An Efficient Algorithm for Mutiple Direction of Arrival Tracking Based on the Constrained Projection Approximation Approach and Kalman Filter

In this paper, we present a direction of arrival (DOA) tracking scheme involving a subspace tracking algorithm and a Kalman filter. The proposed subspace tracking algorithm is based on an interpretation of the signal subspace as the solution of a minimization of a constrained projection approximation task. We show that we can apply the matrix inversion lemma to solve this problem recursively. The proposed algorithm avoids the orthonormalization process after each update for post-processing algorithms which need orthonormal basis of the signal subspace. The DOA's are obtained via a Newton-type method initialized with the DOA's predicted by the Kalman filter. The tracking capability of the proposed algorithm is verified by computer simulations in a scenario involving targets with crossing trajectories.