Reduced-rank space-time adaptive detection for airborne radar

For airborne radar, there are usually insufficient independent and identically distributed (IID) training data because of geometric considerations and terrain variations. The rank reduction technique is one of the most effective approaches to circumvent this problem. In this study, we investigate four reduced-rank spacetime adaptive detectors for airborne radar, namely, the reduced-rank sample-matrix-inversion (RR-SMI), the reduced-rank adaptive matched filter (RR-AMF), the reduced-rank adaptive coherence estimator (RR-ACE), and the reduced-rank generalized likelihood ratio test (RR-GLRT). Their asymptotic analytical probabilities of detection (PD’s) and false alarm (PFA’s) are all derived. These detectors all asymptotically attain a constant false alarm rate (CFAR). It is shown that these four reduced-rank detectors exhibit detection performance which is better than or comparable to that of two existing reduced-rank detectors, proposed by Reed and Gau (RG1 and RG2). Moreover, these four reduced-rank detectors are more robust to change in power of clutter and noise than RG1 and RG2.