Radar low-angle tracking with subarray level ML tracking algorithms

A subarray level maximum likelihood (ML) tracking algorithm is proposed for the radar low-angle tracking problem. The standard specular reflection model is assumed and a uniform linear array of sensors is employed as the radar antenna. The low-angle tracking problem is then treated as an angle of arrival estimation problem and the near ML estimates of the directions of both the direct and specular signals are obtained based on the data proposed. But unlike the other angle-of-arrival estimation problems, no additional iteration is needed as the directions of both the direct and specular signals change slowly with time. Therefore, the algorithm is computationally simple and effective. Its performance was demonstrated by computer simulations