Joint Angle and Velocity-Estimation for Target Localization in Bistatic mmWave MIMO Radar in the Presence of Clutter

TL;DR

This paper introduces a sparse Bayesian learning-based method for accurate joint angle and velocity estimation of targets in bistatic mmWave MIMO radar, effectively handling clutter and off-grid parameter deviations.

Contribution

The paper develops a novel AD-domain sparse Bayesian learning framework with super-resolution off-grid refinement for target localization in cluttered bistatic mmWave MIMO radar.

Findings

Proposed method outperforms existing algorithms in simulations.

Achieves near-Bayesian Cramér-Rao bound performance.

Effectively handles clutter and off-grid target parameters.

Abstract

Sparse Bayesian learning (SBL)-aided target localization is conceived for a bistatic mmWave MIMO radar system in the presence of unknown clutter, followed by the development of an angle-Doppler (AD)-domain representation of the target-plus-clutter echo model for accurate target parameter estimation. The proposed algorithm exploits the three-dimensional (3D) sparsity arising in the AD domain of the scattering scene and employs the powerful SBL framework for the estimation of target parameters, such as the angle-of-departure (AoD), angle-of-arrival (AoA) and velocity. To handle a practical scenario where the actual target parameters typically deviate from their finite-resolution grid, a super-resolution-based improved off-grid SBL framework is developed for recursively updating the parameter grid, thereby progressively refining the estimates. We also determine the Cram\'er-Rao bound (CRB) and Bayesian CRB for target parameter estimation in order to benchmark the estimation performance. Our simulation results corroborate the superior performance of the proposed approach in comparison to the existing algorithms, and also their ability to approach the bounds derived.