Performance analysis of the self-orthogonalizing adaptive lattice filter

This paper describes new self-orthogonalizing adaptive lattice filter (SALF) structures to enhance slow convergence rates caused by eigenvalue spread. Firstly, we propose the variable stepsize self-orthogonalizing adaptive lattice filtering (VSALF) structure to speed up the convergence rate of partial correlation (PARCOR) coefficients. Secondly, the partial self-orthogonalizing adaptive lattice filtering (PSALF) structure is proposed in order to enhance the tracking ability for nonstationary environments. Moreover, the PSALF structure can reduce computational complexity whilst maintaining a fast convergence rate. A performance analysis based on the convergence model of the lattice predictor is given in terms of mean-squared error and variance of the PARCOR coefficient error. Computer simulations are undertaken to verify the performance and applicability of the proposed filter structures.