Measurement based dimension descent association algorithm for OTHR multi-detection multi-target tracking

Yujie Zhang; Zheng Zhang; Jianglong Yu; Qingdong Li; Xiwang Dong; Zhang Ren

doi:10.1016/j.isatra.2023.05.016

Measurement based dimension descent association algorithm for OTHR multi-detection multi-target tracking

ISA Trans. 2023 Sep:140:18-31. doi: 10.1016/j.isatra.2023.05.016. Epub 2023 May 25.

Authors

Yujie Zhang¹, Zheng Zhang², Jianglong Yu³, Qingdong Li⁴, Xiwang Dong⁵, Zhang Ren⁶

Affiliations

¹ School of Automation Science and Electrical Engineering, Science and technology on Aircraft Control Laboratory, Beihang University, China. Electronic address: Zhangyj0410@buaa.edu.cn.
² School of Automation Science and Electrical Engineering, Science and technology on Aircraft Control Laboratory, Beihang University, China. Electronic address: zzhang_buaa@buaa.edu.cn.
³ School of Automation Science and Electrical Engineering, Science and technology on Aircraft Control Laboratory, Beihang University, China. Electronic address: sdjxyjl@buaa.edu.cn.
⁴ School of Automation Science and Electrical Engineering, Science and technology on Aircraft Control Laboratory, Beihang University, China. Electronic address: liqingdong@buaa.edu.cn.
⁵ School of Automation Science and Electrical Engineering, Science and technology on Aircraft Control Laboratory, Beihang University, China; Institute of Artificial Intelligence, Beihang University, China. Electronic address: xwdong@buaa.edu.cn.
⁶ School of Automation Science and Electrical Engineering, Science and technology on Aircraft Control Laboratory, Beihang University, China. Electronic address: renzhang@buaa.edu.cn.

PMID: 37301650
DOI: 10.1016/j.isatra.2023.05.016

Abstract

The problem of multi-detection multi-target tracking (MDMTT) using over-the-horizon radar in dense clutter environment is studied in this paper. The biggest challenge of MDMTT is the 3-dimensional multipath data association among measurements, detection models and targets. In particular, a lot of clutter measurements are generated in dense clutter environment, which increase the computational burden of 3-dimensional multipath data association greatly. A measurement based dimension descent association (DDA) algorithm is proposed to solve the 3-dimensional multipath data association, which decomposes the 3-dimensional multipath data association into two 2-dimensional data associations. The proposed algorithm can reduce the computational burden compared with the optimal 3-dimensional multipath data association and the computational complexity is analyzed. Besides, a time extension method is designed to detect the new-born targets that appear in the tracking scene, which is based on the sequential measurements. The convergence of the proposed measurement based DDA algorithm is analyzed. The estimation error can convergence to 0 as the number of Gaussian mixtures tends to infinity. The effectiveness and rapidity of the measurement based DDA algorithm are demonstrated by the comparative simulation with the previously proposed algorithms.

Keywords: Convergence analysis; Dimension descent association; Multi-detection multi-target tracking; Target detection.