A Dimension-Reduced Modal Space Detector in Deep-Sea Environment

Kong, Dezhi; Sun, Chao; Liu, Xionghou; Li, Mingyang; Xie, Lei

doi:10.1109/oceanskobe.2018.8559405

Cited by 2 publications

(4 citation statements)

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“…7. Therefore, those factors that increase the dimension of the signal subspace discussed in [13] will weaken the improvement effect of our proposed MD-ASD.…”

Section: Estimation Accuracymentioning

confidence: 98%

“…In the deep ocean, the VLA only covers a small portion of the water column. Consequently, the mode depth functions are extremely undersampled, which leads to a fraction of very small singular values in the mode matrix [13], [14]. Noting that N M in the deep ocean, we represent the mode matrix by its singular value decomposition (SVD)…”

Section: A Signal Modelmentioning

confidence: 99%

“…The case of p = N essentially produces an energy detector. In this paper, we mainly investigate the case of p < N , which is often encountered in the deep ocean [13].…”

Section: A Signal Modelmentioning

confidence: 99%

“…The adaptive detector in [3] applied to a shallow-water waveguide is essentially the multidimensional version of the Kelly detector in which the signal subspace is the modal space. In the deep sea, although the number of normal modes excited by a source is far greater than the number of hydrophones, it is quite possible that the signal still lies in a lower-dimensional subspace, called an effective modal subspace, with a dimension less than the hydrophone number [13]. This situation occurs when the array only covers a small portion of the water column, and thus, the normal modes are extremely undersampled [14].…”

Section: Introductionmentioning

confidence: 99%

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

Kong

Sun

et al. 2019

IEEE Access

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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. INDEX TERMS Adaptive subspace detector (ASD), deep-sea environment, generalized likelihood ratio test (GLRT), modal domain.

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“…7. Therefore, those factors that increase the dimension of the signal subspace discussed in [13] will weaken the improvement effect of our proposed MD-ASD.…”

Section: Estimation Accuracymentioning

confidence: 98%

Section: A Signal Modelmentioning

confidence: 99%

“…The case of p = N essentially produces an energy detector. In this paper, we mainly investigate the case of p < N , which is often encountered in the deep ocean [13].…”

Section: A Signal Modelmentioning

confidence: 99%