The unbiased gradient type LS algorithm for adaptive spectrum estimation

A gradient-type-least-squares (LS) algorithm for adaptive implementation of Pisarenko´s method is presented. The algorithm, which uses the Lagrange multiplier technique for parameter updating, has faster convergence and improved tracking capability when compared to a least-mean-squares-type algorithm. Moreover, since the gradient-type LS algorithm does not use an approximation for deriving the updated weight vector, tracking errors in the transient region are smaller than with Reddy´s LS algorithm. Convergence analysis of the gradient-type LS algorithm in the vicinity of a stationary point shows that it is unbiased