Dynamic discriminative compressed sensing estimation of hybrid sparse underwater acoustic channel

Exploitation of hybrid sparsity for improving underwater acoustic communication was investigated. Through the process of multipath propagation, underwater acoustic signal will experience static and time varying reflections on static or dynamic boundaries such as sea bottom or windy sea surface, which generate static multipath arrivals and time varying multipath arrivals respectively. This type of hybrid sparsity renders the traditional sparse channel estimation strategies exceptionally difficult, as the hybrid sparsity is not considered under the framework of Compressed Sensing (CS) or Dynamic Compressed Sensing (DCS). In this paper, a Dynamic Discriminative Compressed Sensing (DDCS) approach is proposed to perform different sparse estimation strategy on different type of multipath components discriminatively. Firstly, hybrid multipath arrivals are initially decomposed into static and time varying components by applying Simultaneous Orthogonal Matching Pursuit (SOMP) among continuous data block for identifying static component and Orthogonal Matching Pursuit (OMP) on its residual error for obtaining time varying component. After that the time varying and static multipath is updated by Kalman filtering CS and SOMP, respectively. At last, the whole channel response is obtained by summing up the static and time varying components. Experimental results exhibit that the proposed algorithm outperforms the classic CS or DCS estimation approached at the presence of time variations, thus demonstrating the effectiveness of hybrid sparsity exploitation in improving channel estimation.