Moving target feature extraction for airborne high-range resolution phased-array radar

We study the feature extraction of moving targets in the presence of tempor ally and spatially correlated ground clutter for airborne high-range resolu tion (HRR) phased-array radar, To avoid the range migration problems that o ccur in HRR radar data, we first divide the HRR range profiles into low-ran ge resolution (LRR) segments. Since each LRR segment contains a sequence of HRR range bins, no information is lost due to the division, and hence, no loss of resolution occurs, We show how to use a vector auto-regressive (VAR ) filtering technique to suppress the ground clutter. Then, a parameter est imation algorithm is proposed for target feature extraction, From the VAR-f iltered data, the target Doppler frequency and the spatial signature vector s are first estimated by using a maximum likelihood (ML) method. The target phase history and direction-of-arrival (DOA) (or the array steering vector for unknown array manifold) are then estimated from the spatial signature vectors by minimizing a weighted least squares (WLS) cost function. The tar get radar cross section (RCS)-related complex amplitude and range-related f requency of each target scatterer are then extracted from the estimated tar get phase history by using RELAX, which is a relaxation-based high-resoluti on feature extraction algorithm. Numerical results are provided to demonstr ate the performance of the proposed algorithm.