Self-adaptation variable-sliding-window multi-target tracking method

Abstract

The invention belongs to the technical field of radar multi-target tracking and discloses a self-adaptation variable-sliding-window multi-target tracking method. The self-adaptation variable-sliding-window multi-target tracking method includes the following steps that firstly, distance-time data or distance-Doppler data are obtained; secondly, an initial detection window of a jth target is set, and corresponding N frame data are stored into the initial detection window of the jth target; thirdly, a detection result of the jth target in the current detection window is obtained according to the N frame data of the jth target in the current detection window; fourthly, if a detection result of the jth target in a previous detection window exists, the fifth step is executed, and if not, the sixth step is executed; fifthly, track fusion is performed according to the detection result of the jth target in the previous detection window and the detection result of the jth target in the current detection window; sixthly, when a radar receives new frame data, the current detection window of the jth target is updated, and then the third step is executed.

Description

technical field [0001] The invention belongs to the technical field of radar multi-target tracking, in particular to an adaptive variable sliding window multi-target tracking method, which can be used for monitoring systems such as radars to realize detection and tracking of high-speed and weak targets. Background technique [0002] In modern warfare under the high-tech background, continuous monitoring of the battlefield, which can provide rich strategic and tactical information for situation assessment, command and other applications, plays a key role in winning the war. With its all-day and all-weather working characteristics, radar has always been the core of the battlefield surveillance system. Ground-based long-range detection radars detect and track space orbit targets, and there are challenges such as long detection distances and weak target echo signals, which is a detection and tracking problem under the condition of low signal-to-noise ratio. [0003] The methods...

AI Technical Summary

Problems solved by technology

The TBD method based on Hough transform is very effective for the detection and estimation of straight lines, but for the detection of complex curves, it has the disadvantages of complex calculation and large storage space.

The random Hough transform can detect various parameterized curves, but this method is difficult to balance between the parameter estimation accuracy and the calculation amount

2) The TBD method based on multi-stage hypothesis testing is an exhaustive search method, which needs to calculate all possible trajectories in the image sequence. When the number of sequence frames becomes longer, the number of trajectories grows explosively, and the amount of calculation is huge.

4) The particle filter tracking before detection algorithm (PF-TBD) and its extended algorithm are research hotspots based on recursive Bayesian filter-like TBD methods, but the particle filter algorithm has the phenomenon of particle degradation, and the resampling step is introduced. After resampling, there is a problem that it is difficult to build a parallel processing structure, and the computational complexity of the PF algorithm is high

Images