Sea–Land Segmentation Algorithm Based on Multiframe Radar Echoes

In modern radar applications, data-driven sea–land segmentation of complex environments based directly on radar returns without the help of electronic charts and other information has attracted researchers’ attention due to the solidification of exogenous information and defect of nonreal timeness. However, the sea–land segmentation based on radar echoes still faces two problems: first, complex clutter environment with mixed ground and sea clutter leads to a large dynamic range of clutter power, which makes power-based segmentation unreliable; and second, stable segmentation features are difficult to obtain when echoes in a single scan period are limited. In the case of multiframe radar echoes, this article first unwraps the phases of two adjacent echo sequences and extracts a new similarity measure for sea–land segmentation based on the covariance of phase difference sequences between sea clutter and ground clutter. Then, inspired by the idea of iteration, this article proposes a method to iterate the covariance matrix by iterating the clutter map of the characteristic differences of multiframe echoes to distinguish between sea and ground clutter more effectively. Experimental results based on the measured data show that the proposed sea–land segmentation method based on multiframe echoes can effectively separate sea and land areas and ensure the quality of the segmentation results, and after several consecutive scan periods, the proposed sea–land segmentation method is more accurate and robust than other sea–land segmentation methods.