Weighted least squares/MFT algorithms for linear differential system identification

Based on two different stochastic signal models, weighted least squares and adaptive weighted least-squares algorithms are developed for linear differential system parameter identification, both utilizing a modulating function technique (MFT). Comparison is made with the well known prediction error method showing that the MFT algorithms give smaller biases and standard deviations for the estimated parameters over a broad range of noise levels.<>