Systems Engineering and Electronics ›› 2025, Vol. 47 ›› Issue (8): 2549-2557.doi: 10.12305/j.issn.1001-506X.2025.08.13

• Sensors and Signal Processing • Previous Articles    

Multi-target tracking method based on single observer passive motion location

Xiaomeng MA1,2(), Dongming DENG2,*(), Yongjian SHEN2(), Jinshan DING1(), Guoqing HAO2()   

  1. 1. School of Electronic Engineering,Xidian University,Xi’an 710071,China
    2. Beijing Research Institute of Telemetry,Beijing 100094,China
  • Received:2024-05-06 Online:2025-08-25 Published:2025-09-04
  • Contact: Dongming DENG E-mail:mxm_201359@163.com;dengdongming19@163.com;shenyongshen@163.com;ding@xidian.edu.cn;13994552790@163.com

Abstract:

To address the multi-target localization problem in single observer passive motion localization, a multi-target single observer passive motion localization method is proposed based on the Gaussian mixture multi-target filter (GM-MTF) of the random finite set (RFS). Firstly, based on the single observer passive motion location method, the localization problem is equivalent to a target tracking problem, and the nonlinear measurement model of multi-target tracking in the passive location system is constructed. Then, based on the GM-MTF, a Gaussian mixture implementation process fused with the cubature Kalman filter is provided to adapt to the measurement nonlinearity model, and the complete implementation process is given. On this basis, combined with the phase difference change rate positioning method, a single observer passive multi-target positioning method with secondary filtering is proposed. The proposed method integrates single observer passive motion localization and RFS multi-target filters, thereby further expanding the single target localization scene to complex multi-target scenes. Meanwhile, simulation results show that compared with traditional phase difference rate of change positioning methods, the proposed method not only has significant advantages in positioning accuracy, but also has certain anti-interference ability and robustness in complex clutter environments.

Key words: single observer passive motion location, multi-target tracking, Gaussian mixture, finite set statistics, cubature Kalman filtering (CKF)

CLC Number: 

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