Title :
Performance analysis of LMS adaptive prediction filters
Author :
Zeidler, James R.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, CA, USA
fDate :
12/1/1990 12:00:00 AM
Abstract :
The conditions required to implement real-time adaptive prediction filters that provide nearly optimal performance in realistic input conditions are delineated. 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
Keywords :
Monte Carlo methods; adaptive filters; filtering and prediction theory; interference (signal); least squares approximations; matched filters; noise; statistical analysis; LMS adaptive prediction filters; Monte Carlo simulations; analytical modelling; filter weights; hardware implementation; input SNR; noise correlation; noise nonstationarity; nonlinear degradation; optimal matched filter; performance analysis; performance bounds; signal bandwidth; signal processing gain; signal-to-noise ratio; stationary input conditions; statistical properties; stochastic properties; Adaptive filters; Adaptive signal processing; Analytical models; Bandwidth; Degradation; Hardware; Least squares approximation; Matched filters; Performance analysis; Signal to noise ratio;
Journal_Title :
Proceedings of the IEEE
