Detection algorithms based on prediction error - additive noise - data orthogonality

Two new iterative algorithms for AR parameter estimation are presented. The first minimizes the sum of the prediction error energy and the cross covariance between prediction error and additive noise, and models the noise covariance matrix with a separate AR filter whose reflection coefficient are constrained to be sufficiently small. The second algorithm minimizes the cross covariance between the prediction error and the data. In both algorithm a steepest descent updating procedure is employed and stability of the AR filter for the stohastic processes is ensured by constraining the corresponding reflection coefficients to be less than one.