Keywords :
Bayesian Cramér-Rae bound; clutter map; cognitive fully adaptive radar; RFView; sparse Bayesian learning; Adaptive radar; Bayesian; Bayesian crame-rae bound; Bayesian learning; Channel impulse response; Clutter maps; Cognitive fully adaptive radar; Rfview; Sparse bayesian; Sparse bayesian learning; Computer Networks and Communications; Signal Processing; Instrumentation; Bayesian Cramer-Rao bound
Abstract :
[en] 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.
Name of the research project :
European Office of Aerospace Research & Development, part of the US Airforce Office of Scientific Research
Funding text :
This work from the University of Luxembourg is partially supported by the grant on ”Active Learning for Cognitive Radars” from the European Office of Aerospace Research & Development, part of the US Airforce Office of Scientific Research.
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