Underwater target ranging technique based on nonstationary frequency-azimuth measurements

Xianghao HOU; Jiarui ZHENG; Xinyu GU; Yixin YANG

doi:10.11990/jheu.202506004

Chinese

您当前的位置：

首页 >

文章列表页 >

Underwater target ranging technique based on nonstationary frequency-azimuth measurements

Research Article | 更新时间：2026-03-31

- Underwater target ranging technique based on nonstationary frequency-azimuth measurements
- Journal of Harbin Engineering University Vol. 46, Issue 8, Pages: 1547-1556(2025)
- 作者机构：
  
  1.西北工业大学航海学院, 陕西西安 710072
  2.杭州应用声学研究所，浙江杭州 310023
- 作者简介：
- 基金信息：
- DOI：10.11990/jheu.202506004
  CLC： TB566
- Received：04 June 2025，
  
  Online First：23 June 2025，
  
  Published：05 August 2025
- 稿件说明：
移动端阅览
Xianghao HOU, Jiarui ZHENG, Xinyu GU, et al. Underwater target ranging technique based on nonstationary frequency-azimuth measurements[J]. Journal of Harbin Engineering University, 2025, 46(8): 1547-1556.
DOI：

Xianghao HOU, Jiarui ZHENG, Xinyu GU, et al. Underwater target ranging technique based on nonstationary frequency-azimuth measurements[J]. Journal of Harbin Engineering University, 2025, 46(8): 1547-1556. DOI： 10.11990/jheu.202506004.

摘要

针对水下环境中量测噪声的非平稳及统计特性未知等问题，本文在典型水下目标跟踪场景下，提出一种融合运动变化约束与自适应噪声估计的量测异常检测与修正机制。结合水下无人潜航器低速航行的运动特性，构建运动模型并引入基于新息分析的动态阈值策略，实现对突发量测异常的实时判别与修正。为增强滤波器在噪声未知环境中的适应能力，进一步引入了Sage-Husa(SH)算法解决噪声未知的问题，在动态环境中自适应调整量测噪声。经仿真与实测结果验证:所提方法与传统方法相比测距误差降低13.16%，表明其在量测噪声非平稳下具备更强的稳健性与适应性。

Abstract

To address the challenges of nonstationary measurement noise and unknown statistical characteristics in underwater environments

a measurement anomaly detection and correction mechanism that integrates motion variation constraints with adaptive noise estimation was developed. Given that autonomous underwater vehicles are characterized by low-speed navigation

a motion model was constructed and a dynamic thresholding strategy based on innovation analysis was introduced to enable real-time identification and correction of abrupt measurement anomalies. The Sage-Husa algorithm was also incorporated to enhance the adaptability of the filter to environments with unknown noise statistics

allowing online adjustment of the measurement noise under dynamic conditions. Both simulation and experimental results demonstrate that the proposed method achieves a 13.16% reduction in ranging error compared to the traditional approach

indicating enhanced robustness and adaptability undernon-stationary measurement noise conditions.

关键词

Keywords

references

HOU Xianghao, CHEN Yuxuan, ZHANG Boxuan, et al. Review of research progress on passive direction-of-arrival tracking technology for underwater targets[J]. Remote sensing, 2024, 16(23): 4511.

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, 1463.

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, 1463.

孙同晶, 朱庆煜, 王治撰. 基于主动波导不变量分布的改进扩展卡尔曼滤波跟踪方法[J]. 电子与信息学报, 2025, 47(1): 167-177.

SUN Tongjing, ZHU Qingyu, WANG Zhizhuan. Improved extended Kalman filter tracking method based on active waveguide invariant distribution[J]. Journal of electronics&information technology, 2025, 47(1): 167-177.

LI Yibing, JIU Mingyang, SUN Qian, et al. An improved target tracking algorithm based on extended Kalman filter for UAV[C]//2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP). Piscataway, NJ, 2018: 435-437.

季陈倍, 田梦楚, 吴云, 等. 基于粒子滤波的无人机实时在线目标跟踪算法研究[J]. 智能物联技术, 2025, 8(1): 13-20.

JI Chenbei, TIAN Mengchu, WU Yun, et al. Research on real-time online target tracking algorithm for unmanned aerial vehicle based on particle filter[J]. Technology of IoT&AI, 2025, 8(1): 13-20.

LI X H,LI Y A,FY Y,et al. EKF and PF algorithmsand their application stotar get tracking[J]. Technical acoustic,2012,31(3):296-299.

陈晓锋, 庄巍, 李少勇, 等. 一种基于强跟踪无迹卡尔曼滤波算法的组合导航方法[J]. 机电工程技术, 2024, 53(12): 105-110.

CHEN Xiaofeng, ZHUANG Wei, LI Shaoyong, et al. A combined navigation method based on strong tracking unscentedKalman filter algorithm[J]. Mechanical&electrical engineering technology, 2024, 53(12): 105-110.

YAN Hao, WANG Fawei. A method of strong tracking UKF based on adaptive constraints[C]//2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Cheng du, 2019: 213-234.

孙宝全, 颜冰, 姜润翔, 等. 基于约束渐进扩展卡尔曼滤波的船舶电场跟踪定位方法[J]. 海军工程大学学报, 2018, 30(6): 30-35.

SUN Baoquan, YAN Bing, JIANG Runxiang, et al. Method of ship electric field tracking and positioning based on constrained progressive extended Kalman filter[J]. Journal of Naval University of Engineering, 2018, 30(6): 30-35.

曲毅, 刘忠. 基于UKF的水下目标纯方位跟踪算法[J]. 舰船科学技术, 2009, 31(7): 133-136.

QU Yi, LIU Zhong. Research of underwater bearings-only target tracking algorithm based on UKF[J]. Ship science and technology, 2009, 31(7): 133-136.

田政, 姜林君, 程显超, 等. 传感器测量衰减下的水下目标纯方位跟踪算法[J]. 海洋测绘, 2020, 40(5): 53-57.

TIAN Zheng, JIANG Linjun, CHENG Xianchao, et al. Multi-sensor bearing-only underwater target trackingargorithm for sensor measurement attenuation[J]. Hydrographic surveying and charting, 2020, 40(5): 53-57.

HOU Xianghao, QIAO Yueyi, ZHANG Boxuan, et al. Robust underwater direction-of-arrival tracking based on variational Bayesian extended Kalman filter[J]. JASA express letters, 2023, 3(1): 014801.

ZHANG Boxuan, HOU Xianghao, YANG Yixin. Robust underwater direction-of-arrival tracking with uncertain environmental disturbances using a uniform circular hydrophone array[J]. The journal of the acoustical society of America, 2022, 151(6): 4101-4113.

ZHANG Boxuan, HOU Xianghao, YANG Yixin, et al. Robust Underwater Direction-of-Arrival Tracking with Uncertain Measurement Noise[C]//International Conference on Guidance, Navigationand Control. Singapore, 2022.

张博轩, 杨益新, 侯翔昊.量测噪声不确定情况下的水下多目标稳健方位跟踪[J].声学学报, 2023, 48(4):605-617.

ZHANG Boxuan, YANG Yixin, HOU Xianghao.Robust underwater multi-target direction-of-arrival tracking withuncertain measurement noise[J].Acta acustica, 2023, 48(4):605-617.

ZHANG Boxuan, HOU Xianghao, YANG Yixin, et al. A fast variational Bayesian adaptive extended Kalman filter for robust underwater direction-of-arrival tracking[J]. IEEE sensors journal, 2023, 23(13): 14709-14720.

陈启航, 罗威, 谢晓乐, 等. 基于残差自适应的纯方位伪线性卡尔曼滤波跟踪方法[J]. 舰船科学技术, 2025, 47(4): 130-136.

CHEN Qihang, LUO Wei, XIE Xiaole, et al. Residual adaptive bearing-only pseudo-linear Kalman filter tracking method[J]. Ship science and technology, 2025, 47(4): 130-136.

匡玉. 基于角度和多普勒信息的多目标被动跟踪与分类[D]. 杭州: 杭州电子科技大学, 2024.

KUANG Yu. Multi-target passive tracking and classification based on angle and Doppler information[D]. Hangzhou: Hangzhou Dianzi University, 2024.

王为, 王俊龙. 利用深海目标方位-频率测量的粒子滤波TMA方法[J]. 声学与电子工程, 2024(4): 1-4, 28.

WANG Wei, WANG Junlong. Particle filter TMA method using deep-sea target azimuth-frequency measurement[J]. Acoustics and electronics engineering, 2024(4): 1-4, 28.

赵显. 多普勒雷达目标跟踪算法研究[D]. 昆明: 昆明理工大学, 2024.

ZHAO Xian. Research on target tracking algorithm of Doppler radar[D]. Kunming: Kunming University of Science and Technology, 2024.

陈小莉, 朱溢涛, 郭文飞, 等.基于卡尔曼滤波的铱星信号多普勒跟踪方法与参数设计[J].电讯技术, 2025, 65(5):756-766.

CHEN Xiaoli,ZHU Yitao,GUO Wenfei,et al.Doppler tracking method and parameter design of iridium signal based on kalman filtering[J]. Telecommunication Engineering, 2025, 65(5):756-766.

Views

121

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design of brain-like small-world reservoir using heat kernel diffusion

Design and control of an underactuated finger based on coupled adaptive linkage mechanism

Large-scale weapon-target allocation based on an artificial bee colony algorithm

Adaptive null strong interference suppression DOA estimation method for vector arrays

Application of adaptive stochastic resonance in signal feature extraction

Related Author

Fangwan HUANG

Zhiyong YU

Ying YAN

Li JIANG

Ming CHENG

Shaowei FAN

Jinghui DAI

Liwei CHEN

Related Institution

College of Computer and Data Science, Fuzhou University

Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University

State Key Laboratory of Robotics and System, Harbin Institute of Technology

School of Information and Communication Engineering, Harbin Engineering University

CSSC Systems Engineering Research Institute

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