Study on an anti-reverberation method based on PCI-SVM. (November 2021)

• Record Type: Journal Article
• Authors: Wang, Maofa; Wu, Shengjie; Guo, Shengming; Peng, Dayong
• Is Part Of: Applied acoustics. Volume 182(2021)
• Publication Date: 2021-11
• Subjects: Active sonar -- Principal Component Inversion (PCI) -- Support Vector Machine (SVM) -- Anti-reverberation; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2021.108189
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: Aiming at the problem that targets are difficult to detect in the marine reverberation environment, this paper studies traditional PCI methods, AR pre-whitening methods, and low-rank matrix decomposition methods, but because traditional PCI methods are difficult to rank, AR pre-whitening methods are difficult to determine the order, and low-rank Matrix factorization method is slow in calculation speed. Based on this research, this paper proposes an anti-reverberation method based on PCI-SVM. This method combines Principal Component Inversion (PCI) and Support Vector Machine (SVM), first obtains the eigenvalues by the principal component inversion algorithm, and then uses the support vector machine to classify and rank the eigenvalues, thereby receiving active sonar the received echo signal is decomposed into subspace to realize the separation of reverberation subspace. This paper uses this method to process simulation data, lake trial data, and sea trial data separately and compares the data processing results with the direct matched filtering method. The comparison results show that the reverberation data processed by the PCI-SVM method has signal detection capabilities Increase by 3.8 dB, 12 dB, and 1.9 dB respectively. At the same time, the PCI-SVM method is compared with the traditional AR anti-reverberation method and PCI anti-reverberation method. The comparison results show that the PCI-SVM method adopted in this paper has better robustness and anti-reverberation ability.