KALMAN-BASED ESTIMATOR FOR DOA ESTIMATIONS

In this correspondence, we introduce a new Kalman-based noise-subspace estimator combined with the Root-MUSIC for direction-of-arrival (DOA) estimation of uncorrelated narrow-band plane waves impinging on an array of sensors. Without both a priori knowledge of the number of sources and the inflation method presented by Yang and Kaveh, we show that the Kalman-based estimator can approximately estimate the complete noise subspace with small bias for high input signal-to-noise ratio (SNR) scenario. The proposed algorithm needs slightly more computation operations per adaptive cycle than Yang and Kaveh's LMS-based algorithm, but with much more rapid convergence. Simulations demonstrate the effectiveness of the proposed algorithm.