Cognitive antenna selection for DOA estimation in automotive radar

The performance of direction-of-Arrival (DOA) estimation is directly determined by the array aperture and the number of elements in the array. In many problems, a large array is available, but the number of receiving channels is limited, either by the system cost, or by the complexity. In this work, we address the problem of DOA estimation with a large number of antenna array elements and a small number of receivers, where the receivers are connected to the array elements via a reconfigurable switching matrix. A cognitive approach, named cognitive antenna selection (CASE), for sequentially switching the elements of the sensor array based on history observations and prior information is proposed. The antenna elements selection is via minimization of the conditional Bobrovski-Zakai bound on the mean-squared-error (MSE) of the DOA estimate. The performance of the proposed algorithm is evaluated in a problem of two sources via simulations and compared to two other non-Adaptive approaches. The proposed algorithm outperforms both of the considered algorithms in terms of MSE both asymptotically and in the threshold region.