Space-Time Adaptive Processing for Airborne Radar

摘要

Future airborne radars will be required to detect targets in an interferencebackground comprised of clutter and jamming. Space-time adaptive processing (STAP) refers to multidimensional adaptive filtering algorithms that simultaneously combine the signals from the elements of an array antenna and the multiple pulses of a coherent radar waveform, to suppress interference and provide target detection. STAP can improve detection of low-velocity targets obscured by mainlobe clutter, detection of targets masked by sidelobe clutter, and detection in combined clutter and jamming environments. This report analyze. a variety of approaches to STAP problem. Optimum, or fully adaptive processing is reviewed. Computational complexity and the need to estimate the interference from a limited amount data available data make fully adaptive STAP impractical. As a result, partially adaptive share-time processors are required. A taxonomy of reduced-dimension STAP algorithms is presented where algorithms are classified based on the type of preprocessor employed. For example, beamspace algorithms use spatial preprocessing, while post-Doppler approaches perform (Doppler) filtering before adaptive processing. In some cases, the specical structure of the clutter can be exploited to design preprocessors yielding minimum clutter tank. For each class, either sample-matrix-inversion (SMO) or subspace-based weight computation may be employed. Simulation results are presented to illustrate various performance metrics, including SINR adapted patterns, minimum detectable velocity, and required degrees of freedom.