CoFAR Clutter Estimation using Covariance-Free Bayesian Learning

TL;DR

This paper introduces covariance-free Bayesian learning techniques for estimating sparse clutter channel impulse responses in adaptive radar systems, improving efficiency and accuracy over existing methods.

Contribution

It develops a novel covariance-free Bayesian framework for sparse CCIR estimation, applicable to various sparsity models, with reduced complexity and enhanced performance.

Findings

Outperforms existing methods like MFO and SOMP in accuracy.

Applicable to multiple sparsity models including group and joint sparsity.

Validated through numerical experiments and Bayesian Cramér-Rao bounds.

Abstract

A cognitive fully adaptive radar (CoFAR) adapts its behavior on its own within a short period of time in response to changes in the target environment. For the CoFAR to function properly, it is critical to understand its operating environment through estimation of the clutter channel impulse response (CCIR). In general, CCIR is sparse but prior works either ignore it or estimate the CCIR by imposing sparsity as an explicit constraint in their optimization problem. In this paper, contrary to these studies, we develop covariance-free Bayesian learning (CoFBL) techniques for estimating sparse CCIR in a CoFAR system. In particular, we consider a multiple measurement vector scenario and estimate a simultaneously sparse (row sparse) CCIR matrix. Our CoFBL framework reduces the complexity of conventional sparse Bayesian learning through the use of the diagonal element estimation rule and conjugate gradient descent algorithm. We show that the framework is applicable to various forms of CCIR sparsity models: group, joint, and joint-cum-group. We evaluate our method through numerical experiments on a data set generated using RFView, a high-fidelity modeling and simulation tool. We derive Bayesian Cram\'{e}r-Rao bounds for the various considered scenarios to benchmark the performance of our algorithms. Our results demonstrate that the proposed CoFBL-based approaches perform better than the existing popular approaches such as multiple focal underdetermined system solver and simultaneous orthogonal matching pursuit.