Group Sparsity Based Higher Order Sparse Spectrum Fitting Approach for USV Localization

The problem of unmanned surface vehicle (USV) localization is investigated in this paper. We develop a group sparsity based higher order sparse spectrum fitting (HOSpSF) approach to improve the accuracy of localization. In the proposed approach, the off-grid issue, and advantages of group sparse are combined. Firstly, a higher-order SPSF method is proposed to solve the off-grid DOA estimation problem by using the second-order Taylor approximation. Next, we present a USV localization approach that optimizes all sensor array nodes by incorporating the group sparsity theory. Simulation results demonstrate the effectiveness and superiority of the proposed method against the state-of-the-art arid-based approaches.