traffic environment information from an ADS-B signal, determining a traffic situation organization mode according to a flight plan and a current flight stage of a Flight Management System (FMS), displaying and constructing corresponding flight traffic situations through an in-cabin display system (CDIT), and realizing air anti-collision enhanced monitoring and collision situation perception and monitoring.

The Kalman filtering adopts a feedback control method to estimate the process state, a time updating equation is used for calculating the current state variable and the error covariance estimation value in time, and a measurement updating equation is used for feedback.

The time updating equation is as follows:

Q(k)＝2aσ_a ²Q*f(x，y，σ)，

wherein: t is the sampling period, a is the maneuver frequency, A (k) is the state transition matrix, B (k) is the input matrix, Q (k) is the process noise matrix, σ_a ²In order to be the variance of the maneuvering acceleration,

in order to be able to estimate the state,

is the state covariance.

The measurement update equation is as follows:

wherein: k_kAs a result of the process parameters,

for state estimation, P_kIs the state covariance.

The fused track refers to the following steps: p_f(k)＝(P_ADS-B(k)^-1+P_TCAS(k)^-1)^-1，X_f(k)＝P_f(k)(P_TCAS(k)^- ¹X_TCAS(k)+P_ADS-B(k)^-1X_ADS-B(k) Whereinsaid: p_f(k) To fuse the systematic error covariance, P_ADS-B(k) Is ADS-B system error covariance, P_TCAS(k) Is the TCAS system error covariance; x_f(k) To fuse the system state matrices, X_TCAS(k) Is a TCAS system state matrix, X_ADS-B(k) Is the ADS-B system state matrix.

Unbiased estimation of known L sensors

And the known estimation error covariance matrix P_ijL, i, j ═ 1.. The unbiased fusion estimate of linear minimum variance weighted by matrixMeter

The optimal weighting array is [ A ]₁，...，A_L]＝(e^TP^-1e)^-1e^TP^-1Optimal fusion estimation error variance matrix P₀＝(e^TP^-1e)^-1。

In the invention, L is 2.

The ADS-B signal comprises an ADS-B signal sent to an airspace from a transmitter or an ADS-B signal generated by actual flight of a civil aviation or navigation aircraft in the airspace, wherein the ADS-B signal comprises a flight identification number, a horizontal position, an altitude, a horizontal speed and a vertical speed of the aircraft; heading, etc.

The aerial anti-collision enhanced monitoring is as follows: the ADS-B signal can be displayed in real time in CDIT and sent as the input of a TCAS subsystem after necessary data decoding, data conversion and the like are carried out on the ADS-B signal at the equipment end, then the ADS-B navigation data and the TCAS data are respectively subjected to local optimization by the method, then optimal information fusion is carried out according to a matrix weighted linear minimum variance criterion, and then a display and alarm monitoring mode is established according to the flight phase state and the flight interval definition.

The collision situation perception and monitoring means that: according to the traffic environment information acquired by ADS-B, based on the air management system airspace information communication of the ground surveillance radar, according to the flight interval definition of the flight phase, the TCAS is supported to calculate the near flight track, the FMS is used for planning the air route, the flight traffic conflict is predicted, and the display and the consultation including the flight traffic conflict consultation (TA) and the air resolution consultation (RA) are provided.

Technical effects

Compared with the prior art, the method has the advantages that on the basis of introducing a track tracking model, Kalman filtering and optimal information fusion, TCAS and ADS-B monitoring systems are fused, a track is generated through an aircraft space motion model, the data accuracy of TCAS, ADS-B and the fusion system is analyzed by using longitude, latitude and altitude three-dimensional information, the time (CPA) of reaching the nearest point of two machines is calculated by a core processing model of an air traffic collision avoidance system, the income of false alarm and missed alarm is improved by analyzing the data fusion, and an intruder simulator performs maneuver evasion in a loop during RA decision making. The information fusion positive gain and negative gain adopt flight path fusion and decision optimization, and then the precision of the system is improved, so that false alarm and false alarm conditions of the system are improved, the system safety is improved, and the remote piloted aircraft environment monitoring is optimized.

Drawings

FIG. 1 is a schematic view of an airspace aircraft track;

FIG. 2 is a diagram illustrating a comparison of mean square error of a subsystem and a fusion system;

FIG. 3 is a schematic diagram of a mean square error comparison of a TCAS and a fusion system;

FIG. 4 is a schematic diagram of a mean square error comparison between ADS-B and a fusion system;

FIG. 5 is a CPA (closest point of approach) time calculation graph;

FIG. 6 is a TCAS algorithm flow chart;

FIG. 7 is a statistical chart of false alarm and false alarm failures of various systems;

fig. 8 is a monitoring capability improvement scheme block diagram.

FIG. 9 is a diagram simulating a human maneuver avoiding track in a loop at decision time.

Fig. 10 is a graph of RA decision interval CPA cumulative deviation.

Fig. 11 is a CPA statistical chart at the amplitude of 100m of an injection step fault.

Fig. 12 is a statistical graph of false alarm and false alarm of each system when the amplitude of the injection step fault is 100 m.

Figure 13 is a CPA statistical plot of injected ramp faults accumulated at 100 m.

Fig. 14 is a statistical graph of system false alarms and false alarm failures at 100m accumulation for injected ramp faults.

Detailed Description

In the embodiment, a flight path is generated by an aircraft space motion model, local Kalman filtering is carried out on longitude, latitude and altitude three-dimensional information of an ADS-B system and a TCAS system based on a current statistical model, data accuracy of the TCAS system, the ADS-B system and a fused system is analyzed, time of reaching a nearest point of two machines is calculated by combining a core processing model of an air traffic collision avoidance system, false alarm and false alarm conditions of each system are counted, and income brought to a combined monitoring system by data fusion is analyzed.

Simulation conditions are as follows: the flight process goes through 3000s, the sampling period T is 1s, and the initial position of the aircraft is as follows: east longitude 98 degrees, north latitude 29 degrees, height 4502 meters; the initial position of the invader is 106 degrees east longitude, 29 degrees north latitude, 3000 meters height, TCAS observation noise standard deviation is 20, and ADS-B observation noise standard deviation is 10. The flight path of the two machines in space is shown in figure 1. Here, the flight path of fig. 1 is that the aircraft gradually climbs upwards from 300m and then goes high, and since XY coordinates are in degrees of longitude and latitude, respectively, the height 4000m has a small variation with respect to the XY axes, so that the rising section is a slope line.

The statistical results obtained by performing the experiments for 200 times are analyzed by combining with the graphs in fig. 2 to 4, the mean square error of the fused system is smaller than that of the TCAS and ADS-B subsystems, namely the fused track information is superior to the information obtained by performing local Kalman filtering on the subsystems.

As shown in fig. 5, a CPA curve graph is obtained by adding the local kalman filtering and the fused flight path to a TCAS core solution model to perform CPA value solution.

As shown in fig. 6, a TCAS algorithm flowchart is shown, track information is generated through a flight calculation model, a system core processing program is fused for data reception, the CPA algorithm is entered after conversion between a geodetic coordinate system and a geocentric coordinate system, the relative position of the local computer and the other computers is calculated, the encounter time is estimated, and the RA decision is entered after collision pre-judgment is completed.

In the embodiment, 200 independent repeated experiments are performed, and the times of early warning and late warning of each system in the warning periods of TA (CPA is 35-45s) and RA (CPA <35s) are counted. The false alarm statistics is that the actual system alarm time is advanced by the theoretical alarm time and exceeds the threshold value (1s), and the actual system alarm of the missed alarm lags the theoretical alarm time and exceeds the threshold value (1 s). As shown in fig. 7 and table 1, qualitative and quantitative analysis can be performed to obtain that the fusion system can reduce the number of false alarms and false alarm misses in both TA alarm and RA alarm intervals. The alarm leakage and delay alarm compress the evasion reaction time of the system and the pilot, and the flight safety is seriously influenced, so that the more accurate alarm time can improve the safety of the system and bring forward income.

	TCAS	ADS-B	Fusion system
				False alarm (TA time)/time	1089	534	380
Missed alarm (TA hour)/time	877	504	365
				False alarm (RA time)/time	1036	329	170
Missed alarm (RA hour)/time	999	379	193

The sub-scheme shown in fig. 8 can improve the environment monitoring capability of remote piloting for different monitoring scenes in different flight phases, such as the five processes of conventional flight process, scene slide process, approach flight process, ocean region flight process and flight interval maintenance, and can also be subdivided into different monitoring scenes.

The invention is further described below with respect to certain exemplary flight phases and scenarios. The part selects the situation that TCAS obtains RA alarm.

And the TCAS core processing system evaluates the spatial domain situation to obtain an RA alarm decision. The dynamic response of a human in a loop, including the response delay of the actual operation of a pilot and the response delay of a mechanical-electrical system can be considered by simulating the dynamic avoidance of the human climbing at the climbing rate of 1500 feet per minute by an intrusion machine, and the computer simulation assumes the delay to be a constant.

The deviation data in the RA decision maneuvering interval is accumulated, and the accumulated deviation of the CPA can be improved by the fusion system obtained from the graph 10, so that the pilot can respond more accurately when maneuvering evasion is carried out, and the flight safety is guaranteed.

If ADS-B information in the fusion system is lost, the local filtering track of the ADS-B is chaotic, and if the fusion system does not take measures, unpredictable faults which may not exist in an original fault library can occur. And the data distortion is detected by calculating the square of the local change rate through the data information at the current moment and the previous moments, the fusion weight is adaptively adjusted, the failure of a single system is diluted, although the accuracy is reduced compared with that before the failure occurs and is degraded to the accuracy of TCAS and the false alarm and false alarm conditions, the stable operation of the system can still be ensured, and the fusion benefit is brought.

And injecting steps with different amplitudes and system responses obtained by slope faults with different speeds into the latitude information of the ADS-B, analyzing the false alarm and false alarm failure statistical results of the TCAS and the fusion system, and proving that the fusion system can ensure the stable operation of the system in the fault mode and is superior to the safety of a single TCAS.

As shown in fig. 11 and fig. 12, a CPA statistical graph and a false alarm and false alarm failure statistical graph of each system are respectively shown when the amplitude of the injection step fault is 100 m.

As shown in fig. 13 and 14, CPA statistical graphs and false alarm missing statistical graphs of each system when accumulating at 100m for injected ramp faults are shown, respectively.

The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

Claims

1. A method for monitoring and optimizing the environment of a remotely piloted aircraft based on ADS-B includes using statistical model to carry out Kalman filtering, carrying out local optimization on ADS-B navigation data and TCAS data respectively, carrying out optimal information fusion according to matrix weighted linear minimum variance criterion to obtain a fusion track, improving flight safety and flight quality through the fusion track, optimizing flight environment monitoring, analyzing traffic environment information from ADS-B signals, determining a traffic situation organization mode according to a flight plan and a current flight stage of a flight management system, and displaying and constructing corresponding flight traffic situations through an in-cabin display system to realize air anti-collision enhanced monitoring and collision situation perception and monitoring.

2. The method of claim 1, wherein the kalman filter estimates the process state using a feedback control method, wherein a time update equation is used to forward estimate the current state variable and the estimated error covariance value in time, and wherein a measurement update equation is used for feedback.

3. The method of claim 1, wherein the time update equation is:

Q(k)＝2aσ_a ²Q*f(x,y,σ)，

wherein: t is the sampling period, a is the maneuver frequency, A (k) is the state transition matrix, B (k) is the input matrix, Q (k) is the process noise matrix, σ_a ²In order to be the variance of the maneuvering acceleration,in order to be able to estimate the state,

is the state covariance.

4. The method of claim 1, wherein the measurement update equation is:

wherein: k_kAs a result of the process parameters,

for state estimation, P_kIs the state covariance.

5. The method of claim 1, wherein said merged track is: p_f(k)＝(P_ADS-B(k)^-1+P_TCAS(k)^-1)^-1，X_f(k)＝P_f(k)(P_TCAS(l)^-1X_TCAS(k)+P_ADS-B(k)^-1X_ADS-B(k) Whereinsaid: p_f(k) To fuse the systematic error covariance, P_ADS-B(k) Is ADS-B system error covariance, P_TCAS(k) Is the TCAS system error covariance; x_f(k) To fuse the system state matrices, X_TCAS(k) Is a TCAS system state matrix, X_ADS-B(k) Is ADS-B system state matrix;

unbiased estimation of known L sensors

And the known estimation error covariance matrix P_ijI, j ═ 1, … L, unbiased fusion estimate of linear minimum variance weighted by matrix

The optimal weighting array is [ A ]₁,…,A_L]＝(e^TP^-1e)^- ¹e^TP^-1Optimal fusion estimation error variance matrix P₀＝(e^TP^-1e)^-1。

6. The method of claim 1, wherein the ADS-B signals comprise ADS-B signals transmitted from a transmitter into an airspace or ADS-B signals generated by actual flight of civil or general aviation aircraft in the airspace, including flight identification number, horizontal position, altitude, horizontal velocity, vertical velocity of the aircraft; and (4) course.

7. The method as claimed in claim 1 or 6, wherein said airborne collision avoidance enhanced monitoring is: the ADS-B signal can be displayed in real time in CDIT and sent as the input of a TCAS subsystem after necessary data decoding, data conversion and the like are carried out on the ADS-B signal at the equipment end, then the ADS-B navigation data and the TCAS data are respectively subjected to local optimization by the method, then optimal information fusion is carried out according to a matrix weighted linear minimum variance criterion, and then a display and alarm monitoring mode is established according to the flight phase state and the flight interval definition.

8. The method according to claim 1 or 6, wherein said collision situation sensing and monitoring is: according to the traffic environment information acquired by ADS-B, based on the air management system airspace information communication of the ground surveillance radar, according to the flight interval definition of the flight phase, the TCAS is supported to calculate the near flight track, the flight traffic conflict is predicted through the FMS flight planning route, and the display and the consultation including the flight traffic conflict consultation and the air resolution consultation are provided.