Title :
Adaptive filtering using quantized output measurements
Author :
Wigren, Torbjörn
Author_Institution :
Dept. of Technol., Uppsala Univ., Sweden
fDate :
12/1/1998 12:00:00 AM
Abstract :
A normalized stochastic gradient adaptive filtering algorithm based on a finite impulse response (FIR) model is discussed. The algorithm identifies the system exactly, given only coarsely quantized output measurements. A description of the quantizer is included in the overall input-output model, and the scheme exploits an approximation of the derivative of the quantizer. Using an associated differential equation, global convergence is established to a zero output error (except for possible colored measurement disturbances) parameter setting or to the boundary of the model set
Keywords :
FIR filters; adaptive filters; convergence of numerical methods; differential equations; gradient methods; quantisation (signal); stochastic processes; FIR model; approximation; colored measurement disturbances; differential equation; finite impulse response model; global convergence; input-output model; normalized stochastic gradient adaptive filtering algorithm; output error; quantized output measurements; zero output error parameter setting; Adaptive filters; Autoregressive processes; Convergence; Echo cancellers; Finite impulse response filter; Least squares approximation; Power system modeling; Quantization; Signal processing algorithms; Stochastic processes;
Journal_Title :
Signal Processing, IEEE Transactions on
