Adaptive subspace detection based on two-step dimension reduction in the underwater waveguide

• 作者: De-zhi Kong Chao Sun Ming-yang Li Lei Xie
• 来源期刊: 防务技术
• 年，卷（期）: 2021,（4）
• 页码范围: 1414-1422
• 页数: 9页
• 语种: 英文
• DOI: 10.3969/j.issn.2214-9147.2021.04.027

摘要

In the underwater waveguide, the conventional adaptive subspace detector (ASD), derived by using the generalized likelihood ratio test (GLRT) theory, suffers from a significant degradation in detection per-formance when the samplings of training data are deficient. This paper proposes a dimension-reduced approach to alleviate this problem. The dimension reduction includes two steps: firstly, the full array is divided into several subarrays; secondly, the test data and the training data at each subarray are transformed into the modal domain from the hydrophone domain. Then the modal-domain test data and training data at each subarray are processed to formulate the subarray statistic by using the GLRT theory. The final test statistic of the dimension-reduced ASD (DR-ASD) is obtained by summing all the subarray statistics. After the dimension reduction, the unknown parameters can be estimated more accurately so the DR-ASD achieves a better detection performance than the ASD. In order to achieve the optimal detection performance, the processing gain of the DR-ASD is deduced to choose a proper number of subarrays. Simulation experiments verify the improved detection performance of the DR-ASD compared with the ASD.