A Robust Target Parameter Extraction Method via Bayesian Compressive Sensing for Noise MIMO Radar

This paper explores the theory of Compressive Sensing (CS) in radar and evaluates the perturbing effect on measurement noise, channel inference and radar system accuracy error. The performance of traditional Compressive Sensing Radar (CSR) are sensitivity to the above perturbations, which causing the mismatch between non-adaptive random measurement and sensing matrix. To solve the problem, a robust algorithm via Bayesian Compressive Sensing (BCS) with application to noise MIMO radar is proposed. First, a noise MIMO radar sparse sensing model is established and the jointly probability density function based on sparse Bayesian model is derived. Then the BCS algorithm and Least-Absolute Shrinkage and Selection Operator (LASSO) algorithm are employed to optimize the jointly probability density function. Comparing with traditional CSR algorithms, this method estimates effectively the parameters of target when existing mismatch in CSR model, reduces the target information estimation error, and enhances the accuracy and robustness of CSR target information extraction. The validity of the proposed method is illustrated by numerical example.