融入先验知识的MIMO声呐自适应检测方法

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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 中图分类号：TN911.7 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.07.3

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.

Keywords：multiple-input multiple-output （MIMO） sonar；adaptive detection； Gaussan background; inverse complex Wishart distribution；Bayesian framework

0 引言

随着应用于通信中的多输入多输出（multiple-inputmultiple-output，MIMO）模式被引入声呐系统，MMO声呐成为近年来的研究热点[1-4]。（剩余12560字）