High-Resolution Ocean Clutter Spectrum Estimation for Shipborne HFSWR Using Sparse-Representation-Based MUSIC

Abstract

The spreading of the dominant first-order Bragg lines in shipborne high-frequency surface wave radar (HFSWR) severely obscures the detection of the slow-moving targets and the measurement of ocean clutter. Space–time adaptive processing (STAP) is an effective tool for solving the problem. It normally requires a large number of independent and identically distributed (i.i.d.) training samples to estimate the ocean clutter spectrum and design the filter to eliminate the ocean clutter from the test cell. However, the training samples are insufficient due to the system limitation of shipborne HFSWR, and the stationarity of training data is destroyed in the nonstationary and nonhomogeneous ocean environment, which result in decreased performance. Thus, the estimation of the ocean clutter spectrum with small training samples or even only the test cell is an important work for shipborne HFSWR. In this paper, by exploiting the intrinsic sparsity of the ocean clutter in shipborne HFSWR, the multiple signal classification (MUSIC) algorithm based on the sparse representation technique, called SR–MUSIC, is introduced to estimate the ocean clutter spectrum. The correctness of the ocean clutter sparsity and the validity of the SR–MUSIC algorithm for the high-resolution ocean clutter spectrum estimation are verified by the simulation results.