Title :
A mildly weaker sufficient condition in IIR adaptive filtering
Author_Institution :
Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA
fDate :
1/1/1994 12:00:00 AM
Abstract :
The cross-covariance matrix of two stable autoregressive (AR) sequences is considered. A mildly weaker condition is identified that ensures the nonsingularity of this matrix. As one consequence of this result, a weaker sufficient condition is obtained that would guarantee the unimodality of the mean-square output error surface of an IIR adaptive filter with white noise excitation
Keywords :
adaptive filters; digital filters; filtering and prediction theory; matrix algebra; series (mathematics); stochastic processes; time series; white noise; IIR adaptive filter; IIR adaptive filtering; cross-covariance matrix; matrix nonsingularity; mean-square output error surface; mildly weaker sufficient condition; stable autoregressive sequences; unimodality; white noise excitation; Adaptive filters; Equations; IIR filters; Image reconstruction; Projection algorithms; Quantization; Signal processing; Sufficient conditions; Upper bound; White noise;
Journal_Title :
Signal Processing, IEEE Transactions on
