EI / SCOPUS / CSCD 收录

中文核心期刊

Volume 49 Issue 3
May  2024
Turn off MathJax
Article Contents
Abstract
References
Related Articles
LIANG Guolong, ZHANG Boyu, QI Bin, HAO Yu, DU Zhiyao, LI Xiang. Underwater multitarget fusion tracking method for passive sonar[J]. ACTA ACUSTICA, 2024, 49(3): 501-512. DOI: 10.12395/0371-0025.2022188
Citation: LIANG Guolong, ZHANG Boyu, QI Bin, HAO Yu, DU Zhiyao, LI Xiang. Underwater multitarget fusion tracking method for passive sonar[J]. ACTA ACUSTICA, 2024, 49(3): 501-512. DOI: 10.12395/0371-0025.2022188
shu

Underwater multitarget fusion tracking method for passive sonar

  • 1.

    National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University　Harbin　150001

  • 2.

    Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology　Harbin　150001

  • 3.

    College of Underwater Acoustic Engineering, Harbin Engineering University　Harbin　150001

More Information
  • PACS: 
    • 43.30  (Underwater sound)
    • 43.60  (Acoustic signal processing)
  • Received Date: December 27, 2022
  • Revised Date: April 03, 2023
  • Available Online: May 08, 2024
    Graphical Abstract
    • Abstract

  • 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.

    • FullText(HTML)
    • References (29)
  • [1]
    王冠群, 张春华, 尹力, 等. 联合多站阵元域数据的水下目标检测与跟踪. 声学学报, 2019; 44(4): 491−502 DOI: 10.15949/j.cnki.0371-0025.2019.04.010
    [2]
    谢志华, 蒋丞, 吴俊超, 等. 水下目标多平台协同定位和跟踪方法. 声学学报, 2021; 46(6): 1028−1038 DOI: 10.15949/j.cnki.0371-0025.2021.06.022
    [3]
    王冠群, 张春华, 尹力, 等. 改进联合多站多伯努利滤波器的水下多目标跟踪. 声学学报, 2021; 46(4): 508−518 DOI: 10.15949/j.cnki.0371-0025.2021.04.003
    [4]
    金盛龙, 李宇, 黄海宁. 水下多目标方位的联合检测与跟踪. 声学学报, 2019; 44(4): 503−512 DOI: 10.15949/j.cnki.0371-0025.2019.04.011
    [5]
    鄢社锋, 马远良. 传感器阵列波束优化设计及应用. 北京: 科学出版社, 2009
    [6]
    Fortmann T, Bar-Shalom Y, Scheffe M. Sonar tracking of multiple targets using joint probabilistic data association. IEEE J. Oceanic Eng., 1983; 8(3): 173−184 DOI: 10.1109/JOE.1983.1145560
    [7]
    生雪莉, 陈洋, 郭龙祥, 等. 标记关联的多声呐多目标航迹融合方法. 哈尔滨工程大学学报, 2020; 41(9): 1346−1352 DOI: 10.11990/jheu.201910044
    [8]
    Vo B N, Ma W K. The Gaussian mixture probability hypothesis density filter. IEEE Trans. Signal Process., 2006; 54(11): 4091−4104 DOI: 10.1109/TSP.2006.881190
    [9]
    Vo B N, Vo B T, Hoang H G. An efficient implementation of the generalized labeled multi-Bernoulli filter. IEEE Trans. Signal Process., 2017; 65(8): 1975−1987 DOI: 10.1109/TSP.2016.2641392
    [10]
    申屠晗, 李凯斌, 荣英佼, 等. 一种多传感器自适应量测迭代更新GM-PHD跟踪算法. 电子与信息学报, 2022; 44(12): 4168−4177 DOI: 10.11999/JEIT211138
    [11]
    Shi K, Shi Z, Yang C, et al. Road-map aided GM-PHD filter for multivehicle tracking with automotive radar. IEEE Trans. Industr. Inform., 2022; 18(1): 97−108 DOI: 10.1109/TII.2021.3073032
    [12]
    Fu Z, Angelini F, Chambers J, et al. Multi-level cooperation fusion of GM-PHD filters for online multiple human tracking. IEEE Trans. Multimedia, 2019; 21(9): 2277−2291 DOI: 10.1109/TMM.2019.2902480
    [13]
    Sung Y, Tokekar P. GM-PHD filter for searching and tracking an unknown number of targets with a mobile sensor with limited FOV. IEEE Trans. Autom. Sci. Eng., 2022; 19(3): 2122−2134 DOI: 10.1109/TASE.2021.3073938
    [14]
    周天, 张丽红, 杜伟东, 等. 声呐图像多目标跟踪高斯滤波算法. 哈尔滨工程大学学报, 2020; 41(5): 691−697 DOI: 10.11990/jheu.201808089
    [15]
    马雪飞, 李胤, 吴英姿, 等. 基于高斯混合概率假设滤波的水下目标跟踪算法. 应用声学, 2023; 42(2): 249−259 DOI: 10.11684/j.issn.1000-310X.2023.02.007
    [16]
    Wu Z, Cai, Q, Fu M. Covariance Intersection for partially correlated random vectors. IEEE Trans. Automat. Contr., 2018; 63(3): 619−629 DOI: 10.1109/TAC.2017.2718243
    [17]
    Wang B, Yi W, Hoseinnezhad R, et al. Distributed fusion with multi-Bernoulli filter based on generalized covariance intersection. IEEE Trans. Signal Process., 2017; 65(1): 242−255 DOI: 10.1109/TSP.2016.2617825
    [18]
    Li G, Battistelli G, Yi W, et al. Distributed multi-sensor multi-view fusion based on generalized covariance intersection. Signal Process., 2020; 166: 107246 DOI: 10.1016/j.sigpro.2019.107246
    [19]
    Yi W, Li S Q, Wang B, et al. Computationally efficient distributed multi-sensor fusion with multi-Bernoulli filter. IEEE Trans. Signal Process., 2020; 68: 241−256 DOI: 10.1109/TSP.2019.2957638
    [20]
    孙大军, 张珂, 梅继丹, 等. 稀疏矢量阵信号栅瓣及对称伪峰干扰抑制. 声学学报, 2021; 46(1): 23−34 DOI: 10.15949/j.cnki.0371-0025.2021.01.003
    [21]
    Panta K, Clark D E, Vo B N. Data association and track management for the Gaussian mixture probability hypothesis density filter. IEEE Trans. Aerosp. Electron. Syst., 2009; 45(3): 1003−1016 DOI: 10.1109/TAES.2009.5259179
    [22]
    王宇杰, 李宇, 鞠东豪, 等. 一种基于水下无人航行器的多目标被动跟踪算法. 电子与信息学报, 2020; 42(8): 2013−2020 DOI: 10.11999/JEIT190675
    [23]
    Zhao J, Gui R, Dong X. PHD filtering for multi-source DOA tracking with extended co-prime array: An improved MUSIC pseudo-likelihood. IEEE Commun. Lett., 2021; 25(10): 3267−3271 DOI: 10.1109/LCOMM.2021.3099569
    [24]
    Dong F, Xu L, Li X. Particle filter algorithm for DOA tracking using co-prime array. IEEE Commun. Lett., 2020; 24(11): 2493−2497 DOI: 10.1109/LCOMM.2019.2953466
    [25]
    Qin Z, Kirubarajan T, Liang Y. Application of an efficient graph-based partitioning algorithm for extended target tracking using GM-PHD filter. IEEE Trans. Aerosp. Electron. Syst., 2020; 56(6): 4451−4466 DOI: 10.1109/TAES.2020.2990803
    [26]
    He S, Shin H S, Tsourdos A. Multi-sensor multi-target tracking using domain knowledge and clustering. IEEE Sens. J., 2018; 18(19): 8047−8084 DOI: 10.1109/JSEN.2018.2863105
    [27]
    Yi W, Fu L, García-Fernández N F. Particle filtering based track-before-detect method for passive sonar systems. Signal Process., 2019; 165: 303−314 DOI: 10.1016/j.sigpro.2019.07.027
    [28]
    Vo B T, Vo B N. Labeled random finite sets and multi-object conjugate priors. IEEE Trans. Signal Process., 2013; 61(13): 3460−3475 DOI: 10.1109/TSP.2013.2259822
    [29]
    Schuhmacher D, Vo B T, Vo B A. A consistent metric for performance evaluation of multi-object filters. IEEE Trans. Signal Process., 2008; 56(8): 3447−3457 DOI: 10.1109/TSP.2008.920469
    • Related Articles

  • Related Articles

    [1]WU Xintong, LIU Yu, MA Xiaochuan, MA Zhongjing. Adaptive measurement conversion for underwater target tracking with unknown non-Gaussian noise[J]. ACTA ACUSTICA, 2024, 49(4): 671-682. DOI: 10.12395/0371-0025.2024031
    [2]WANG Yan, LI Xiang, QI Bin, LIANG Guolong. A track-before-detect method for neighboring targets based on auxiliary particle filter in passive sonar scenarios[J]. ACTA ACUSTICA, 2023, 48(2): 277-290. DOI: 10.15949/j.cnki.0371-0025.2023.02.015
    [3]WANG Guanqun, ZHANG Chunhua, YIN Li, LI Yu, ZHANG Yangfan. Underwater multi-target tracking using improved multi-sensor multi-Bernoulli filter[J]. ACTA ACUSTICA, 2021, 46(4): 508-518. DOI: 10.15949/j.cnki.0371-0025.2021.04.003
    [4]ZHOU Shengzeng, DU Xuanmin. A robust subband subarray steered minimum variance beamforming algorithm[J]. ACTA ACUSTICA, 2019, 44(4): 707-714. DOI: 10.15949/j.cnki.0371-0025.2019.04.031
    [5]SUN Xu, LI Ranwei, HU Peng. A tracking filter method of active sonar subject to unknown target maneuvering[J]. ACTA ACUSTICA, 2016, 41(3): 371-378. DOI: 10.15949/j.cnki.0371-0025.2016.03.012
    [6]DENG Jing, ZHENG Fang, LIU Jian, WU Wenhu. Using subband Mel-spectrum centroid and Gaussian mixture correlation for robust speaker identification[J]. ACTA ACUSTICA, 2006, 31(5): 471-475. DOI: 10.15949/j.cnki.0371-0025.2006.05.012
    [7]TONG Weifeng, WU Guoqing. Multisensor target tracking and fusion approach[J]. ACTA ACUSTICA, 2003, 28(5): 416-420. DOI: 10.15949/j.cnki.0371-0025.2003.05.006
    [8]MAO Jie, LI Mingxuan. Application of subband adaptive filtering techniques to ultrasonic detection in multilayers[J]. ACTA ACUSTICA, 2003, 28(3): 212-216. DOI: 10.15949/j.cnki.0371-0025.2003.03.004
    [9]LI Hong, WANG Zhao, XIANG Ming, YANG Rijie, ZHAO Junwei, GONG Xianyi. Application of the EM algorithm for the sonar data fusion[J]. ACTA ACUSTICA, 2001, 26(2): 140-144. DOI: 10.15949/j.cnki.0371-0025.2001.02.009
    [10]WANG Xuejun, SHI Xizhi, LIN Liangji, ZHANG Mingzhi. Underwater target recognition and data fusion[J]. ACTA ACUSTICA, 1999, 24(5): 544-549. DOI: 10.15949/j.cnki.0371-0025.1999.05.014

  • Cited by

    Periodical cited type(2)

    1. 赵金剑，刘蓓蕾，赵勇. 基于多传感器融合的图像识别与目标检测研究. 无线互联科技. 2024(18): 31-34 .
    2. 肖雨晴，罗亮，于博向，杨志渊，郝连东，艾君鹏. 面向海域环境感知的视觉处理方法研究综述. 计算机工程与应用. 2024(23): 62-78 .

    Other cited types(0)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (285) PDF downloads (122) Cited by(2)
    Related

    Official Website

    WeChat

    Email Alert RSS

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return