A hybrid STAP approach to target detection for heterogeneous scenarios in radar seekers
Adaptive detection of moving targets on the sea is important for radar seekers. Recently, more attention has been paid to the deleterious effect of clutter heterogeneity on space-time adaptive processing (STAP) for pulse Doppler radar. Since secondary samples are no longer statistically independent and identically distributed (IID) in heterogeneous environments, this is subjected to a great challenge to target detection for radar seekers. Due to the fact that chaff jamming severely affects the performance degradation of target detection, the hybrid detection algorithm is proposed to suppress the sea clutter and chaff jamming. Firstly, the range cells can be classified into two regions according to the power, namely clutter region and hybrid region. Then we propose different algorithms to process two regions. The fixed point (FP) estimator is used to estimate the clutter covariance matrix in clutter region. While the power selected training (PST) algorithm is used to select the homogeneous secondary samples, and an algorithm based on two-step subspace projection for hybrid interference suppression is presented in hybrid region. Finally, the proposed Pareto-based generalized likelihood ratio test (PBGLRT) detector can detect the slowly moving targets in heterogeneous interference. Simulation results show that the PBGLRT detector outperforms both the low rank normalized adaptive match filter (LRNAMF) and normalized adaptive match filter (NAMF) detectors against interference heterogeneity.
space-time adaptive processing generalized likelihood ratio test normalized adaptive match filter sample matrix inversion exponentially embedded family minimum description length