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 performance 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.

在水下波导中，使用广义似然比检验（GLRT）理论推导出的传统自适应子空间检测器（ASD）在训练数据采样不足时检测性能显着下降。本文提出了一种降维方法来缓解这个问题。降维包括两个步骤：首先，将整个数组划分为若干个子数组；其次，将每个子阵列的测试数据和训练数据从水听器域转换到模态域。然后对每个子阵列的模态域测试数据和训练数据进行处理，利用 GLRT 理论制定子阵列统计量。降维 ASD (DR-ASD) 的最终检验统计量是通过对所有子阵列统计量求和获得的。降维后，可以更准确地估计未知参数，因此 DR-ASD 实现了比 ASD 更好的检测性能。为了达到最优的检测性能，推导了DR-ASD的处理增益来选择适当数量的子阵列。仿真实验验证了 DR-ASD 与 ASD 相比改进的检测性能。