Robust STAP algorithm based on knowledge‐aided SR for airborne radar

Space–time adaptive processing (STAP) for airborne radar employs training snapshots to estimate a clutter covariance matrix (CCM). However, the resulting estimated covariance matrix can be corrupted by a target-like signal (outlier), which leads to a further target self-nulling phenomenon. When these outliers are dense, STAP performance degradation is most severe. To cope with this problem, a novel robust STAP algorithm based on knowledge-aided sparse recovery (SR) is proposed, which can eliminate the influence of dense outliers on target detection. This algorithm exploits the property that the clutter components of side-looking airborne radar are distributed along the clutter ridge, which is used to distinguish clutter components and outliers. Each snapshot is decomposed by the SR method, and then the similar degree function is defined to recognise and select clutter components via a threshold. Subsequently, the CCM is estimated by the select clutter components. Therefore, this algorithm can select appropriate coefficients and space–time steering vectors to assess clutter accurately. Monte Carlo experiments affirm that the proposed algorithm has advantages in robustness and target detection over other conventional STAP algorithms [e.g. samples matrix inverse-STAP, prolate spheroidal wave function-generalised inner product, and SR-STAP algorithms] in heterogeneous clutter environments.