Active sonar tracking based on bearing filter and converted measurement

Yangfan DU; Wen ZHANG; Xuanzhi ZHAO; Zengli LIU; Kang LIU

doi:10.11990/jheu.202506015

Chinese

您当前的位置：

首页 >

文章列表页 >

Active sonar tracking based on bearing filter and converted measurement

Research Article | 更新时间：2026-03-31

- Active sonar tracking based on bearing filter and converted measurement
- Journal of Harbin Engineering University Vol. 46, Issue 8, Pages: 1625-1634(2025)
- 作者机构：
  
  1.昆明理工大学信息工程与自动化学院, 云南昆明 650504
  2.南方科技大学环境科学与工程学院, 广东深圳 518055
- 作者简介：
- 基金信息：
- DOI：10.11990/jheu.202506015
  CLC： TB566
- Received：07 June 2025，
  
  Online First：24 June 2025，
  
  Published：05 August 2025
- 稿件说明：
移动端阅览
Yangfan DU, Wen ZHANG, Xuanzhi ZHAO, et al. Active sonar tracking based on bearing filter and converted measurement[J]. Journal of Harbin Engineering University, 2025, 46(8): 1625-1634.
DOI：

Yangfan DU, Wen ZHANG, Xuanzhi ZHAO, et al. Active sonar tracking based on bearing filter and converted measurement[J]. Journal of Harbin Engineering University, 2025, 46(8): 1625-1634. DOI： 10.11990/jheu.202506015.

摘要

针对主动声呐跟踪中多普勒量测与目标运动状态间非线性关系复杂、自由度不匹配的问题，本文提出一种基于角度滤波与4变量转换量测的主动声呐跟踪方法。首先，设计线性结构角度滤波器，通过最小方差准则融合角度量测与状态预测，提供高精度的角速度伪量测；其次，结合声呐原始量测(距离、方位角、径向速度)与角速度伪量测，构建4维增广量测向量，提出4变量多普勒转换量测方法，将极坐标系量测无偏转换至笛卡尔坐标系，解决自由度不足问题；最后，利用线性卡尔曼滤波实现状态更新，形成角度滤波与状态滤波的双重交互结构。仿真结果表明，相比现有算法，所提方法在目标慢速机动或转弯时具有更高的估计精度和一致性，有效降低了非线性近似误差，提升了多普勒信息利用率。所提方法为同时保证量测转换的无偏性和协方差的准确性提供了思路，具有较高的实际应用潜力。

Abstract

Aiming to address the intricate nonlinear relationship and the disparity in degrees of freedom between Doppler measurements and target motion states in active sonar tracking

this study proposes a tracking method based on bearing filtering and four-variable converted measurement. Initially

a linear structure bearing filter is engineered to amalgamate bearing measurements and state predictions employing the minimum variance criterion

producing high-precision bearing rate pseudomeasurements. Secondly

the integration of the original sonar measurements (range

bearing

and radial velocity) with the bearing rate pseudomeasurements results in the construction of a four-dimensional augmented measurement vector. A four-variable Doppler conversion measurement method is proposed to unbiasedly convert polar coordinate measurements into Cartesian coordinates

addressing the issue of insufficient degrees of freedom. Linear Kalman filtering is employed to facilitate state updates

establishing an interactive dual-filter structure for bearing and motion states in Cartesian coordinates. The simulation results indicate that

in comparison with existing algorithms

during slow maneuvers or target turning

the proposed method achieves higher estimation accuracy and consistency

effectively reducing nonlinear approximation errors and enhancing the utilization of Doppler information. The proposed methodology establishes a novel framework for ensuring both the unbiasedness of measurement conversion and the precision of covariance estimation

demonstrating considerable potential for practical applications.

关键词

Keywords

references

侯翔昊, 乔钢, 周建波, 等. 基于矢量对偶四元数的水下机动目标自适应跟踪[J]. 哈尔滨工程大学学报, 2020, 41(10): 1444-1449.

HOU Xianghao, QIAO Gang, ZHOU Jianbo, et al. Adaptive tracking algorithm for underwater maneuvering target based on vector dual quaternions[J]. Journal of Harbin Engineering University, 2020, 41(10): 1444-1449.

薛树强, 杨元喜, 肖圳, 等. 全球导航卫星系统-声呐组合观测模型分类体系[J]. 哈尔滨工程大学学报, 2023, 44(11): 1857-1868.

XUE Shuqiang, YANG Yuanxi, XIAO Zhen, et al. Global navigation satellite system-acoustic combined observation model classification system[J]. Journal of Harbin Engineering University, 2023, 44(11): 1857-1868.

王宁, 段睿, 周笑仪. 量测不确定性条件下的箱粒子滤波目标跟踪方法[J]. 电子与信息学报, 2024, 46(9): 3654-3661.

WANG Ning, DUAN Rui, ZHOU Xiaoyi. A target tracking method based on box-particle filter under measurement uncertainty[J]. Journal of electronics&information technology, 2024, 46(9): 3654-3661.

李可非, 马晓川, 刘宇, 等. 基于转换量测容积卡尔曼滤波器带多普勒量测的目标跟踪算法[J]. 控制与决策, 2021, 36(6): 1425-1434.

LI Kefei, MA Xiaochuan, LIU Yu, et al. Converted measurement cubature Kalman filter for Doppler-assisted target tracking[J]. Control and decision, 2021, 36(6): 1425-1434.

HUANG Jianjun, HU Haoqiang, KANG Li. Time convolutional network-based maneuvering target tracking with azi muth-Doppler measurement[J]. Sensors, 2024, 24(1): 263.

LI Wujun, YI Wei, TEH K C, et al. Adaptive multiframe detection algorithm with range-Doppler-azimuth measurements[J]. IEEE transactions on geoscience and remote sensing, 2022, 60: 5119616.

LENOACH J, LEXA M, CORALUPPI S. Feature-aided tracking techniques for active sonar applications[C]//2021 IEEE 24th international conference on information fusion (FUSION). Piscataway, NJ, 2021: 1-7.

LUO Li, HUANG Yulong, WANG Guoqing, et al. An on-line full-parameters calibration method for SINS/DVL integrated navigation system[J]. IEEE sensors journal, 2023, 23(24): 30927-30939.

HUANG Yulong, BAI Mingming, LI Youfu, et al. An improved variational adaptive Kalman filter for cooperative localization[J]. IEEE sensors journal, 2021, 21(9): 10775-10786.

ZHANG Chenglin, SHI Wentao, GONG Zijun, et al. Target tracking using time delay and Doppler shift with nonnegligible motion effect in bistatic sonar[J]. IEEE sensors journal, 2023, 23(14): 15910-15923.

KONATOWSKI S, KANIEWSKI P, MATUSZEWSKI J. Comparison of estimation accuracy of EKF, UKF and PF filters[J]. Annual of navigation, 23: 69-87.

LEFEBVRE T, BRUYNINCKX H, DE SCHULLER J. Comment on "a new method for the nonlinear transformation of means and covariances in filters and estimators" with authors′ reply[J]. IEEE transactions on automatic control, 2002, 47(8): 1406-1409.

ARASARATNAM I, HAYKIN S. Cubature Kalman filters[J]. IEEE transactions on automatic control, 2009, 54(6): 1254-1269.

王建国, 龙腾, 何佩琨. 一种在Kalman滤波中引入径向速度测量的新方法[J]. 信号处理, 2002, 18(5): 414-416, 409.

WANG Jianguo, LONG Teng, HE Peikun. A new method of incorporating radial velocity measurement into Kalman filter[J]. Signal processing, 2002, 18(5): 414-416, 409.

段战胜, 韩崇昭. 极坐标系中带多普勒量测的雷达目标跟踪[J]. 系统仿真学报, 2004, 16(12): 2860-2863.

DUAN Zhansheng, HAN Chongzhao. Radar target tracking with Doppler measurements in polar coordinates[J]. Acta simulata systematica sinica, 2004, 16(12): 2860-2863.

ZHOU Gongjian, PELLETIER M, KIRUBARAJAN T, et al. Statically fused converted position and Doppler measurement Kalman filters [J]. IEEE transactions on aerospace and electronic systems, 2014, 50(1): 300-318.

BAR-SHALOM Y. Negative correlation and optimal tracking with Doppler measurements[J]. IEEE transactions on aerospace and electronic systems, 2001, 37(3): 1117-1120.

LEI Ming, HAN Chongzhao. Sequential nonlinear tracking using UKF and raw range-rate measurements[J]. IEEE transactions on aerospace and electronic systems, 2007, 43(1): 239-250.

DUAN Zhansheng, LI X R, HAN Chongzhao, et al. Sequential unscented Kalman filter for radar target tracking with range rate measurements[C]//2005 7th International Conference on Information Fusion. Piscataway, NJ, 2005: 8pp.

吴心童, 刘宇, 马晓川, 等. 含未知非高斯噪声的自适应量测转换水下目标跟踪[J]. 声学学报, 2024, 49(4): 671-682.

WU Xintong, LIU Yu, MA Xiaochuan, et al. Adaptive measurement conversion for underwater target tracking with unknown non-Gaussian noise[J]. Acta acustica, 2024, 49(4): 671-682.

LERRO D, BAR-SHALOM Y. Tracking with debiased consistent converted measurements versus EKF[J]. IEEE transactions on aerospace and electronic systems, 2002, 29(3): 1015-1022.

MO Longbin, SONG Xiaoquan, ZHOU Yiyu, et al. Unbiased converted measurements for tracking[J]. IEEE transactions on aerospace and electronic systems, 1998, 34(3): 1023-1027.

BORDONARO S, WILLETT P, BAR-SHALOM Y. Decorrelated unbiased converted measurement Kalman filter[J]. IEEE transactions on aerospace and electronic systems, 2014, 50(2): 1431-1444.

LIU Hongqiang, ZHOU Zhongliang, YU Lei, et al. Two unbiased converted measurement Kalman filtering algorithms with range rate[J]. IET radar, sonar&navigation, 2018, 12(11): 1217-1224.

BORDONARO S, WILLETT P, BAR-SHALOM Y. Consistent linear tracker with converted range, bearing, and range rate measurements[J]. IEEE transactions on aerospace and electronic systems, 2017, 53(6): 3135-3149.

ZHANG Wen, ZHAO Xuanzhi, LIU Zengli, et al. Converted state equation Kalman filter for nonlinear maneuvering target tracking[J]. Signal processing, 2023, 202: 108741.

LI K, ZHAO S, HUANG B, et al. Bayesian filtering for LI Ke, ZHAO Shunyi, HUANG Biao, et al. Bayesian filtering for high-dimensional state-space models with state partition and error compensation[J]. IEEE/CAA journal of automatica sinica, 2024, 11(5): 1239-1249.

Views

158

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Online identification method for morphing vehicles with time-varying aerodynamic parameters

Deceleration transition controller design of thrust-vectored short takeoff and vertical landing aircraft based on control allocation

Data fusion method based on IMM-Kalman for an integrated navigation system

Selective catalytic reduction control strategy based on model predictive algorithm

Related Author

Jianying WANG

Yanxue ZHANG

Wenxia HUO

Pengyu ZHANG

Xinyue LU

TANG Yong

LIU Jicheng

ZHEN Ziyang

Related Institution

School of Aeronautics and Astronautics, Sun Yat-Sen University

Science and Technology on Space Physics Laboratory

College of Automatic Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

AVIC Chengdu Aircraft Design &amp Research Institute Chengdu China

Institute of Microelectronics of the Chinese Academy of Sciences Beijing China

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰