Low-altitude unmanned aerial vehicle trajectory tracking method and system based on multi-source data fusion

Abstract

The invention discloses a multi-source data fusion low-altitude unmanned aerial vehicle trajectory tracking method and system. The method comprises the steps of flight path data collection, wherein the flight path data collection is divided into cooperative flight path data collection and non-cooperative data collection; flight path data and flight path fusion: performing cooperative flight path data and cooperative flight path fusion processing and radar flight path data and radar flight path fusion processing; the flight path data and flight path fusion comprises data preprocessing, flight path data filtering updating and flight path data and flight path association; the cooperation track and radar track fusion is used for carrying out association fusion on the cooperation track and a radar mark track, and comprises a track filtering sub-process and track-to-track association; the flight path management is used for maintaining and managing all current flight paths, including temporary flight path maintenance and management and stable flight path maintenance and management, the temporary flight path is mainly a radar temporary flight path, and the stable flight path comprises a stable cooperative target flight path and a radar stable flight path. Fusion tracking of the flight path of the unmanned aerial vehicle detected by various heterogeneous sensing devices can be realized.

Description

Technical Field

[0001] The present invention belongs to the field of low-altitude economy and unmanned aerial vehicle application, and in particular relates to a low-altitude unmanned aerial vehicle trajectory tracking method and system with multi-source data fusion. Background Art

[0002] The low-altitude economy is regarded as an important engine for future economic development. With the opening of low-altitude airspace and the expansion of drone applications, new challenges have been brought to national security, social security and personal privacy protection. To develop the low-altitude economy represented by drones, we must also take into account national and social security and personal privacy protection. Drone supervision technology is the key to solving the above problems. How to identify cooperative drones and non-cooperative drones and track and predict their flight paths in real time is the core technology for drone supervision.

[0003] At present, the means of detecting and tracking drones are mainly through ADS-B identification technology, radar detection and photoelectric detection. However, due to the limitations of technical principles and working conditions, a single detection technology cannot detect and track drones in an all-round and full-process manner. Using multiple detection devices to detect and track drones is a reliable solution, but when using multiple devices for detection, due to different data sources (such as radar, ADS-B, collaborative drone platform report data, etc.), and each data source may also be mixed with interference data, it often causes multiple data sources to appear for the same target. Summary of the invention

[0004] In order to solve the technical problems existing in the background technology, the present invention aims to provide a low-altitude UAV trajectory tracking method and system with multi-source data fusion, which comprehensively organizes each single data and outputs unified, more complete and comprehensive data to achieve all-round and full-process monitoring of the UAV in the target airspace.

[0005] In order to solve the technical problem, the technical solution of the present invention is:

[0006] A multi-source data fusion low-altitude UAV trajectory tracking system, the system comprising: a track data acquisition module, a track data and track fusion module, a collaborative track and radar track fusion module and a track management module;

[0007] The track data acquisition module includes: a collaborative track data acquisition unit and a non-collaborative data acquisition unit; the collaborative track data includes: ADS-B track data and UAV platform track data, and the non-collaborative track data includes: radar monitoring track data;

[0008] The track data and track fusion module includes a collaborative track data and collaborative track fusion unit, and a radar track data and radar track fusion unit; both are used for data preprocessing, track data filtering and updating, and track data and track association;

[0009] The cooperative track and radar track fusion module is used to associate and fuse the cooperative track with the radar beacon track, including a track filtering sub-process and track-to-track association;

[0010] The track management module is used to maintain and manage all current tracks, including temporary tracks and stable tracks. The temporary tracks include radar temporary tracks, and the stable tracks include stable cooperative target tracks and radar stable tracks.

[0011] Furthermore, the track data includes: altitude, longitude, latitude, heading angle, speed, collection time, target batch number and target identification information.

[0012] Furthermore, the collaborative track and radar track fusion module uses the nearest neighbor association criterion to determine whether two tracks are associated. If there is a radar track and there are N consecutive points, N ≥ 3 and N is configurable, all associated with the same collaborative track, then the two tracks are considered to be associated.

[0013] Furthermore, two modules, namely, track data and track fusion and cooperative track and radar track fusion, jointly constitute two-level fusion; in the cooperative track data and track fusion unit, the cooperative track data and the cooperative track process are associated and fused, and in the radar track data and radar track fusion unit, the radar data and the radar track are associated and fused; in the cooperative track and radar track fusion module, the cooperative track and the radar track are associated and fused to form a multi-source unified target track monitoring;

[0014] Among them, the track data and track fusion processing flow: pre-process the track data, then perform track filtering and updating, and then perform track data and track association processing;

[0015] The track data preprocessing includes: data analysis, which is to analyze and convert the data from ADS-B equipment, drone cloud platform and radar from different detection equipment or systems into a unified data format according to the data protocol; data time-space conversion, including time alignment processing and track data coordinate system conversion, and the unified track data coordinate system is a rectangular coordinate system with a set reference position as the origin; data cleaning is to filter out invalid data and interference data, including judging whether data items are missing, judging whether they exceed the equipment monitoring airspace range, judging time differences and processing duplicate data.

[0016] Further, when the collaborative track data is associated with the collaborative track, the target identifier of the collaborative data is used to associate with the collaborative track. If the association is successful, the collaborative target track is updated; if the association is not successful, a new collaborative target track starting point is generated; when the radar track data is associated with the radar track, the radar track is associated with the target batch number. If the association is successful, the radar target track is updated; if the association is not successful, it is associated with the radar target temporary track;

[0017] The association between the track data and the track is performed in a rectangular coordinate system centered on the reference position point;

[0018] The track filtering update calculates the speed and coordinates in the three directions of x, y, and z through a filtering algorithm, and predicts the heading angle and flight speed based on the filtered x, y, and z data, and then updates them to the corresponding track.

[0019] Further, in the fusion of the collaborative track and the radar track, the collaborative target track has more than N consecutive points associated with the radar stabilized target track, the collaborative target track is associated with the radar stabilized target track, and the collaborative track data is associated with the radar stabilized target track.

[0020] Furthermore, in the three processes of collaborative track data and track fusion, radar data and track fusion, and collaborative track and radar track fusion, α-β filter waves are used to perform track filtering updates. When the α-β filter updates the track, the track x and y axes are first filtered, and then the z axis is filtered.

[0021] A low-altitude UAV trajectory tracking method based on multi-source data fusion, characterized in that the method is applied to any of the above-mentioned systems, and the method includes:

[0022] S1: Obtain track monitoring data and determine the type of track data. If it is radar track data, jump to step S2; if it is collaborative track data, jump to step S4;

[0023] S2: First, filter the radar track data, and then determine whether it is associated with a radar stable track. If not, jump to step S3. If so, further determine whether it is a cooperative target track. If so, merge and update the track with the cooperative target track to obtain the cooperative target track. If not, obtain the radar stable track.

[0024] S3: Determine whether it is associated with the radar temporary track. If so, the temporary track is converted to a stable track to obtain the radar stable track. If not, obtain the starting point of the new track to obtain the radar temporary track.

[0025] S4: first perform collaborative track filtering on the collaborative track data, and then determine whether it is associated with the collaborative track. If not, generate a new collaborative target starting point to obtain the collaborative target track. If so, determine whether there is an associated radar stable track. If not, obtain the collaborative target track. If so, jump to step S5;

[0026] S5: Mark the radar track as the track of the cooperative target, and then determine whether the cooperative track data is an outlier point. If so, delete the outlier point data to obtain the cooperative target track. If not, directly obtain the cooperative target track.

[0027] Compared with the prior art, the advantages of the present invention are:

[0028] 1. Two-level fusion processing method for track data and track fusion technology: hierarchical fusion reduces the complexity of fusion processing and improves the compatibility and scalability of the fusion system, and has fast fusion processing speed and high accuracy of fusion results;

[0029] 2. Application of distributed fusion processing technology: Distributed fusion processing method is adopted in the process of data collection and track data and track fusion. Collaborative track data and track fusion, radar track data and radar fusion are processed separately. Finally, collaborative track and radar track fusion are performed, which is conducive to distinguishing collaborative targets from non-collaborative targets.

[0030] 3. Flexible use of adaptive α-β filtering algorithm multiple times: α-β filtering does not depend on the specific model of the system, has good stability and fast calculation speed. Using α-β filtering algorithm three times can not only process dynamic data and track complex targets, but also ensure the speed and performance of the system;

[0031] 4. Intelligent track maintenance and management technology: During the flight of a drone, the device may lose a certain section of track data, or the drone may land. In the case of data loss, the data of the last point in the track can be combined with AI reasoning to calculate the approximate location after the loss period.

[0032] Compared with the prior art, the method for tracking drone tracks based on multi-source data fusion provided by the present invention can realize efficient fusion of multi-source heterogeneous detection equipment data, accurately track and predict drone tracks, and accurately distinguish cooperative drone tracks from non-cooperative drone tracks. It can effectively realize effective supervision of drones and prediction of their flight risks, which is conducive to promoting the rapid development of the low-altitude economy under the premise of ensuring national and social security and personal privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A diagram showing a method for tracking a UAV track based on multi-source data fusion provided by an embodiment of the present invention is shown;

[0034] Figure 2 A specific method processing flow chart provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0035] The specific implementation mode of the present invention is described below in conjunction with embodiments:

[0036] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification so that people familiar with this technology can understand and read them, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0037] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0038] Embodiment 1:

[0039] like Figure 1 As shown in the figure, a UAV track tracking method based on multi-source data fusion is mainly based on the use of secondary data fusion processing and combined with α-β filtering algorithm. The whole processing flow includes four sub-processes: track data collection, track data and track fusion, collaborative track and radar track fusion and track management. It can fuse multi-source heterogeneous track data from ADS-B system, UAV platform and radar detection, so as to realize UAV track tracking and prediction.

[0040] The track data collection includes collaborative track data collection and non-collaborative data collection, wherein the collaborative track data mainly includes ADS-B track data and UAV platform track data, and the non-collaborative track data is track data monitored by radar; the track data includes: altitude, longitude, latitude, heading angle, speed, collection time, target batch number (radar track data) and target identification (collaborative track data) information.

[0041] The track data and track fusion include collaborative track data and track fusion processing, radar track data and radar track fusion processing; the collaborative track data and track fusion processing is mainly used for collaborative track data and collaborative track association processing, including data preprocessing, track filtering update, data and track fusion; the radar track data and radar track fusion processing is mainly used for radar track data and radar track association processing, including data preprocessing, track filtering update, data and track fusion.

[0042] The collaborative track and radar track fusion processing adopts the nearest neighbor association criterion to determine whether the two tracks are associated. If there is a radar track and there are N consecutive points (N≥3 and N is configurable) associated with the same collaborative track, the two tracks are considered to be associated.

[0043] The track management is used to maintain and manage all tracks of the system, including temporary tracks and stable tracks. Temporary tracks include temporary cooperative tracks and temporary radar tracks, and stable tracks include stable cooperative tracks and stable radar tracks.

[0044] Furthermore, a two-level fusion processing method is used to fuse the track data with the track;

[0045] Firstly, the collaborative track data and the collaborative track process are associated and fused in the collaborative track data and track fusion processing process, and the radar data and the radar track are associated and fused in the radar track data and track processing process; then, the collaborative track and the radar track are associated and fused in the collaborative track and radar track fusion processing process to form a unified target multi-source surveillance track.

[0046] Furthermore, the track data and track fusion processing flow is to first perform data budget processing on the track data, then perform track filtering and updating on the track, and then perform track data and track association processing.

[0047] The collaborative track data preprocessing includes ADS-B, drone cloud platform and radar multi-source heterogeneous data protocol analysis, data filtering and cleaning, and data space-time conversion to unify the data format and space-time background; the data space-time conversion is to realize the space-time of the position coordinate data and convert the position data into a rectangular coordinate system with a reference position as the origin, and the reference position can be configured and modified in the system; the data cleaning is to filter out invalid data and interference data, including judging whether the data item is missing, judging whether it exceeds the equipment monitoring airspace range, judging the time difference and processing duplicate data; wherein: (1) data analysis, The data from different detection equipment or systems (such as ADS-B equipment, drone cloud platform and radar) are parsed and converted into a unified data format (Protobuf format is used for implementation) according to their data protocols. Multi-source heterogeneous data protocol; (2) Data time and space conversion, including time alignment processing and track data coordinate system conversion. After unification, the track data coordinate system is a rectangular coordinate system with the set reference position as the origin; (3) Data cleaning is to filter out invalid data and interference data, including judging whether the data item is missing, judging whether it exceeds the equipment monitoring airspace range, judging the time difference and processing duplicate data.

[0048] Furthermore, when the collaborative track data is associated with the collaborative track, the target identifier of the collaborative data is used to associate with the collaborative track. If the association is successful, the collaborative target track is updated; if the association is unsuccessful, a new collaborative target track starting point is generated.

[0049] When the radar track data is associated with the radar track, the radar track is associated with the target batch number. If the radar track is associated, the radar target track is updated; if the radar track is not associated, the radar target temporary track is associated;

[0050] The reported track data is associated with the track in a rectangular coordinate system centered on the reference position point (also x, y, z coordinates).

[0051] The track filtering update is to calculate the speed and coordinates in the three directions of x, y, and z through a filtering algorithm, and predict the heading angle, flight speed, etc. based on the filtered x, y, and z data, and then update them to the corresponding track.

[0052] Furthermore, the collaborative track is fused with the radar track, and the nearest neighbor association criterion is used to determine whether the two tracks are associated. The collaborative target track has more than N consecutive points associated with the radar stabilized target track, the collaborative target track is associated with the radar stabilized target track, and the collaborative track data is associated with the radar stabilized target track.

[0053] Furthermore, the UAV track tracking method adopts multiple α-β filtering algorithms to perform track filtering updates. α-β filters are used to update track filtering in the three processes of collaborative track data and track fusion, radar data and radar track fusion, and collaborative track and radar track fusion. When the α-β filter updates the track, the track x and y axes are first filtered and then the z axis is filtered.

[0054] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0058] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

[0059] Many other changes and modifications may be made without departing from the concept and scope of the present invention.It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A low-altitude UAV trajectory tracking system with multi-source data fusion, characterized in that: The system comprises: a track data acquisition module, a track data and track fusion module, a collaborative track and radar track fusion module and a track management module; The track data acquisition module includes: a collaborative track data acquisition unit and a non-collaborative data acquisition unit; the collaborative track data includes: ADS-B track data and UAV platform track data, and the non-collaborative track data includes: radar monitoring track data; The track data and track fusion module includes a collaborative track data and collaborative track fusion unit, and a radar track data and radar track fusion unit; both are used for data preprocessing, track data filtering and updating, and track data and track association; The cooperative track and radar track fusion module is used to associate and fuse the cooperative track with the radar beacon track, including a track filtering sub-process and track-to-track association; The track management module is used to maintain and manage all current tracks, including temporary tracks and stable tracks. The temporary tracks include radar temporary tracks, and the stable tracks include stable cooperative target tracks and radar stable tracks.

2. According to the multi-source data fusion low-altitude UAV trajectory tracking system of claim 1, it is characterized in that: The track data includes: altitude, longitude, latitude, heading angle, speed, acquisition time, target batch number and target identification information.

3. The low-altitude UAV trajectory tracking system with multi-source data fusion according to claim 1 is characterized in that: The cooperative track and radar track fusion module adopts the nearest neighbor association criterion to determine whether two tracks are associated. If there is a radar track and there are N consecutive points, N≥3 and N is configurable, all associated with the same cooperative track, then the two tracks are considered to be associated.

4. The low-altitude UAV trajectory tracking system with multi-source data fusion according to claim 1 is characterized in that: The two-level fusion is composed of two modules: track data and track fusion, and collaborative track and radar track fusion. In the collaborative track data and track fusion unit, the collaborative track data and collaborative track process are associated and fused. In the radar track data and radar track fusion unit, the radar data and radar track are associated and fused. In the collaborative track and radar track fusion module, the collaborative track and radar track are associated and fused to form a multi-source unified target track monitoring. Among them, the track data and track fusion processing flow: pre-process the track data, then perform track filtering and updating, and then perform track data and track association processing; The track data preprocessing includes: data analysis, which is to analyze and convert the data from ADS-B equipment, drone cloud platform and radar from different detection equipment or systems into a unified data format according to the data protocol; data time-space conversion, including time alignment processing and track data coordinate system conversion, and the unified track data coordinate system is a rectangular coordinate system with a set reference position as the origin; data cleaning is to filter out invalid data and interference data, including judging whether data items are missing, judging whether they exceed the equipment monitoring airspace range, judging time differences and processing duplicate data.

5. The multi-source data fusion low-altitude UAV trajectory tracking system according to claim 4 is characterized in that: When the collaborative track data is associated with the collaborative track, the target identifier of the collaborative data is used to associate with the collaborative track. If the association is successful, the collaborative target track is updated; if the association is unsuccessful, a new collaborative target track starting point is generated; when the radar track data is associated with the radar track, the radar track is associated with the target batch number. If the association is successful, the radar target track is updated; if the association is unsuccessful, the radar target temporary track is associated; The association between the track data and the track is performed in a rectangular coordinate system centered on the reference position point; The track filtering update calculates the speed and coordinates in the three directions of x, y, and z through a filtering algorithm, and predicts the heading angle and flight speed based on the filtered x, y, and z data, and then updates them to the corresponding track.

6. The multi-source data fusion low-altitude UAV trajectory tracking system according to claim is characterized in that: In the fusion of the cooperative track and the radar track, the cooperative target track has more than N consecutive points associated with the radar stabilized target track, the cooperative target track is associated with the radar stabilized target track, and the cooperative track data is associated with the radar stabilized target track.

7. The low-altitude UAV trajectory tracking system with multi-source data fusion according to claim 1 is characterized in that: In the three processes of collaborative track data and track fusion, radar data and track fusion, and collaborative track and radar track fusion, α-β filter waves are used to perform track filtering updates. When the α-β filter updates the track, the track x and y axes are first filtered, and then the z axis is filtered.

8. A low-altitude UAV trajectory tracking method based on multi-source data fusion, characterized in that: The method is applied to the system described in any one of claims 1 to 7, and the method comprises: S1: Obtain track monitoring data and determine the type of track data. If it is radar track data, jump to step S2; if it is collaborative track data, jump to step S4; S2: First, filter the radar track data, and then determine whether it is associated with a radar stable track. If not, jump to step S3. If so, further determine whether it is a cooperative target track. If so, merge and update the track with the cooperative target track to obtain the cooperative target track. If not, obtain the radar stable track. S3: Determine whether it is associated with the radar temporary track. If so, the temporary track is converted to a stable track to obtain the radar stable track. If not, obtain the starting point of the new track to obtain the radar temporary track. S4: first perform collaborative track filtering on the collaborative track data, and then determine whether it is associated with the collaborative track. If not, generate a new collaborative target starting point to obtain the collaborative target track. If so, determine whether there is an associated radar stable track. If not, obtain the collaborative target track. If so, jump to step S5; S5: Mark the radar track as the track of the cooperative target, and then determine whether the cooperative track data is an outlier point. If so, delete the outlier point data to obtain the cooperative target track. If not, directly obtain the cooperative target track.

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