CoFAR Clutter Channel Estimation Via Sparse Bayesian Learning

A cognitive fully adaptive radar (CoFAR) alters its behavior autonomously to accomplish desired tasks. The knowledge of the target environment is essential to the efficient operation of CoFAR. In this work, we consider the enhanced environment sensing aspect and study the problem of clutter channel impulse response (CIR) estimation in CoFAR. Using the high-fidelity modeling and simulation tool RFView, we show that the clutter CIR is sparse. Subsequently, we propose a sparse Bayesian learning (SBL) framework for estimating the underlying sparse clutter CIR, which does not require the a priori knowledge of the unknown clutter CIR's sparsity profile. Further, we derive the Bayesian Cramér-Rao bound (BCRB) for the proposed method and show the effectiveness of the proposed SBL-based clutter channel estimation method by comparing its performance with the derived BCRB.