Low complexity sparse Bayesian estimation for UWB radar imaging

Ultra-wideband (UWB) radar imaging can provide high-resolution images of obscured objects using radio-frequency signals. Due to its vast applications, UWB radar imaging received considerable attention in the past decade. Compressive sensing (CS) has been used as a viable solution for the larger data required by radar imaging. The advances of CS-based UWB-radar imaging is burdened by the complexity of the reconstruction algorithms and their weak noise immunity. Exploiting the structure of the basis-matrix, a low-complexity Bayesian-based estimation algorithm is proposed. The algorithm takes advantage of the radar-return statistic to find an approximate minimum mean-square error estimate of the radar image. The low complexity is achieved by utilising the block-matrix-inversion formula to execute the algorithm in an order-recursive manner. Further simplification is achieved by using exponential-sum formula to find the correlation between the columns of the basismatrix. The proposed algorithm is evaluated over experimental and simulated data. The results show faster processing time compared to other known algorithms, with comparable reconstruction quality.