Underwater multitarget fusion tracking method for passive sonar

被引：0
|
作者：
Liang G. [1 ,2 ,3 ]
Zhang B. [3 ]
Qi B. [1 ,2 ,3 ]
Hao Y. [1 ,2 ,3 ]
Du Z. [3 ]
Li X. [3 ]
机构：
[1] National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin
[2] Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin
[3] College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin
来源：
Shengxue Xuebao/Acta Acustica | / 49卷 / 03期
关键词：
Gaussian mixture probability hypothesis density filter; Generalized covariance intersection; Passive sonar; Subband fusion tracking;
D O I：
10.12395/0371-0025.2022188
中图分类号：
学科分类号：
摘要：
The marine environmental noise will cause the detection results of weak targets significantly different in subdbands and lead to the performance degradation problem of tracking algorithms based on fullband detection results. To address this issue, a subband fusion tracking method is proposed. A modified Gaussian mixture probability hypothesis density (GM-PHD) filter is introduced to obtain direction of arrival (DOA) tracking results for different subbands. In addition, the subband tracking results are fused by the generalized covariance intersection (GCI) technique to obtain the tracking results with integrated subband information. Simulation results illustrate that the proposed method can improve the tracking ability of weak targets with different signal-to-noise ratios in each subband, and the computation time is relatively close to the comparison methods. The sea trial data processing results further demonstrate the effectiveness of the proposed method. © 2024 Science Press. All rights reserved.
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页码：501 / 512
页数：11
相关论文
共 24 条
  • [1] 44
  • [2] 46
  • [3] 44
  • [4] (2009)
  • [5] Fortmann T, Bar-Shalom Y, Scheffe M., Sonar tracking of multiple targets using joint probabilistic data association, IEEE J. Oceanic Eng, 8, 3, pp. 173-184, (1983)
  • [6] Vo B N, Ma W K., The Gaussian mixture probability hypothesis density filter, IEEE Trans. Signal Process, 54, 11, pp. 4091-4104, (2006)
  • [7] Vo B N, Vo B T, Hoang H G., An efficient implementation of the generalized labeled multi-Bernoulli filter, IEEE Trans. Signal Process, 65, 8, pp. 1975-1987, (2017)
  • [8] 44, 12, pp. 4168-4177, (2022)
  • [9] Shi K, Shi Z, Yang C, Et al., Road-map aided GM-PHD filter for multivehicle tracking with automotive radar, IEEE Trans. Industr. Inform, 18, 1, pp. 97-108, (2022)
  • [10] Fu Z, Angelini F, Chambers J, Et al., Multi-level cooperation fusion of GM-PHD filters for online multiple human tracking, IEEE Trans. Multimedia, 21, 9, pp. 2277-2291, (2019)
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