Ship detection in strong clutter environment based on adaptive regression thresholding for hfswr

Abstract

High Frequency Surface Wave Radar (HFSWR) has the ability to detect and track ships in real time and beyond the horizon continuously. The method of adaptive power regression thresholding (APRT) is one of good adaptive detection methods for HFSWR, and it works well in most cases. But in strong clutter environment, some strong clutters with high amplitude will raise the noise level, which would overestimate the detection threshold along Doppler cells or range cells and cause some ships become undetectable. This paper proposes an improved method to solve the problem appearing in strong clutter environment based on APRT method. It adds a process of strong clutter suppression before ship detection to eliminate its influence on estimation of noise level and detection threshold. The processing results of real measured HFSWR data show that the improved method can effectively improve the performance of detector under strong sea clutter environment.