CN-121995956-A - High-speed unmanned aerial vehicle tracking method and system based on Kalman filtering

CN121995956ACN 121995956 ACN121995956 ACN 121995956ACN-121995956-A

Abstract

The invention provides a high-speed unmanned aerial vehicle tracking method and system based on Kalman filtering, which relate to the technical field of target tracking, and the method comprises the steps of collecting asynchronous data flow of a high-speed unmanned aerial vehicle to be tracked; the method comprises the steps of carrying out space-time registration and target association processing on asynchronous data streams to obtain a historical position sequence of a high-speed unmanned aerial vehicle to be tracked, constructing a Kalman filter based on a current statistical model, inputting the historical position sequence into the Kalman filter, outputting a future predicted position, comparing the future predicted position with the current position of the high-speed unmanned aerial vehicle to be tracked to obtain a position error, adjusting the position error through a proportional-integral-differential control algorithm to obtain a turntable control instruction of the high-speed unmanned aerial vehicle to be tracked, and driving the high-speed unmanned aerial vehicle to be tracked to travel according to the future predicted position according to the turntable control instruction so as to realize tracking of the high-speed unmanned aerial vehicle to be tracked. The invention can reduce the condition of tracking loss and improve the stability and instantaneity of tracking.

Inventors

WANG XUHUA
WANG XUMING
REN QUN
MIAO WENJIE

Assignees

杭州雷擎电子科技发展有限公司

Dates

Publication Date: 20260508
Application Date: 20260410

Claims (10)

1. The high-speed unmanned aerial vehicle tracking method based on Kalman filtering is characterized by comprising the following steps of: s1, collecting asynchronous data flow of a high-speed unmanned aerial vehicle to be tracked; S2, performing space-time registration and target association processing on the asynchronous data stream to obtain a historical position sequence of the high-speed unmanned aerial vehicle to be tracked; S3, constructing a Kalman filter based on a current statistical model; s4, inputting the historical position sequence into the Kalman filter, and outputting a future predicted position; s5, comparing the future predicted position with the current position of the high-speed unmanned aerial vehicle to be tracked to obtain a position error; S6, adjusting the position error through a proportional-integral-derivative control algorithm to obtain a turntable control instruction of the high-speed unmanned aerial vehicle to be tracked; and S7, driving the high-speed unmanned aerial vehicle to be tracked to travel according to the future predicted position according to the turntable control instruction so as to track the high-speed unmanned aerial vehicle to be tracked.
2. The kalman filter-based high-speed unmanned aerial vehicle tracking method according to claim 1, wherein S2 specifically comprises: S201, calibrating a camera, and determining an internal reference matrix and an external reference matrix of the camera; S202, back-projecting two-dimensional coordinate information of the asynchronous data stream under a camera coordinate system into a sensor coordinate system according to the internal reference matrix and the external reference matrix to obtain a first unit direction vector of the camera under the sensor coordinate system; S203, constructing a second unit direction vector of the laser beam under the sensor coordinate system; S204, combining the first unit direction vector, the second unit direction vector and a laser ranging value, and calculating three-dimensional coordinates of the high-speed unmanned aerial vehicle to be tracked under the sensor coordinate system; and S205, performing delay compensation on the three-dimensional coordinates through a backtracking positioning mechanism to obtain the historical position sequence.
3. The kalman filter-based high-speed unmanned aerial vehicle tracking method according to claim 2, wherein S205 specifically comprises: S2051, inquiring a turntable angle corresponding to a time stamp according to the time stamp of the image data in the asynchronous data stream; S2052, judging whether a turntable angle sample matched with the time stamp exists or not, if so, taking the turntable angle sample as a target turntable angle, otherwise, screening a preset number of adjacent time sample points, and calculating the target turntable angle in a linear interpolation mode; s2053, correcting the space pointing relation corresponding to the three-dimensional coordinates based on the target turntable angle to finish delay compensation; s2054, taking the three-dimensional coordinates after delay compensation as the spatial positions of the high-speed unmanned aerial vehicle to be tracked at the historical moment to form the historical position sequence.
4. The kalman filter-based high-speed unmanned aerial vehicle tracking method according to claim 1, wherein S3 specifically comprises: S301, defining a state vector according to the historical position sequence; S302, constructing a state equation and an observation equation according to the state vector to obtain the Kalman filter.
5. The kalman filter-based high-speed unmanned aerial vehicle tracking method according to claim 1, wherein S4 specifically comprises: s401, calculating a state vector of the high-speed unmanned aerial vehicle to be tracked at the next moment through the Kalman filter; S402, calculating a prediction error covariance matrix according to the current acceleration value of the high-speed unmanned aerial vehicle to be tracked; S403, calculating Kalman gain according to the prediction error covariance matrix; S404, calculating optimal estimation according to the Kalman gain and the state vector; And S405, updating the state vector according to the optimal estimation to obtain the future predicted position.
6. The kalman filter based high speed unmanned aerial vehicle tracking method of claim 5, further comprising, after S402, before S403: and updating the prediction error covariance matrix.
7. The kalman filter-based high-speed unmanned aerial vehicle tracking method according to claim 1, wherein S5 specifically comprises: S501, calculating a predicted azimuth angle and a predicted pitch angle according to the future predicted position; s502, obtaining the position error according to the predicted azimuth angle and the predicted pitch angle.
8. The kalman filter-based high-speed unmanned aerial vehicle tracking method according to claim 1, wherein S6 specifically comprises: s601, calculating a proportional term, an integral term and a differential term of the position error; S602, adding the proportional term, the integral term and the differential term to obtain a turntable control output quantity; And S603, sending the turntable control output quantity to a turntable servo driver to obtain the turntable control instruction.
9. A Kalman filtering-based high-speed unmanned aerial vehicle tracking system is characterized by comprising a processor and a memory; The memory stores a program or instructions executable on the processor, which when executed by the processor, implement the steps of the kalman filter based high speed unmanned aerial vehicle tracking method as claimed in any one of claims 1 to 8.
10. A readable storage medium, characterized in that it has stored thereon a program or instructions which, when executed by a processor, implement the steps of the kalman filter based high speed unmanned tracking method according to any of claims 1 to 8.

Description

High-speed unmanned aerial vehicle tracking method and system based on Kalman filtering Technical Field The invention relates to the technical field of target tracking, in particular to a high-speed unmanned aerial vehicle tracking method and system based on Kalman filtering. Background In sensitive areas such as airports, nuclear power plants, military places, large-scale activity venues and the like, how to effectively detect, identify and stably track an unauthorized unmanned aerial vehicle, further timely take interference measures to suppress the occurrence of black flight and privacy invasion of the unmanned aerial vehicle, and the method has become a core technical problem to be solved in the field of low-altitude security protection. According to the existing unmanned aerial vehicle tracking scheme, the core extracts brightness information and optimizes an image sequence through capturing an unmanned aerial vehicle image, the moving direction and the flying speed of a target are estimated to generate a moving track, meanwhile, the flying anomaly detection and the form adjustment strategy are combined, the image capturing parameters and the algorithm sensitivity are reversely optimized, the sudden maneuver of the target is handled, the problems that the unmanned aerial vehicle is difficult to identify and the high-speed target is easy to track and lose under the complex illumination condition are solved to a certain extent, and the unmanned aerial vehicle tracking system has certain universality and tracking stability. However, the prior art relies on a single vision sensor, has weak anti-interference capability, cannot acquire real three-dimensional information of a target, causes poor tracking precision, lacks prediction capability of future motion state of the target, has obvious hysteresis for tracking a high-speed unmanned aerial vehicle, is difficult to adapt to sudden maneuver characteristic, is easy to generate tracking loss, and cannot meet high-precision tracking requirements of low-altitude security in a sensitive area. Disclosure of Invention In view of the shortcomings of the prior art, the embodiment of the invention aims to provide a high-speed unmanned aerial vehicle tracking method based on Kalman filtering, which can solve the technical problems that the prior art depends on a single visual sensor, has weak anti-interference capability, cannot acquire real three-dimensional information of a target, causes poor tracking precision, lacks prediction capability of future motion state of the target, has obvious hysteresis for tracking the high-speed unmanned aerial vehicle, is difficult to adapt to sudden maneuvering characteristics, is easy to generate tracking loss, and cannot meet high-precision tracking requirements of low-altitude security in a sensitive area. In a first aspect of the embodiment of the present invention, a high-speed unmanned aerial vehicle tracking method based on kalman filtering is provided, including: s1, collecting asynchronous data flow of a high-speed unmanned aerial vehicle to be tracked; S2, performing space-time registration and target association processing on the asynchronous data stream to obtain a historical position sequence of the high-speed unmanned aerial vehicle to be tracked; S3, constructing a Kalman filter based on a current statistical model; s4, inputting the historical position sequence into a Kalman filter, and outputting a future predicted position; s5, comparing the future predicted position with the current position of the high-speed unmanned aerial vehicle to be tracked to obtain a position error; S6, adjusting the position error through a proportional-integral-derivative control algorithm to obtain a turntable control instruction of the high-speed unmanned aerial vehicle to be tracked; and S7, driving the high-speed unmanned aerial vehicle to be tracked to travel according to the predicted position in the future according to the turntable control instruction so as to realize tracking of the high-speed unmanned aerial vehicle to be tracked. In a second aspect of the embodiment of the invention, a high-speed unmanned aerial vehicle tracking system based on Kalman filtering is provided, which comprises a processor and a memory, wherein the processor is used for processing the signals; the memory stores a program or instructions executable on the processor, which when executed by the processor, implement the steps of the kalman filter based high speed drone tracking method as described in the first aspect. In a third aspect of the embodiments of the present invention, a readable storage medium is provided, where a program or an instruction is stored, where the program or the instruction implement the steps of the kalman filter based high-speed unmanned aerial vehicle tracking method according to the first aspect when executed by a processor. The technical scheme provided by the embodiment of the invention has the beneficial effects that at