Gauss Newton variable forgetting factor recursive least squares for time varying parameter tracking

The Gauss-Newton variable forgetting factor recursive least squares (GN-VFF -RLS) algorithm is presented, which can be used to improve the tracking cap ability in time varying parameter estimation. Compared to the existing algo rithm, the exponentially windowed recursive least squares (EW-RLS) algorith m with optimal forgetting factor, the presented method leads to a significa nt improvement in fast time varying parameter estimation. The effects of si gnal to noise ratio and nonstationarity have been tested using computer sim ulations with the given parameter model. An assessment of the performance o f each algorithm is presented in terms of the mean-square-deviation (MSD).