Performance Analysis of LMS Adaptive Prediction Filters

摘要

Real-time implementation of adaptive linear prediction filters have been shown toprovide useful signal processing gains in a wide range of practical applications. The performance of such filters is generally bounded by that of the ideal Weiner filter, but the magnitude of the implementation errors is dependent on fixed adaptive filter parameters such as the adaptive time constant, the filter order, and the prediction distance. The goal of this paper is to delineate the conditions required to implement real-time adaptive prediction filters that provide nearly optimal performance in realistic input conditions. The effects of signal bandwidth, input signal-to-noise ratio (SNR), noise correlation, and noise nonstationarity are explicitly considered. Analytical modeling, Monte Carlo simulations and experimental results obtained using a hardware implementation are utilized to provide performance bounds for specified input conditions. It is shown that there is a nonlinear degradation in the signal processing gain as a function of the input SNR that results from the statistical properties of the adaptive filter weights. The stochastic properties of the filter weights ensure that the performance of the adaptive filter is bounded by that of the optimal matched filter for known stationary input conditions. Proper selection of the fixed filter parameters provide performance results which closely approximate that of the ideal Weiner filter.