Performance analysis of reduced-rank STAP

The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. In particular, it was shown that when the space-time covariance matrix is estimated from a dataset with limited support, reduced-rank methods outperform full-rank space-time adaptive processing (STAP). We study the application of several reduced-rank methods to the STAP problem and demonstrate their utility by simulations in terms of the output signal-to-noise ratio and detection probability. It is shown that reduced-rank processing has two opposite effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio. Several reduced-rank methods are analyzed and compared for both cases of known and unknown covariance matrix. While the best performance is obtained using transforms based on the eigendecomposition (data dependent), the loss incurred by the application of fixed transforms (such as the discrete cosine transform) is relatively small. The main advantage of fixed transforms is the availability of efficient computational procedures for their implementation. These findings suggest that reduced-rank methods could facilitate the development of practical, real-time STAP technology