Adaptive recovery of a chirped sinusoid in noise. II. Performance of the LMS algorithm

For pt.I see ibid., vol.39, no. 3, p.583-94 (1991). The authors present a methodology for evaluating the tracking behavior of the least-mean square (LMS) algorithm for the nontrivial case of recovering a chirped sinusoid in additive noise. A complete closed-form analysis of the LMS tracking properties for a nonstationary inverse system modeling problem is also presented. The mean-square error (MSE) performance of the LMS algorithm is calculated as a function of the various system parameters. The misadjustment or residual of the adaptive filter output is the excess MSE as compared to the optimal filter for the problem. It is caused by three errors in the adaptive weight vector: the mean lag error between the (time-varying mean) weight and the time-varying optimal weight; the fluctuations of the lag error; and the noise misadjustment which is due to the output noise. These results are important because they represent a precise analysis of a nonstationary deterministic inverse modeling system problem with the input being a colored signal. The results are in agreement with the form of the upper bounds for the misadjustment provided by E. Eweda and O. Macchi (1985) for the deterministic nonstationarity