A Modal-Domain Adaptive Subspace Detector in a Deep-Sea Environment

In deep-sea environments, the conventional adaptive subspace detector (ASD) is realized in the hydrophone domain by applying the generalized likelihood ratio test (GLRT), in which acoustic signals lie in lower-dimensional modal subspaces. When the number of snapshots in training data are deficient, ASD detection performance degrades significantly. This paper proposes a modal-domain ASD (MD-ASD) to alleviate the snapshot deficiency problem. In the MD-ASD procedure, the test and training data are mapped into the modal domain before proceeding to the GLRT; thus, the MD-ASD procedure is treated in a lower dimension and has a lower computational burden than the ASD procedure. Derivation of the MD-ASD distribution reveals the performance of the MD-ASD converges to that of the corresponding matched subspace detector (MSD). Utilizing the property of the acoustic signal and ambient noise lying in a common modal subspace, we demonstrate that the unknown parameters of the MD-ASD procedure achieve better estimation accuracies than the ASD procedure. The MD-ASD also obtains a larger output signal-to-noise ratio than the ASD, thus outperforming the ASD in detection performance, especially for the deficient training data case. Numerical simulations validate the improved detection performance of our proposed detector compared with the ASD.