DOA estimation and self-calibration in partially calibrated subarray-based uniform linear arrays

The problem of direction-of-arrival (DOA) estimation in partially calibrated arrays composed of multiple uniform linear arrays is studied. We assume that each subarray is calibrated well, so that the steering vectors of all subarrays are exactly known. However, the intersubarray calibration errors are unknown. The proposed method first obtains a set of initial DOA results by employing a rotational invariance property between two sets of received data, and then more accurate DOA estimation and self-calibration are performed by using the MUSIC-based method through a local searching algorithm. We also derive the corresponding stochastic Cramer-Rao bound to which we compare the performance of the proposed estimator.