融入先验知识的MIMO声呐自适应检测方法

doi:10.12305/j.issn.1001-506X.2025.07.30

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (7): 2383-2388.doi: 10.12305/j.issn.1001-506X.2025.07.30

• 通信与网络 • 上一篇

融入先验知识的MIMO声呐自适应检测方法

马治勋¹^,², 殷超然¹, 王天琪¹, 郝程鹏¹^,*

1. 中国科学院声学研究所, 北京 100190
2. 中国科学院大学电子电气与通信工程学院, 北京 100049

收稿日期:2024-08-23 出版日期:2025-07-16 发布日期:2025-07-22
通讯作者: 郝程鹏
作者简介:马治勋 (1991—), 男, 助理研究员, 硕士, 主要研究方向为水声信号处理、自适应目标检测
殷超然 (1996—), 男, 助理研究员, 博士, 主要研究方向为统计信号处理、自适应目标检测
王天琪 (1997—), 女, 助理研究员, 博士, 主要研究方向为目标检测、水声信号处理
郝程鹏 (1975—), 男, 研究员, 博士, 主要研究方向为水声信号处理、信号检测与估计

Adaptive detection method of MIMO sonar incorporating prior knowledge

Zhixun MA¹^,², Chaoran YIN¹, Tianqi WANG¹, Chengpeng HAO¹^,*

1. Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2024-08-23 Online:2025-07-16 Published:2025-07-22
Contact: Chengpeng HAO

摘要/Abstract

摘要：

为了提高多输入多输出声呐在高斯噪声和混响背景下的检测性能, 提出了一种融入先验知识的贝叶斯自适应检测方法。考虑一种高斯噪声和混响共存的干扰场景, 首先引入贝叶斯理论, 将未知混响协方差矩阵建模为逆复Wishart分布的随机矩阵; 其次, 联合利用两组训练数据, 设计一种两步式干扰协方差矩阵估计方法; 最后, 利用干扰协方差矩阵估计值代替其真实值, 得到基于贝叶斯框架的自适应匹配滤波器。仿真结果表明, 所提出的检测方法能够更准确地实现对干扰协方差矩阵的估计, 并且在训练数据不足时, 该方法具有稳健的检测性能。

关键词: 多输入多输出声呐, 自适应检测, 高斯背景, 逆复Wishart分布, 贝叶斯框架

Abstract:

In order to improve the detection performance of multiple-input multiple-output (MIMO) sonar in Gaussian noise and reverb background, an adaptive detection method incorporating priori knowledge is proposed. Considering an interference scenario in which Gaussian noise and reverb coexist, firstly, Bayesian theory is introduced to model the unknown reverb covariance matrix as a random matrix with inverse complex Wishart distribution. Secondly, two sets of training data are jointly exploited to devise a two-step estimation method of the interference covariance matrix. Finally, interference covariance estimate is used in place of its true value and the adaptive matched filter is obtained under the Bayesian framework. The simulation results show that the proposed detection method can achieve more accurate estimation of the interference covariance matrix and has a robust detection performance when the training data is insufficient.

Key words: multiple-input multiple-output (MIMO) sonar, adaptive detection, Gaussian background, inverse complex Wishart distribution, Bayesian framework

中图分类号:

TN911.7

马治勋, 殷超然, 王天琪, 郝程鹏. 融入先验知识的MIMO声呐自适应检测方法[J]. 系统工程与电子技术, 2025, 47(7): 2383-2388.

Zhixun MA, Chaoran YIN, Tianqi WANG, Chengpeng HAO. Adaptive detection method of MIMO sonar incorporating prior knowledge[J]. Systems Engineering and Electronics, 2025, 47(7): 2383-2388.

图/表 5

参考文献 33

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融入先验知识的MIMO声呐自适应检测方法

Adaptive detection method of MIMO sonar incorporating prior knowledge

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