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兵工学报 ›› 2023, Vol. 44 ›› Issue (5): 1394-1402.doi: 10.12382/bgxb.2022.0028

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基于贝叶斯估计的浅海声源被动定位方法

石海杰1, 李京华1,*(), 刘丽丽1, 常虹2   

  1. 1 西北工业大学 电子信息学院, 陕西 西安 710129
    2 西安邮电大学 通信与信息工程学院, 陕西 西安 710121
  • 收稿日期:2023-01-01 上线日期:2022-06-14
  • 通讯作者:
    *邮箱: E-mail:
  • 基金资助:
    陕西省重点研发计划项目(2020GY-56)

Passive Localization Method for Acoustic Sources in Shallow Water Based on Bayesian Estimation

SHI Haijie1, LI Jinghua1,*(), LIU Lili1, CHANG Hong2   

  1. 1 School of Electronics and Information,Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China
    2 School of Communications and Information Engineering, Xi’an University of Posts & Telecommunications, Xi’an 710121, Shaanxi, China
  • Received:2023-01-01 Online:2022-06-14

摘要:

针对浅海环境干涉复杂和非稳定特性,建立概率密度函数形式的声场模型,有效克服定位时的模型失配问题;设计以声源状态矢量为后验概率的贝叶斯定位模型,用迭代形式达到时间换空间的目的,实现单个水听器对运动目标的定位;提出网格划分直方图滤波法,将解析积分转化成数值求和,提高算法效率。SWellEx-96实测数据验证结果表明,深度200m、距离10km的范围内,深度定位相对误差可控制在12.04%,距离定位相对误差可控制在6.47%。新方法可用于浅海隐蔽低耗武器平台对移动目标的探测。

关键词: 水下武器平台, 浅海, 贝叶斯估计, 水声定位, 概率密度

Abstract:

An acoustic field model in the form of probability density function is established to solve the problem of model mismatch in shallow water. The Bayesian localization model with the state vector of an acoustic source as a posteriori probability is designed to achieve the purpose of exchanging time for space by iteration and realize the localization of a moving target by a single hydrophone. The grid histogram filtering algorithm is proposed to convert analytical integral into numerical summation and improve efficiency of the algorithm. The SWellex-96 experimental results show that the relative error of depth localization can be controlled at 12.04% and the relative error of distance localization at 6.47% within a depth of 200m and a distance of 10km. The proosed method can be used to detect targets in shallow water with concealed low-energy consumption weapon platform.

Key words: underwater weapon platform, shallow water, Bayesian estimation, underwater acoustic location, probability density