Kalman filtering method for sparse off-grid angle estimation for bistatic multiple-input multiple-output radar

In order to address the off-grid angular estimation of direction of departure and direction of arrival of a target for bistatic multiple-input multiple-output radar, a novel method involving the combined effect of compressive sensing theory and an optimal estimation algorithm is proposed. The proposed method, named as simultaneous orthogonal matching pursuit with Kalman filtering (SOMP-KF) first exploit the sparsity of the target in the spatial domain by discretising the area of detection to formulate a dictionary matrix. Sparse sampling created during the discretisation of the off-grid space leads to a remodelling of the problem where a linearisation technique that inculcates a grid-varying position vector is applied to the Kalman filtering method. The modified Kalman filtering method resolves the off-grid offset, which hence results in achieving the off-grid angle estimation objective. Additionally, the Cramer-Rao lower bounds are derived theoretically for all parameters to explain the estimation performance. Experimental analysis against existing methods indicates the proposed SOMP-KF effectiveness in improving the angle estimation of target whiles, maintaining a minimal computational cost than its competitors.