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.