Seabed target detection under the constraint of mixed 1-norm and total variation

This paper presents an innovative sparse-constrained arrival estimation algorithm using a weighted sum of the 1-norm and total variation constraints as a regularization term. The proposed algorithm achieves high resolution under the 1-norm sparsity constraint and enhances the target's edge features and internal smoothness under the total variation constraint. In addition, the proposed algorithm can improve both the direction of arrival resolution and the estimation performance of the direction of arrival extended targets. Further, the proposed algorithm is extended to the broadband scenes, where the received signal is transformed back to the time domain after being divided into sub-bands in the frequency domain. The obtained time-domain signals are processed by narrowband algorithms. The main advantage of the proposed method is that it avoids the loss of range resolution caused by conventional piecewise processing. The effectiveness of the proposed algorithm is verified by simulation experiments in a seabed exploration scenario. The experimental results show that the proposed algorithm not only can enhance the azimuth resolution but can also reduce the total square error compared to the commonly-used algorithms. Finally, the results demonstrate that adding the total variation constraints to the sparse-constrained direction of the arrival estimation algorithm can preserve the structural properties of targets and effectively improve the detection performance of large azimuth-expanding targets on the seabed.