An Estimation of the Performance of HRLS Algorithm

The householder RLS (HRLS) algorithm is an O(N) algorithm which recursively updates an arbitrary square-root of the input data correlation matrix and naturally provides the LS weight vector. A data dependent householder matrix is applied for such an update. In this paper a recursive estimate of the eigenvalue spread and misalignment of the algorithm is presented at a very low computational cost. Misalignment is found to be highly sensitive to the eigenvalue spread of input signals, output noise of the system and exponential window. Simulation results show noticeable degradation in the misalignment by increase in eigenvalue spread as well as system’s output noise, while exponential window was kept constant. Keywords—HRLS algorithm, eigenvalue spread, misalignment.