RLS-Based Adaptive Time-Varying RCS Estimation and Imaging in MIMO Radar Systems

This paper presents recursive least squares (RLS)-based adaptive techniques and the pertinent analysis for time-varying radar cross section (RCS) estimation along with 2D imaging in monostatic MIMO radar systems. Initially, a block-RLS (BRLS) algorithm is developed for RCS estimation and 2D imaging for a scenario with an unknown number of targets present in the radar scanning region with unknown angles and ranges. This is followed by a fast BRLS (FBRLS) scheme, which has faster convergence with improved estimation as well as imaging performances in comparison to the BRLS. Convergence analysis for the resulting mean squared observation and estimation errors, as well as computational complexities of the proposed algorithms, are also presented. Finally, a simulation based comparative study illustrates the improved estimation, convergence and imaging performance of the proposed schemes in comparison to the existing LMS-based schemes, while also validating the analytical results.