CN111816005A - ADS-B-based optimization method for environmental monitoring of remote piloted aircraft - Google Patents

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

ADS-B-based optimization method for environmental monitoring of remote piloted aircraft

technical field

The invention relates to a technology in the field of aviation control, in particular to a method for optimizing the environment monitoring of remote piloted aircraft based on Automatic Dependent Surveillance-Broadcast (ADS-B).

Background technique

The existing Automatic Dependent Surveillance (ADS) includes several modes such as ADS-Addressing (ADS-A) and ADS-Contract (ADS-C). Among them, ADS-B (ADS-Broadcast), That is, automatic dependent surveillance broadcast, precise positioning information generated by airborne navigation equipment and GNSS positioning systems, ground equipment and other aircraft receive this information through aeronautical data links, and aircraft and ground systems conduct air-to-air, air-to-air through high-speed data links. Integrated coordinated surveillance on the ground and on the ground. The biggest difference between ADS-B and ADS-A/C is that it does not use point-to-point communication, but broadcast. In this way, not only the monitoring of the aircraft on the ground can be realized, but also the mutual monitoring between the aircraft and the aircraft can be realized. ADS-B is a relatively accurate airspace surveillance technology that can be applied, and is considered by the FAA to be an important part of free flight in the future. It can be used in collision avoidance, surveillance and approach assistance, and play a greater role. Compared with primary surveillance radar and secondary surveillance radar systems, it has obvious advantages in real-time, accuracy and economy. ADS-B and its related technologies are the inevitable direction for the future development of airspace situational surveillance. Its implementation is in line with future air traffic modes including free flight, and it also has a good reference for civil aircraft manufacturing.

The ADS-B system data can improve the prediction accuracy of the Air Collision Avoidance System (TCAS), increase the probability of long-distance true alarms, and reduce the false alarm rate and missed alarm rate. The combination of TCAS II system and ADS-B system will have great benefits in air warning accuracy and flight safety. After the track fusion and decision optimization are adopted, the accuracy of the system is improved, and even if a certain key information of TCAS or ADS-B is lost unilaterally, the normal operation of the system can still be guaranteed, and the probability of failure of the alarm system is reduced.

SUMMARY OF THE INVENTION

Aiming at the shortcomings of the existing monitoring systems that still mainly rely on traditional monitoring equipment such as TCAS, TAWS, and WXR, the present invention proposes an ADS-B-based remote piloted aircraft environment monitoring optimization method. The false alarm rate/missing alarm rate of TCAS, and based on the ADS-B technology, a monitoring capability improvement plan is proposed for different flight scenarios and flight stages to improve the traffic situational awareness of remote driving, thereby improving flight safety in remote driving situations. sex.

The present invention is achieved through the following technical solutions:

The invention relates to an ADS-B-based remote-piloted aircraft environment monitoring optimization method. Kalman filtering is performed by using a statistical model, and after local optimization is performed on ADS-B navigation data and TCAS data respectively, the optimization is performed according to the matrix weighted linear minimum variance criterion. Excellent information fusion to obtain the fusion track, and improve flight safety and flight quality through the fusion track, optimize the flight environment monitoring, and then analyze the traffic environment information from the ADS-B signal, according to the flight management system (FMS) flight plan The traffic situation organization mode is determined according to the current flight stage, and the corresponding flight traffic situation is displayed and constructed through the in-cabin display system (CDIT), so as to realize enhanced surveillance of air collision avoidance and collision situational awareness and monitoring.

The described Kalman filter adopts the method of feedback control to estimate the process state, the time update equation is used to estimate the current state variable and the estimated value of the error covariance forward in time, and the measurement update equation is used for feedback.

Q(k)＝2aσ_a ²Q*f(x，y，σ)，

其中：T为采样周期，a为机动频率，A(k)为状态转移矩阵，B(k)为输入矩阵，Q(k)为过程噪声矩阵，σ_a ²为机动加速度方差，

为状态估计，

为状态协方差。The time update equation described is:

Q(k)=2aσ _a ² Q*f(x, y, σ),

Where: T is the sampling period, a is the maneuvering frequency, A(k) is the state transition matrix, B(k) is the input matrix, Q(k) is the process noise matrix, σ _a ² is the maneuvering acceleration variance,

is the state estimate,

is the state covariance.

其中：K_k为过程参数，

为状态估计，P_k为状态协方差。The measurement update equation is:

Where: K _k is the process parameter,

is the state estimate, and P _k is the state covariance.

The fusion track refers to: P _f (k)=(P _ADS-B (k) ^-1 +P _TCAS (k) ^-1 ) ^-1 , X _f (k)=P _f (k)(P _TCAS (k) ^{- 1} X _TCAS (k)+P _ADS-B (k) ^-1 X _ADS-B (k)), where: P _f (k) is the fusion system error covariance, P _ADS-B (k ) is the ADS-B system error covariance, P _TCAS (k) is the TCAS system error covariance; X _f (k) is the fusion system state matrix, X _TCAS (k) is the TCAS system state matrix, X _ADS-B (k ) is the ADS-B system state matrix.

且已知估计误差协方差阵P_ij，i，j＝1，...L，按矩阵加权线性最小方差无偏融合估计

最优加权阵为[A₁，...，A_L]＝(e^TP^-1e)^-1e^TP^-1，最优融合估计误差方差阵P₀＝(e^TP^-1e)^-1。Unbiased estimation of known L sensors

And the estimated error covariance matrix P _ij , i, j=1, ... L is known, and the linear minimum variance unbiased fusion estimation is weighted by the matrix

The optimal weighting matrix is [A ₁ , . . . , A _L ]=(e ^T P ^-1 e) ^-1 e ^T P ^-1 , and the optimal fusion estimation error variance matrix P ₀ =(e ^T P ^-1 e ) ^-1 .

In the present invention, L=2.

The ADS-B signal includes the ADS-B signal sent from the transmitter to the airspace or the ADS-B signal generated by the actual flight of civil aviation or general aviation aircraft in the airspace, including the flight identification number, horizontal position, altitude, level of the aircraft. Speed, vertical speed; heading, etc.

The air-collision enhanced monitoring refers to: after the ADS-B signal is processed by necessary data decoding and data conversion on the device side, it can be displayed in CDIT in real time and sent as the input of the TCAS subsystem, and then the The ADS-B navigation data and the TCAS data are optimized separately, and then the optimal information fusion is carried out according to the matrix weighted linear minimum variance criterion, and then the display and alarm monitoring mode is established according to the flight stage state and the definition of the flight interval.

The collision situation awareness and monitoring refers to: according to the traffic environment information obtained by ADS-B, the airspace information communication of the air traffic control system based on the ground surveillance radar, according to the definition of the flight interval in the flight stage, support TCAS to calculate the trajectory of the approaching flight, Through the FMS system, flight planning routes, flight traffic conflicts are predicted, and display and consultation including flight traffic conflict advisory (TA) and air decision advisory (RA) are provided.

technical effect

Compared with the prior art, on the basis of introducing the track tracking model, Kalman filter and optimal information fusion, the present invention fuses the TCAS and ADS-B surveillance systems, generates the track through the aircraft space motion model, and uses Longitude, latitude, and altitude 3D information analyzes the data accuracy of TCAS, ADS-B and the fusion system. The core processing model of the air traffic collision avoidance system calculates the time to the closest point of approach (CPA) between the two aircraft, and analyzes data fusion for improving virtual reality. The income of the alarm and the missed alarm, when the RA decision is made, the intrusion machine simulates the human to maneuver in the loop to avoid it. The positive and negative benefits of information fusion, after the use of track fusion and decision-making optimization, the accuracy of the system is improved, thereby improving the system's false alarms and missed alarms, improving system security, and optimizing the environmental monitoring of remotely piloted aircraft.

Description of drawings

Figure 1 is a schematic diagram of the airspace aircraft track;

Figure 2 is a schematic diagram of the comparison of the mean square error of the sub-system and the fusion system;

Figure 3 is a schematic diagram of the mean square error comparison between TCAS and fusion systems;

Figure 4 is a schematic diagram of the comparison of the mean square error of ADS-B and the fusion system;

Fig. 5 is the time solution diagram of CPA (closest point);

Figure 6 is a flowchart of the TCAS algorithm;

Figure 7 is a statistical diagram of false alarms and missed alarms in each system;

FIG. 8 is a framework diagram of a monitoring capability improvement scheme.

Figure 9 is the evasive trajectory diagram of the simulated person maneuvering in the loop when making a decision.

Figure 10 is a graph of cumulative deviation of CPA in the RA decision interval.

Fig. 11 is a CPA statistic diagram when the amplitude of the injected step fault is 100m.

Figure 12 is a statistical diagram of false alarms and missed alarms of each system when the amplitude of the injected step fault is 100m.

Figure 13 shows the CPA statistics when the injection ramp faults are accumulated at 100m.

Figure 14 Statistical diagram of false alarms and missed alarms of each system when the injection slope fault is accumulated by 100m.

Detailed ways

In this embodiment, the flight path is generated by the space motion model of the aircraft, and based on the current statistical model, local Kalman filtering is performed on the three-dimensional information of longitude, latitude, and altitude of the ADS-B and TCAS systems, and the TCAS system, the ADS-B system, and the fusion system are analyzed. Combined with the core processing model of the air traffic collision avoidance system, it calculates the time to reach the closest point between the two aircraft, counts the false alarms and missed alarms of each system, and analyzes the benefits brought by data fusion to the combined monitoring system.

Simulation conditions: The flight process is 3000s, the sampling period is T=1s, the initial position of the aircraft is 98 degrees east longitude, 29 degrees north latitude, and 4502 meters high; the initial position of the intruder is 106 degrees east longitude, 29 degrees north latitude, and 3000 meters high, and the TCAS observation noise The standard deviation is 20, and the standard deviation of the ADS-B observation noise is 10. The trajectories of the two aircraft in space are shown in Figure 1. Here, the track in Figure 1 is that the aircraft gradually climbs up from 300m, and then cruises at a fixed altitude. Because the XY coordinates are in degrees of longitude and latitude, respectively, the change in the height of 4000m is very small relative to the XY axis, so in fact, the ascending section is a slope. Wire.

The following are the statistical results obtained from 200 experiments, combined with Figure 2 to Figure 4 for analysis, the mean square error of the fusion system is smaller than the mean square error of the TCAS and ADS-B sub-systems, that is, the track information after fusion is better than that of the sub-systems. The information obtained by the system performing local Kalman filtering.

As shown in Figure 5, it is the CPA curve obtained by adding the local Kalman filter and the fused track to the TCAS core calculation model to calculate the CPA value.

As shown in Figure 6, it is the flow chart of the TCAS algorithm. The flight path information is generated by the flight solution model, the core processing program of the fusion system is used to receive data, and after the transformation of the geodetic coordinate system and the geocentric coordinate system, the CPA algorithm is entered to calculate the local machine. , The relative position of the other machine, estimate the time of encounter, and enter the RA decision after completing the conflict pre-judgment.

In this embodiment, 200 independent repeated experiments are carried out, and the number of early alarms and delayed alarms of each system in the TA (CPA is in 35-45s) and RA (CPA<35s) alarm periods is counted. False alarm statistics are that the actual system alarm time is ahead of the theoretical alarm time and exceeds the threshold (1s), and the actual system alarm of missing alarms lags behind the theoretical alarm time and exceeds the threshold (1s). As shown in Figure 7 and Table 1, qualitative and quantitative analysis can be performed, and it is concluded that the fusion system can reduce the number of false alarms and missed alarms in both the TA alarm and RA alarm intervals. Missing alarms and delayed alarms compress the evasion reaction time of the system and the pilot, which seriously affects flight safety. Therefore, a more accurate alarm time can improve system safety and bring positive benefits.

TCAS ADS-B fusion system False alarm (TA time)/time 1089 534 380 Missing alarm (when TA)/time 877 504 365 False alarm (when RA)/time 1036 329 170 Missing alarm (at RA)/time 999 379 193

The sub-scheme shown in Figure 8 can improve the environmental monitoring capability of remote driving for different monitoring scenarios in different flight stages, such as five routine flight process, surface taxiing process, approach flight process, ocean area flight process, and flight interval maintenance. process, and can also be subdivided into different monitoring scenarios.

Next, the present invention will be further described with respect to a typical flight stage and scenario. This part selects the situation in which TCAS generates an RA alarm.

The TCAS core processing system evaluates the airspace situation and draws an RA warning decision. The intrusion aircraft climbs at a climb rate of 1500 feet per minute to perform maneuver avoidance, simulating the dynamic response of the human in the loop, including the response delay of the pilot's actual operation, and the response delay of the mechanical and electrical system. The delay is assumed to be a constant.

By accumulating the deviation data in the RA decision-making maneuver interval, it can be concluded from Fig. 10 that the fusion system can improve the accumulated deviation of the CPA, ensure a more accurate response of the pilot during maneuver avoidance, and ensure flight safety.

If the ADS-B information in the fusion system is lost, the local filtering track of the ADS-B itself is chaotic, and if the fusion system does not take measures, unpredictable faults that may not exist in the original fault database will occur. By calculating the sum of the squares of the local rate of change and detecting this data distortion through the data information of the current moment and the previous moments, adaptively adjusting the fusion weights, the failure of a single system is diluted, although the accuracy is lower than that before the failure, and the degradation is degraded. For the accuracy of TCAS, false alarms and missed alarms, it can still ensure the stable operation of the system and bring integration benefits.

The latitude information of ADS-B is injected into steps of different amplitudes, and the system responses obtained from slope faults at different speeds are analyzed. The statistical results of false alarms and missed alarms of TCAS and the fusion system are analyzed, and it is proved that the fusion system can guarantee the above failure modes. The system operates stably and is superior to the security of a single TCAS system.

As shown in Figure 11 and Figure 12, the CPA statistics and the false alarm and missed alarm statistics of each system are respectively shown when the amplitude of the injected step fault is 100m.

As shown in Figure 13 and Figure 14, the CPA statistics chart and the statistics chart of false alarms and missed alarms of each system when the injection ramp fault is accumulated at 100m are respectively shown.

The above-mentioned specific implementation can be partially adjusted by those skilled in the art in different ways without departing from the principle and purpose of the present invention. The protection scope of the present invention is based on the claims and is not limited by the above-mentioned specific implementation. Each implementation within the scope is bound by the present invention.

Claims (8)

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)^{- 1}e^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.

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