DP-TBD-based maneuvering target detecting and tracking method

Abstract

The invention provides a DP-TBD-based maneuvering target detecting and tracking method and mainly aims to solve the problem that a target with greater maneuverability cannot be detected and tracked accurately in the prior art. The method comprises the steps of firstly, setting a motion model and an observation model of a target; secondly, optimizing a state transition method and weighting observation quantity accumulation values, and acquiring the observation quantity accumulation values and the state transition processes of all moments through recursion; thirdly, comparing the observation quantity accumulation values of the last frame with a threshold value and using the states corresponding to the observation quantity accumulation values exceeding the threshold value as the candidate estimation states of the detected target; fourthly, performing backward recursion on the candidate estimation states obtained in the last step and backtracks the candidate motion paths of the target; fifthly, acquiring the final target path and speed estimation through path screening. The method can effectively accumulate the energy of maneuvering targets and reduce the influence of noise on the detection performance, and can be used for the detection and tracking of maneuvering targets.

Description

technical field [0001] The invention relates to the technical field of radar, in particular to a method for detecting and tracking a moving target, which can be used for detecting and tracking a moving target. Background technique [0002] Track-before-detection TBD technology was originally applied to small target detection in optical and infrared images, and has also achieved many achievements in the field of radar target detection. Commonly used TBD algorithms include Hough transform, dynamic programming, and particle filtering. The tracking-before-detection DP-TBD algorithm based on dynamic programming has a clear principle, excellent performance, easy implementation, and has been widely used. [0003] According to the different construction methods of the accumulated value function in the DP-TBD algorithm, it can be divided into two categories: probability density accumulation and energy accumulation. The energy accumulation-based DP-TBD algorithm proposed by Tonisson...

AI Technical Summary

Problems solved by technology

[0004] The above research has optimized the value function construction, threshold calculation, peak value extraction and track backtracking of the DP-TBD algorithm, and achieved good results. Efficient Detection and Tracking

This is because the maneuvering range of the target exceeds the state transition range preset by the algorithm, so that the energy of the target outside the coverage range of the algorithm cannot be effectively accumulated.

If the state transition range preset by the algorithm is increased to cover the maneuvering range of the target, the calculation amount of the algorithm will be greatly increased, and the search ranges under different speed channels will overlap. speed estimate of

In addition, as the search range of the algorithm increases, the influence of noise or clutter within the state transition range on the target will also increase, which will cause some points in the estimated track to deviate from the real track

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