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

• Published in: IEEE transactions on signal processing Vol. 49; no. 2; pp. 277 - 289
• Main Authors: Li, Jian, Guoqing Liu, Nanzhi Jiang, Stoica, P.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Airborne radar; Algorithms; Applied sciences; Arrays; Exact sciences and technology; Feature extraction; Frequency estimation; Grounds; History; Information, signal and communications theory; Mathematical analysis; Maximum likelihood estimation; Moving targets; Pattern recognition; Phase estimation; Phased arrays; Radar clutter; Reactive power; Services and terminals of telecommunications; Signal processing; Signatures; Spatial resolution; Studies; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telemetry. Remote supervision. Telewarning. Remote control; Telemetry: remote control, remote sensing; radar; Vectors (mathematics); High resolution; Moving target; Phased array radar; Airborne radar; Theoretical study; Signal processing; Phased array; Algorithm; Pattern extraction
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/78.902110

We study the feature extraction of moving targets in the presence of temporally and spatially correlated ground clutter for airborne high-range resolution (HRR) phased-array radar. To avoid the range migration problems that occur in HRR radar data, we first divide the HRR range profiles into low-range 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 estimation algorithm is proposed for target feature extraction. From the VAR-filtered data, the target Doppler frequency and the spatial signature vectors 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 an unknown array manifold) are then estimated from the spatial signature vectors by minimizing a weighted least squares (WLS) cost function. The target radar cross section (RCS)-related complex amplitude and range-related frequency of each target scatterer are then extracted from the estimated target phase history by using RELAX, which is a relaxation-based high-resolution feature extraction algorithm. Numerical results are provided to demonstrate the performance of the proposed algorithm.