A novel reconstruction approach for high-resolution demodulation spectrum with group-sparsity characteristics

Abstract

There exists a distinct envelope modulation of the radiated noise of underwater acoustic target because of the propeller beat. These parameters related with envelop modulation imply a wealth of physical information such as the speed of the propeller. Therefore, the detection of envelope modulation on noise (DEMON) characteristics is critical for classifying and recognizing the underwater acoustic target. In this study, a novel high-resolution reconstruction approach of DEMON is proposed by exploiting its group sparsity across subbands compared with the drawback in Fourier transform (FT)-based DEMON. On the one hand, the estimation of sparse DEMON is converted into solving underdetermined equation in the inverse Fourier basis by exploiting the sparsity of DEMON; on the other hand, the reconstruction method can be improved and developed using the exploitation of the group sparsity across subbands. Unlike the non-sparse FT-based DEMON, which requires a further detection of line spectrum for classification or recognition, our proposed DEMON is sparse and directly provides the line spectrum. It effectively avoids the threshold choice in the detection and artificial interference in the feature extraction. Furthermore, the proposed method is developed in the non-parametrical sparse Bayesian learning framework, so it has the capability of learning the sparsity of DEMON automatically.