Sbl-Based Joint Target Imaging And Doppler Frequency Estimation In Monostatic Mimo Radar Systems

This paper proposes a novel sparse Bayesian learning (SBL) framework towards target imaging in monostatic MIMO radar systems. Owing to the improved sparse signal recovery guaranteed by SBL, the proposed SBL-based imaging approach is seen to yield a higher resolution and significantly greater sidelobe suppression in comparison to the existing state-of-the-art non-sparse and sparse imaging techniques. Further, a novel joint SBL-based target imaging and angular Doppler frequency estimation scheme is also developed for scenarios with multiple mobile point targets and unknown angular Doppler frequencies. It is demonstrated that the Doppler frequency estimates can be obtained based on a first order Taylor series expansion of the overcomplete dictionary matrix expressed as a function of the Doppler frequencies. Simulation results are presented to validate the efficacy of the proposed techniques.