A Low-Complexity Nonparametric STAP Detector

Phased array radars use space time adaptive processing (STAP) to detect targets in angle, range, and speed using an adaptive weight vector that depends mainly on the covariance matrix of the cell under test (CUT). This covariance matrix is estimated from the secondary cells surrounding the CUT under the assumption of homogeneous clutter and noise background. However, these secondary cells are often contaminated by multiple discrete interferers, targets or combination thereof, which degrade the estimation of the CUT's covariance matrix and, in turn, the detection performance. In this paper, we address the problem of detecting the nonhomogeneous secondary cells that need to be excluded from the adaptive weight calculation. We introduce a nonparametric and covariance-free alternative to the normalized adaptive matched filter (NAMF) test that does not need the tedious estimation process of the covariance matrix matrix of secondary cells nor prior knowledge about the interference distribution. Consequently, the computational complexity of the weight vector is reduced, which is of a great importance for real-time operation of radar systems. The equivalent robust performance of the proposed test compared to the NAMF test is demonstrated through simulations under different clutter scenarios and operation conditions.