Comparisons of system identification methods in the presence of high noise levels and bandlimited inputs

In this paper we investigate the performance of three well known system identification methods based on an FIR (finite impulse response) model of the system. The methods will be referred to in this paper as the least squares analysis (LSA) method, the least mean Squares adaptation algorithm (LMS) and the short-time spectral analysis (SSA) procedure. Our particular interest in this paper concerns the performance of these algorithms in the presence of high noise levels and in situations where the input signal is bandlimited. Both white and nonwhite random noise signals as well as speech signals are used as test signals to measure the performance of the system identification techniques, Quantitative results in terms of an accuracy measure of system identification are presented and a simple analytical model is used to explain the measured results.