Direction finding of bistatic MIMO radar based on quantum-inspired grey wolf optimization in the impulse noise

Abstract A novel direction-finding method is proposed for bistatic multiple-input-multiple-output (MIMO) radar in the impulse noise in this paper. The method has the capacity to suppress the impulse noise by means of infinite norm normalization and can obtain better performance for direction finding via the weighted signal subspace fitting algorithm. To solve the objective function of this method, we devise a quantum-inspired grey wolf optimization algorithm to acquire the global optimal solution. The proposed method based on QGWO can resolve the direction-finding difficulties of bistatic MIMO radar. Monte-Carlo experiments have confirmed the robustness and superiority of the proposed method for locating independent and coherent sources with a small number of snapshots in the impulse noise compared with some existing direction-finding methods in a series of scenarios. In addition, we present the Cramér-Rao bound (CRB) of angle estimation for bistatic MIMO radar in the impulse noise, which generalizes the Gaussian CRB for performance analysis