title: Quasi-Newton algorithm for adaptive minor component extraction
abstract: An adaptive quasi-Newton algorithm is first developed to extract a single minor component corresponding to the smallest eigenvalue of a stationary sample covariance matrix. A deflation technique instead of the commonly used inflation method is then applied to extract the higher-order minor components. The algorithm enjoys the advantage of having a simpler computational complexity and a highly modular and parallel structure for efficient implementation. Simulation results are given to demonstrate the effectiveness of the proposed algorithm for extracting multiple minor components adaptively
keyphrases: quasi-Newton algorithm adaptive minor component extraction eigenvalue stationary sample covariance matrix deflation technique higher-order minor components computational complexity parallel structure simulation results modular structure adaptive estimation DOA estimation ROOT-MUSIC estimator