Multi-target Track-to-Track Association Based on Relative Coordinate Assignment Matrix
Jintao Chen, Yan Zhang, Yang Yang, Chengzhi Qu · 2021
Multi-sensor data fusion has been widely recognized to improve the performance of multi-target tracking. Track-to-track association (TTTA) must be completed before performing track-to-track fusion. However, the TTTA problem will be complicated by the systematic sensor biases which are different from random error. This article focuses on the TTTA problem in the presence of sensor biases and dense false tracks. A novel multi-target TTTA method based on the relative coordinate assignment matrix (RCAM) is proposed. More specifically, the RCAM leverages the relative coordinate information between two targets instead of the absolute position information of each target. We theoretically analyze the influence of systematic azimuth and range biases on target measurement information and RCAM of target, and test the proposed method in different challenging scenarios. Simulation results demonstrate better performance of the new method against systematic sensor biases and false tracks compared with other competing methods.