Measurement-driven Gauss-Hermite particle filter with soft spatiotemporal constraints for multi-optical theodolites target tracking

被引:4
|
作者
Zhang, Hongwei [1 ]
Li, Pengfei [2 ]
机构
[1] Sun Yat Sen Univ, Sch Aeronaut & Astronaut, Shenzhen 518071, Peoples R China
[2] Army Acad Artillery & Air Def, Zhengzhou Campus, Zhengzhou 450052, Peoples R China
关键词
Causality-invariant; Measurement-driven Gauss-Hermite Particle Filter (MGHPF); Multi-optical theodolite tracking system; Soft spatiotemporal con-straints; Target maneuvering behavior;
D O I
10.1016/j.cja.2023.03.007
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Multi-Optical Theodolite Tracking systems (MOTTs) can stealthily extract the target's status information from bearings only through non-contact measurement. The constrained MOTTs are partially compatible, yet many existing research works and results are based on the known model, ignoring its discrimination with the target maneuvering behavior pattern. To compensate for these mismatches, this paper develops a Measurement-driven Gauss-Hermite Particle Filter (MGHPF), which elegantly fuses the spatiotemporal constraints and its soft form to perform MOTT missions. Specifically, the target dynamic model and tracking algorithm are based on the target behavior pattern with the adaptive turn rate, fully exploiting the spatial epipolar geometry characteristics for each intersection measurement by a minimax strategy. Then, the center of the feasible area is approximated via the analytic coordinate transformation, and the latent samples are updated via the deterministic Gauss-Hermite integral method with the target's predictive turn rate. Simultaneously, the effects of truncation correction and compensation feedback from the current measurement and historical estimation data are adaptively incorporated into the PF's importance distribution to cover the mixture likelihood. Besides, an effective causality-invariant updating rule is provided to estimate the parameters of these soft spatiotemporal constrained MOTTs with convergence guarantees. Simulated and measured results show good agreement; compared with the stateof-the-art Multi-Model Rao-Blackwell Particle Filter (MMRBPF), the proposed MGHPF improves the filtering accuracy by 7.4%-34.7% and significantly reduces the computational load.(c) 2023 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
引用
收藏
页码:313 / 330
页数:18
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