A track fusion method for UAV DAA

Abstract

The present invention provides a track fusion method for unmanned aerial vehicle DAA, comprising: S10, receiving sensor data from various sensors; S20, establishing a track for each target of each sensor according to the received sensor data; S30, identifying and linking the tracks of the same target from different sensors; S40, finding the best track in the linked tracks; S50, performing track extrapolation according to the best track; S60, sending the final track to the unmanned aerial vehicle DAA device after completing the track extrapolation. The present invention fully considers and gives play to the advantages of ATAR, AST, and ADS-B sensors to perform track fusion, and makes full use of the information of cooperative sensors.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) DAA, and in particular to a track fusion method for unmanned aerial vehicle (UAV) DAA. Background Art

[0002] In recent years, unmanned aircraft systems (UAS) have been rapidly developed and widely used. In the civilian field, UAS are widely used in remote sensing mapping, geological survey, disaster relief, etc.

[0003] The UAV industry is developing rapidly and the number of drones is increasing dramatically. Due to the lack of necessary detection and avoidance (DAA) equipment, drones themselves and other aircraft in the airspace have posed a great threat. In response to the above problems, domestic and foreign research institutions have conducted a lot of research and testing. At present, the RTCA organization has released the DO-365 standard. According to this standard, the avoidance processing process of the drone DAA system is as follows:

[0004] a) Target detection: Detect aircraft in the airspace (referred to as “intruders”) through air-to-air radar (ATAR), active surveillance (AST), and automatic dependent surveillance-B (ADS-B) sensors;

[0005] b) Track fusion: The ATAR, AST, and ADS-B sensor data are fused through the track fusion algorithm to generate a reliable global track;

[0006] c) Warning and guidance processing: Calculate warnings based on the global track and provide guidance information when necessary.

[0007] Track fusion requires the use of appropriate algorithms based on the characteristics of the sensor and its own application scenarios to achieve better results with the least amount of calculation.

[0008] There is no mature track fusion algorithm for ATAR, AST, and ADS-B sensors. The current track fusion algorithm has the following problems:

[0009] a) The sensors that track fusion relies on are mostly primary radars, and most of them are homogeneous sensors. The application scenario is mainly ground surveillance.

[0010] b) Collaborative sensors (AST, ADS-B) are rarely involved in track fusion, and the information of cooperative sensors is not fully utilized. Summary of the invention

[0011] The present invention aims to provide a track fusion method for UAV DAA to solve the problems existing in track fusion during the avoidance processing of the above-mentioned UAV DAA system.

[0012] The present invention provides a track fusion method for unmanned aerial vehicle DAA, comprising the following steps:

[0013] S10, receiving sensor data from various sensors;

[0014] S20, establishing a track for each target of each sensor according to the received sensor data;

[0015] S30, identifying and linking tracks of the same target from different sensors;

[0016] S40, finding the best track among the linked tracks;

[0017] S50, performing track extrapolation based on the best track;

[0018] S60, after completing the track extrapolation, the final track is sent to the UAV DAA device.

[0019] Furthermore, the various sensors in step S10 include:

[0020] Air-to-air radar ATAR;

[0021] Active surveillance system AST;

[0022] and, the Automatic Dependent Surveillance System - ADS-B.

[0023] Further, in step S20, the method of establishing a track for each target of the received air-to-air radar ATAR data includes:

[0024] S211, calculate the current target position based on the aircraft data and air-to-air radar ATAR data;

[0025] S212, the track database stores the initialized track and the established track, and detects whether the track extrapolated position in the track database matches the current target position:

[0026] (1) If there is no track extrapolation position in the track database that matches the current target position, the track is initialized based on the air-to-air radar ATAR data;

[0027] (2) If the extrapolated position of the initialization track in the track database matches the current target position, the track is established based on the initialization track;

[0028] (3) If the extrapolated position of an established track in the track database matches the current target position, the established track is updated by performing Kalman filtering on the air-to-air radar ATAR data.

[0029] Further, the air-to-air radar ATAR data is represented by slant range, azimuth, and pitch angle, which are recorded as (ρ, β, ε), and the method for updating the established track by performing Kalman filtering on the air-to-air radar ATAR data is:

[0030] (a) Extrapolate the position based on the k-th track Predict the k+1th track extrapolated position prediction value through ENU prediction logic

[0031] (b) Extrapolate the predicted position of the k+1th track Perform coordinate conversion from ENU coordinates to ρβε coordinates and obtain

[0032] (c) Calculate the slant range, azimuth, pitch angle and The residual of

[0033] (d) performing coordinate transformation from ρβε coordinate to ENU coordinate on the residual and gain k(k+1);

[0034] (e) Use the k+1th track extrapolation position prediction value As well as the residual error and gain k(k+1) after coordinate transformation, calculate the k+1th track extrapolation position

[0035] (f) Extrapolate the k+1th track position Combine the longitude, latitude and altitude in the local data to calculate the absolute longitude, latitude and altitude of the current target.

[0036] Furthermore, in step S20, the method of establishing a track for each target of the received active surveillance system AST data includes:

[0037] S221, calculating the current target position according to the aircraft data and the active surveillance system AST data;

[0038] S222, the track database stores the initialized track and the established track, and detects whether the track extrapolated position in the track database matches the current target position:

[0039] (1) If there is no track extrapolated position in the track database that matches the current target position, the track is initialized based on the active surveillance system AST data;

[0040] (2) If the extrapolated position of the initialization track in the track database matches the current target position, the track is established based on the initialization track;

[0041] (3) If the extrapolated position of an established track in the track database matches the current target position, the established track is updated by performing Kalman filtering on the active surveillance system AST data.

[0042] Further, the active surveillance system AST data is represented by slant range, azimuth, and altitude report values, recorded as (ρ, β, H), and the method for updating the established track by performing Kalman filtering on the active surveillance system AST data is:

[0043] (a) Extrapolate the position based on the k-th track Predict the k+1th track extrapolated position prediction value through ENU prediction logic

[0044] (b) Extrapolate the predicted position of the k+1th track Perform coordinate conversion from ENU coordinates to slant range azimuth coordinates;

[0045] (c) Calculate the residual of the k+1th track extrapolated position prediction value after the slant range and azimuth in the active surveillance system AST data and the coordinate conversion;

[0046] (d) performing coordinate conversion from slant range azimuth coordinates to ENU coordinates on the residual 1 and gain k(k+1);

[0047] (e) Use the k+1th track extrapolation position prediction value As well as the residual one and gain k(k+1) after coordinate transformation, calculate the EN value in the k+1th track extrapolation position;

[0048] (f) Calculate the residual difference between the altitude report value in the active surveillance system AST data and the k+1th track extrapolated position prediction value;

[0049] (g) Use the k+1th track extrapolation position prediction value As well as residual 2 and gain k(k+1), calculate the altitude value in the k+1th track extrapolation position;

[0050] (h) The k+1th track extrapolation position is obtained from the EN value and altitude value in the k+1th track extrapolation position. Extrapolate the k+1th track position Combine the longitude, latitude and altitude in the local data to calculate the absolute longitude, latitude and altitude of the current target.

[0051] Furthermore, let [e s n s u s]′ is the extrapolated position of the local track in the ENU coordinate system, [enu]′ is the extrapolated position of the global track in the ENU coordinate system; [ρ s β s ε s ]′ is the extrapolated position of the local track in the spherical coordinate system; [ρ β ε]′ is the extrapolated position of the global track in the spherical coordinate system; wherein the local track refers to the track before linking, and the global track refers to the track after linking; then step S30 includes the following sub-steps:

[0052] S31, read in a local track i;

[0053] S32, read in a global track j;

[0054] S33, calculate the parameters Δs, Δz, Δρ, Δβ, Δε of the track connection:

[0055] (1) The lateral position difference between the local track i and the global track j: in:

[0056] Δx=|ee s |;

[0057] Δy=|nn s |;

[0058] (2) The vertical position difference between the local track i and the global track j: Δz = |uu s |;

[0059] (3) Slant range difference between local track i and global track j: Δρ = |ρ-ρ s |;

[0060] (4) The azimuth difference between the local track i and the global track j: Δβ = |β-β s |;

[0061] (5) The pitch angle difference between the local track i and the global track j: Δε = |ε-ε s |;

[0062] S34, judging whether the local track i and the global track j meet the connection condition according to the track connection parameters calculated in step S33, if yes, proceed to S35, otherwise terminate;

[0063] S35, determining whether the ICAO addresses of the local track i and the global track j match, if they match, directly linking the local track i and the global track j, otherwise proceeding to step S36;

[0064] S36, determining whether the secondary response A code of the local track i matches the global track i, if so, directly linking the local track i with the global track j, otherwise proceeding to step S37;

[0065] S37, repeat S32 to S36, and select the global track with the smallest Δs from the global tracks that have not yet been linked to link.

[0066] Furthermore, the method for finding the best track in the linked tracks in step S40 is:

[0067] S41, find the best sensor in horizontal and vertical directions:

[0068] (1) For the best sensor in the vertical direction:

[0069] First determine whether it is a cooperative sensor: cooperative sensors take precedence over non-cooperative sensors; among them, cooperative sensors refer to active surveillance systems AST and automatic dependent surveillance systems ADS-B; non-cooperative sensors refer to air-to-air radars ATAR;

[0070] If they are all cooperative sensors, then the vertical rate uncertainty of the cooperative sensors is determined, and the sensor with the smallest vertical rate uncertainty is the optimal sensor;

[0071] (2) For the optimal sensor in the horizontal direction, calculate the horizontal position uncertainty of each sensor Select horizontal position uncertainty The smallest sensor is the best sensor;

[0072]

[0073]

[0074]

[0075] Among them, σ east is the position uncertainty of the sensor in the east direction; σ v,east is the velocity uncertainty of the sensor in the east direction; σ north is the north position uncertainty of the sensor; σ north is the uncertainty of the sensor's north velocity; t is the track calculation interval;

[0076] S42, the track generated by the best sensor is the best track.

[0077] Preferably, active surveillance system AST data and air-to-air radar ATAR data are needed to verify the validity of automatic dependent surveillance system ADS-B data.

[0078] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0079] 1. The present invention fully considers and gives full play to the advantages of ATAR, AST and ADS-B sensors to carry out track fusion, and makes full use of the information of cooperative sensors.

[0080] 2. The present invention can unify the track data of the three sensors, ATAR, AST and ADS-B, into the longitude, latitude and altitude mode, which is convenient for subsequent track fusion processing.

[0081] 3. To solve the problem of poor angle measurement performance of the active surveillance system AST, the present invention adopts a combination of horizontal and vertical Kalman filtering to improve track filtering performance.

[0082] 4. When linking tracks, the present invention makes full use of the ICAO address and secondary A code provided by AST and ADS-B to improve the efficiency and quality of track linking.

[0083] 5. The present invention introduces the best sensor, and only uses the best sensor to update the global track within a certain period of time, thereby reducing the amount of calculation.

[0084] 6. The present invention uses ATAR and AST data to verify ADS-B data, which can effectively filter out false targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0086] Figure 1 The figure is a flow chart of a track fusion method for UAV DAA according to an embodiment of the present invention.

[0087] Figure 2 A flow chart of establishing a track using air-to-air radar ATAR data according to an embodiment of the present invention.

[0088] Figure 3 A flow chart of Kalman filtering in establishing a track using air-to-air radar ATAR data according to an embodiment of the present invention.

[0089] Figure 4 A flow chart of Kalman filtering in-track for establishing the active monitoring system AST data in an embodiment of the present invention.

[0090] Figure 5 The flowchart of the track linking of an embodiment of the present invention. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0092] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0093] Example

[0094] like Figure 1 As shown, this embodiment proposes a track fusion method for UAV DAA, including the following steps:

[0095] S10, receiving sensor data from various sensors; wherein the various sensors include:

[0096] Air-to-air radar ATAR;

[0097] Active surveillance system AST;

[0098] and, the Automatic Dependent Surveillance System - ADS-B.

[0099] S20, establishing a track for each target of each sensor according to the received sensor data;

[0100] 1. Air-to-Air Radar (ATAR)

[0101] like Figure 2 As shown, the method of establishing a track for each target of the received air-to-air radar ATAR data includes:

[0102] S211, calculate the current target position based on the aircraft data and air-to-air radar ATAR data;

[0103] S212, the track database stores the initialized track and the established track, and detects whether the track extrapolated position in the track database matches the current target position:

[0104] (1) If there is no track extrapolation position in the track database that matches the current target position, the track is initialized based on the air-to-air radar ATAR data;

[0105] (2) If the extrapolated position of the initialization track in the track database matches the current target position, the track is established based on the initialization track;

[0106] (3) If the extrapolated position of the established track in the track database matches the current target position, the established track is updated by performing Kalman filtering on the air-to-air radar ATAR data. The air-to-air radar ATAR data is represented by slant range, azimuth, and pitch angle, denoted as (ρ, β, ε), such as Figure 3 As shown, the method for updating the established track by performing Kalman filtering on air-to-air radar ATAR data is:

[0107] (a) Extrapolate the position based on the k-th track Predict the k+1th track extrapolated position prediction value through ENU prediction logic

[0108] (b) Extrapolate the predicted position of the k+1th track Perform coordinate conversion from ENU coordinates to ρβε coordinates and obtain

[0109] (c) Calculate the slant range, azimuth, pitch angle and The residual of

[0110] (d) performing coordinate transformation from ρβε coordinate to ENU coordinate on the residual and gain k(k+1);

[0111] (e) Use the k+1th track extrapolation position prediction value As well as the residual error and gain k(k+1) after coordinate transformation, calculate the k+1th track extrapolation position

[0112] (f) Extrapolate the k+1th track position Combine the longitude, latitude and altitude in the aircraft data to calculate the absolute longitude, latitude and altitude (Φ, λ, h) of the current target.

[0113] 2. Active Monitoring System (AST)

[0114] Similar to air-to-air radar ATAR, you can refer to Figure 2 , a method for establishing a track for each target of the received active surveillance system AST data includes:

[0115] S221, calculating the current target position according to the aircraft data and the active surveillance system AST data;

[0116] S222, the track database stores the initialized track and the established track, and detects whether the track extrapolated position in the track database matches the current target position:

[0117] (1) If there is no track extrapolated position in the track database that matches the current target position, the track is initialized based on the active surveillance system AST data;

[0118] (2) If the extrapolated position of the initialization track in the track database matches the current target position, the track is established based on the initialization track;

[0119] (3) If the extrapolated position of the established track in the track database matches the current target position, the established track is updated by performing Kalman filtering on the active surveillance system AST data. Among them, since the target measurement characteristics of the active surveillance system AST are different from those of the air-to-air radar ATAR (the altitude report value H of the transponder is used instead of the pitch angle ε in the air-to-air radar ATAR), the Kalman filter algorithm needs to be appropriately adjusted. The extended Kalman filter is used for distance and azimuth (ρ, β), and the Kalman filter is used for altitude. Finally, the altitude and E (east) N (north) values ​​are combined and converted to output longitude, latitude, and altitude. Therefore, if Figure 4 As shown, the active surveillance system AST data is represented by slant range, azimuth, and altitude report values, recorded as (ρ, β, H), and the method for updating the established track by performing Kalman filtering on the active surveillance system AST data is:

[0120] (a) Extrapolate the position based on the k-th track Predict the k+1th track extrapolated position prediction value through ENU prediction logic

[0121] (b) Extrapolate the predicted position of the k+1th track Perform coordinate conversion from ENU coordinates to slant range azimuth coordinates;

[0122] (c) Calculate the residual of the k+1th track extrapolated position prediction value after the slant range and azimuth in the active surveillance system AST data and the coordinate conversion;

[0123] (d) performing coordinate conversion from slant range azimuth coordinates to ENU coordinates on the residual 1 and gain k(k+1);

[0124] (e) Use the k+1th track extrapolation position prediction value As well as the residual one and gain k(k+1) after coordinate transformation, calculate the EN value in the k+1th track extrapolation position;

[0125] (f) Calculate the residual difference between the altitude report value in the active surveillance system AST data and the k+1th track extrapolated position prediction value;

[0126] (g) Use the k+1th track extrapolation position prediction value As well as residual 2 and gain k(k+1), calculate the altitude value in the k+1th track extrapolation position;

[0127] (h) The k+1th track extrapolation position is obtained from the EN value and altitude value in the k+1th track extrapolation position. Extrapolate the k+1th track position Combine the longitude, latitude and altitude in the aircraft data to calculate the absolute longitude, latitude and altitude (Φ, λ, h) of the current target.

[0128] 3. Automatic Dependent Surveillance System - ADS-B

[0129] Automatic Dependent Surveillance System ADS-B data provides complete track data (such as position, speed and other motion parameters). Therefore, the Automatic Dependent Surveillance System ADS-B data can be directly used by the DDA algorithm in the drone DAA equipment.

[0130] However, due to the problem that some automatic dependent surveillance systems ADS-B cannot provide positioning accuracy. Automatic dependent surveillance system ADS-B data may be spoofed, generating false and misleading tracking information to be sent to the drone DAA equipment for alarm and guidance functions. Therefore, the drone DAA needs to perform additional verification of the automatic dependent surveillance system ADS-B data. This embodiment uses active surveillance system AST data and air-to-air radar ATAR data to verify the validity of the automatic dependent surveillance system ADS-B data. The verification requirements are as follows:

[0131] The validation requirements for using active surveillance system AST data to validate automatic dependent surveillance system ADS-B data include:

[0132] a) Slope range difference: <0.25NM;

[0133] b) Azimuth difference: <45°;

[0134] c) Altitude: <200ft.

[0135] The validation requirements for using air-to-air radar ATAR data to validate ADS-B data include: (air-to-air radar ATAR has higher azimuth measurement accuracy, but slightly worse altitude measurement performance):

[0136] a) Slope range difference: <0.25NM;

[0137] b) Azimuth difference: <15°;

[0138] c) Altitude: <500ft.

[0139] Generally, the drone DDA equipment will set a period (for example, every 10 seconds) to verify the validity of the ADS-B data.

[0140] Through step S20, the track data of the three sensors can be unified into the longitude, latitude and altitude mode to facilitate subsequent processing.

[0141] S30, identifying and linking tracks of the same target from different sensors;

[0142] Track linking is mainly to link the local tracks of the same target from different sensors (ADS-B, ATAR, AST) into a global track. Track connection depends on the following parameters:

[0143] (1) ICAO address, i.e. the 24-digit International Civil Aviation Organization address;

[0144] (2) Second response A code;

[0145] (3) Location association.

[0146] Therefore, let [e s n s u s ]′ is the extrapolated position of the local track in the ENU coordinate system, [enu]′ is the extrapolated position of the global track in the ENU coordinate system; [ρ s β s ε s ]′ is the extrapolated position of the local track in the spherical coordinate system; [ρ β ε]′ is the extrapolated position of the global track in the spherical coordinate system; where the local track refers to the track before the link, and the global track refers to the track after the link; Figure 5 As shown, step S30 includes the following sub-steps:

[0147] S31, read in a local track i;

[0148] S32, read in a global track j;

[0149] S33, calculate the parameters Δs, Δz, Δρ, Δβ, Δε of the track connection:

[0150] (1) The lateral position difference between the local track i and the global track j: in:

[0151] Δx=|ee s |;

[0152] Δy=|nn s |;

[0153] (2) The vertical position difference between the local track i and the global track j: Δz = |uu s |;

[0154] (3) Slant range difference between local track i and global track j: Δρ = |ρ-ρ s |;

[0155] (4) The azimuth difference between the local track i and the global track j: Δβ = |β-β s |;

[0156] (5) The pitch angle difference between the local track i and the global track j: Δε = |ε-ε s |;

[0157] S34, according to the track connection parameters calculated in step S33, determine whether the local track i and the global track j meet the connection condition, if yes, proceed to S35, otherwise terminate; wherein the connection condition refers to whether the parameters Δz, Δρ, Δβ, Δε are within the preset range, for example:

[0158] Δρ<0.25NM;

[0159] Δε<2.5° or Δz<250′;

[0160] Δβ<45°;

[0161] S35, determining whether the ICAO addresses of the local track i and the global track j match, if they match, directly linking the local track i and the global track j, otherwise proceeding to step S36;

[0162] S36, judging whether the secondary reply A codes of the local track i and the global track j match, if they match, directly linking the local track i and the global track j, otherwise, proceeding to step S37;

[0163] S37, repeat S32 to S36, and select the global track with the smallest Δs from the global tracks that have not yet been linked to link.

[0164] S40, finding the best track among the linked tracks;

[0165] Once the tracks are linked, the global track executes an optimal sensor selector, and the horizontal and vertical optimal sensors are estimated independently. Then the method for finding the best track in the linked tracks in step S40 is:

[0166] S41, find the best sensor in horizontal and vertical directions:

[0167] (1) For the best sensor in the vertical direction:

[0168] First determine whether it is a cooperative sensor: cooperative sensors take precedence over non-cooperative sensors; among them, cooperative sensors refer to active surveillance systems AST and automatic dependent surveillance systems ADS-B; non-cooperative sensors refer to air-to-air radars ATAR;

[0169] If they are all cooperative sensors, then the vertical rate uncertainty of the cooperative sensors is determined, and the sensor with the smallest vertical rate uncertainty is the optimal sensor;

[0170] (2) For the optimal sensor in the horizontal direction, calculate the horizontal position uncertainty of each sensor Select horizontal position uncertainty The smallest sensor is the best sensor;

[0171]

[0172]

[0173]

[0174] Among them, σ east is the position uncertainty of the sensor in the east direction; σ v,east is the velocity uncertainty of the sensor in the east direction; σ north is the north position uncertainty of the sensor; σ north is the uncertainty of the sensor's north velocity; t is the track calculation interval;

[0175] S42, the track generated by the best sensor is the best track. In order to avoid switching the best sensor all the time, the track of the backup sensor (i.e., other sensors except the best sensor) is only selected when the track has better uncertainty within a certain time (preset as needed, such as at least 2.5 seconds).

[0176] S50, performing track extrapolation according to the optimal track; that is, the horizontal speed and the vertical speed can be estimated through the optimal track, and the track extrapolation is performed according to the estimated horizontal speed and the vertical speed. The track extrapolation method is a prior art and will not be described in detail here.

[0177] S60, after completing the track extrapolation, the final track is sent to the UAV DAA device, and the UAV DAA device can perform warning calculations based on the fused final track and provide guidance information when necessary.

[0178] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A track fusion method for UAV DAA, characterized in that: The steps include: S10, receiving sensor data from various sensors; the various sensors in step S10 include: Air-to-air radar ATAR; Active Surveillance System AST; and, Automatic Dependent Surveillance-B; S20, establishing a track for each target of each sensor according to the received sensor data; S30, identifying and linking tracks of the same target from different sensors; S40, finding the best track among the linked tracks; S50, performing track extrapolation based on the best track; S60, after completing the track extrapolation, the final track is sent to the UAV DAA device; Assume [e s n s u s ]′ is the extrapolated position of the local track in the ENU coordinate system, [enu]′ is the extrapolated position of the global track in the ENU coordinate system; [ρ s β s ε s ]′ is the extrapolated position of the local track in the spherical coordinate system; [ρβε]′ is the extrapolated position of the global track in the spherical coordinate system; wherein the local track refers to the track before linking, and the global track refers to the track after linking; then step S30 includes the following sub-steps: S31, read in a local track i; S32, read in a global track j; S33, calculate the parameters Δs, Δz, Δρ, Δβ, Δε of the track connection: (1) The lateral position difference between the local track i and the global track j: in: Δx=|ee s |; Δy=|nn s |; (2) The vertical position difference between the local track i and the global track j: Δz = |uu s |; (3) Slant range difference between local track i and global track j: Δρ = |ρ-ρ s |; (4) The azimuth difference between the local track i and the global track j: Δβ = |β-β s |; (5) The pitch angle difference between the local track i and the global track j: Δε = |ε-ε s |; S34, judging whether the local track i and the global track j meet the connection condition according to the track connection parameters calculated in step S33, if yes, proceed to S35, otherwise terminate; S35, determining whether the ICAO addresses of the local track i and the global track j match, if they match, directly linking the local track i and the global track j, otherwise proceeding to step S36; S36, judging whether the secondary reply A codes of the local track i and the global track j match, if they match, directly linking the local track i and the global track j, otherwise, proceeding to step S37; S37, repeat S32 to S36, and select the global track with the smallest Δs from the global tracks that have not yet been linked to link.

2. The track fusion method for UAV DAA according to claim 1 is characterized in that: In step S20, the method of establishing a track for each target of the received air-to-air radar ATAR data includes: S211, calculate the current target position based on the aircraft data and air-to-air radar ATAR data; S212, the track database stores the initialized track and the established track, and detects whether the track extrapolated position in the track database matches the current target position: (1) If there is no track extrapolation position in the track database that matches the current target position, the track is initialized based on the air-to-air radar ATAR data; (2) If the extrapolated position of the initialization track in the track database matches the current target position, the track is established based on the initialization track; (3) If the extrapolated position of an established track in the track database matches the current target position, the established track is updated by performing Kalman filtering on the air-to-air radar ATAR data.

3. The track fusion method for UAV DAA according to claim 2 is characterized in that: The air-to-air radar ATAR data is represented by slant range, azimuth, and pitch angle, which are recorded as (ρ, β, ε). The method for updating the established track by performing Kalman filtering on the air-to-air radar ATAR data is: (a) Extrapolate the position based on the k-th track (k / k) Predict the k+1th track extrapolated position prediction value through ENU prediction logic (k+1 / k); (b) Extrapolate the predicted position of the k+1th track (k+1 / k) performs coordinate transformation from ENU coordinates to ρβε coordinates, and obtains (k+1 / k) ρβε ; (c) Calculate the slant range, azimuth, pitch angle and (k+1 / k) ρβε The residual of (d) performing coordinate transformation from ρβε coordinate to ENU coordinate on the residual and gain k(k+1); (e) Use the k+1th track extrapolation position prediction value (k+1 / k), as well as the residual error and gain k(k+1) after coordinate transformation, calculate the k+1th track extrapolation position (k+1 / k+1); (f) Extrapolate the k+1th track position (k+1 / k+1) Combine the longitude, latitude and altitude in the local data to calculate the absolute longitude, latitude and altitude of the current target.

4. The track fusion method for UAV DAA according to claim 1 is characterized in that: In step S20, the method of establishing a track for each target of the received active surveillance system AST data includes: S221, calculating the current target position according to the aircraft data and the active surveillance system AST data; S222, the track database stores the initialized track and the established track, and detects whether the track extrapolated position in the track database matches the current target position: (1) If there is no track extrapolated position in the track database that matches the current target position, the track is initialized based on the active surveillance system AST data; (2) If the extrapolated position of the initialization track in the track database matches the current target position, the track is established based on the initialization track; (3) If the extrapolated position of an established track in the track database matches the current target position, the established track is updated by performing Kalman filtering on the active surveillance system AST data.

5. The track fusion method for UAV DAA according to claim 4 is characterized in that: The active surveillance system AST data is represented by slant range, azimuth, and altitude report values, recorded as (ρ, β, H). The method for updating the established track by performing Kalman filtering on the active surveillance system AST data is: (a) Extrapolate the position based on the k-th track (k / k) Predict the k+1th track extrapolated position prediction value through ENU prediction logic (k+1 / k); (b) Extrapolate the predicted position of the k+1th track (k+1 / k) performs coordinate conversion from ENU coordinates to slant range azimuth coordinates; (c) Calculate the residual of the k+1th track extrapolated position prediction value after the slant range and azimuth in the active surveillance system AST data and the coordinate conversion; (d) performing coordinate conversion from slant range azimuth coordinates to ENU coordinates on the residual 1 and gain k(k+1); (e) Use the k+1th track extrapolation position prediction value (k+1 / k), as well as the residual after coordinate transformation and gain k(k+1), calculate the EN value in the k+1th track extrapolation position; (f) Calculate the residual difference between the altitude report value in the active surveillance system AST data and the k+1th track extrapolated position prediction value; (g) Use the k+1th track extrapolation position prediction value (k+1 / k), as well as residual 2 and gain k(k+1), calculate the altitude value in the k+1th track extrapolation position; (h) The k+1th track extrapolation position is obtained from the EN value and altitude value in the k+1th track extrapolation position. (k+1 / k+1), extrapolate the k+1th track position (k+1 / k+1) Combine the longitude, latitude and altitude in the local data to calculate the absolute longitude, latitude and altitude of the current target.

6. The track fusion method for UAV DAA according to claim 1 is characterized in that: The method for finding the best track among the linked tracks in step S40 is: S41, find the best sensor in horizontal and vertical directions: (1) For the best sensor in the vertical direction: First determine whether it is a cooperative sensor: cooperative sensors take precedence over non-cooperative sensors; among them, cooperative sensors refer to active surveillance systems AST and automatic dependent surveillance systems ADS-B; non-cooperative sensors refer to air-to-air radars ATAR; If they are all cooperative sensors, then the vertical rate uncertainty of the cooperative sensors is determined, and the sensor with the smallest vertical rate uncertainty is the optimal sensor; (2) For the optimal sensor in the horizontal direction, calculate the horizontal position uncertainty of each sensor Select horizontal position uncertainty The smallest sensor is the best sensor; Among them, σ east is the position uncertainty of the sensor in the east direction; σ v,east is the velocity uncertainty of the sensor in the east direction; σ north is the north position uncertainty of the sensor; σ north is the uncertainty of the sensor's north velocity; t is the track calculation interval; S42, the track generated by the best sensor is the best track.

7. The track fusion method for UAV DAA according to claim 1 is characterized in that: Active surveillance system AST data and air-to-air radar ATAR data are needed to verify the validity of automatic dependent surveillance system ADS-B data.

Images