Low-Frequency Noise Suppression Based on Mode Decomposition and Low-Rank Matrix Approximation for Underwater Acoustic Target Signal

Marine ambient noise can negatively affect underwater acoustic target (UWAT) recognition. Previous related studies have focused on the suppression of high-frequency noise. However, marine ambient noise in the frequency domain is concentrated at low frequencies, overlapping with the signal components of UWATs. Low-rank (LR) matrix approximation is an effective class of denoising methods, but its direct application on UWAT signals tends to result in the loss of weak signal components. To better suppress low-frequency noise, we propose a denoising method based on mode decomposition and LR matrix approximation. This method first decomposes the UWAT signal into a series of modes using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), which disperses the signal components into different modes thus emphasizing weak signal components. Subsequently, an adaptive dual judgment method based on amplitude-aware permutation entropy (AAPE), cosine similarity (CS), and K-means++ is applied to all modes to identify the signal modes and then discard the noise modes for initial denoising. Finally, an improved OptShrink algorithm which can adaptively choose the rank by clustering and shrink singular values is proposed to extract the LR signal matrix for each signal mode and further suppress the low-frequency noise in the signal modes. Experimental results on the ShipsEar dataset show that our method can effectively suppress low-frequency noise. More importantly, the difference between UWATs with different labels is also enhanced after employing our proposed method.