TY - GEN
T1 - Least mean square algorithm for unbiased impulse response estimation
AU - So, H. C.
PY - 2002
Y1 - 2002
N2 - In this paper, a least mean square (LMS) type algorithm is devised for unbiased system identification in the presence of white input and output noise, assuming that the ratio of the noise powers is known. The proposed approach aims to minimize the mean square value of the equation-error function under a constant-norm constraint, and is equivalent to minimizing a modified mean square error function. Analysis of the algorithm shows that the estimates will converge to the true values in the mean sense. Computer simulations are included to corroborate the theoretical development and to evaluate the impulse response estimation performance of the LMS algorithm.
AB - In this paper, a least mean square (LMS) type algorithm is devised for unbiased system identification in the presence of white input and output noise, assuming that the ratio of the noise powers is known. The proposed approach aims to minimize the mean square value of the equation-error function under a constant-norm constraint, and is equivalent to minimizing a modified mean square error function. Analysis of the algorithm shows that the estimates will converge to the true values in the mean sense. Computer simulations are included to corroborate the theoretical development and to evaluate the impulse response estimation performance of the LMS algorithm.
UR - https://www.scopus.com/pages/publications/0036979277
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0036979277&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
VL - 2
SP - II164-II167
BT - Midwest Symposium on Circuits and Systems
T2 - 2002 45th Midwest Symposium on Circuits and Systems
Y2 - 4 August 2002 through 7 August 2002
ER -