Subspace Tracking in Colored Noise Based on Oblique Projection

Projection approximation subspace tracking (PAST) algorithm gives biased subspace estimation when the received signal is corrupted by colored noise. In this paper, an unbiased version of PAST is proposed for the colored noise scenario. Firstly, a maximum likelihood (ML) and minimum variance unbiased (MVUB) estimator for the clean signal is derived using simultaneous diagonalization and oblique projection. Then, we provide a recursive algorithm, named oblique PAST (obPAST), to track the signal subspace and update the estimator in colored noise. Experimental results show the effectiveness of the obPAST algorithm