Reverberation suppression using non-negative matrix factorization to detect low-Doppler target with continuous wave active sonar

Abstract

In active sonar systems, the detection of echo from targets can deteriorate due to reverberation. Detection becomes more difficult if targets have low-Doppler frequency and are located near the reverberation band, especially in an environment with low signal-to-reverberation ratio. In this paper, we propose an algorithm for the reverberation suppression of continuous wave signals using non-negative matrix factorization. To extract the target echo signal mixed with reverberations, the bases for the target echo and the reverberation are independently defined, and different constraints are applied for their corresponding estimation. We also derive constraints on temporal continuity and temporal length to estimate bases for the target echo. Experiments using simulated reverberations are performed to evaluate the proposed algorithm, and the results show an enhancement in the signal-to-noise ratio by 6-15 dB, as well as in the detection probability at several signal-to-reverberation ratios. Moreover, an experiment is conducted using reverberation measured from an ocean, and the results show that the proposed algorithm can effectively suppress reverberation and enhance detection performance in practical settings.

Feature Extraction Based on the Non-Negative Matrix Factorization of Convolutional Neural Networks for Monitoring Domestic Activity With Acoustic Signals

“…3 (a). By focusing on these characteristics of the NMF method in the acoustic signals, several NMF applications have been developed, e.g., the speech denoising [28], [29] and the active sonar reverberation suppression [44], as shown in Fig. 3 (b).…”

Linear Frequency Modulated Reverberation Suppression Using Non-Negative Matrix Factorization Methods, Dechirping Transformation and Modulo Operation

“…3 presents the spectrogram of LFM reverberation with a buried target echo. Because the reverberation spectrum masks the target echo signal, it is difficult to recognize that the target is present in this spectrogram (indicated by the red arrow) [1,2,17].…”

“…. (12) Recently Lee and Lim proposed a CW reverberation suppression algorithm using an NMF method [17]. To establish the frame of the algorithm, they first divided the input signal spectrogram into the target echo and reverberation parts, and then applied different constraints to each basis.…”

“…Perform inverse STFT and apply rechirping transformation using (37) 3.3 Finally, the target echo signal can be reconstructed using (38) the update rules to utilize the sparsity of the target echo basis matrix. The constraints are the same as those of the conventional method [17]. However, the update rule to utilize the sparsity is an original contribution of this study.…”

“…Recently, Lee et al proposed a CW reverberation suppression algorithm based on non-negative matrix factorization (NMF) [17]. The NMF algorithm decomposes a non-negative matrix into two non-negative matrices with the advantage of representing the sparse features of the matrix [18,19,20].…”