This paper presents a unified approach to analyze the convergence properties of Steiglitz-McBride(1966) method (SMM) in general environments. SMM is formulated as a successive substitution equation. Using results from fixed point theory enables a unified analysis of SMM in both white and colored noise, and sufficient and insufficient order cases. This analysis provides us with several new results. Specifically, for sufficient order filters in white noise environments, the convergence rate of SMM can be predicted by the signal-power to noise-power ratio (SNR) at plant output. For sufficient order filters in colored noise, SMM may diverge or converge depending on the initial estimate and SNR at plant output. If SMM converges, the convergence point is near the unbiased solution. SNR again determines the bias magnitude. For insufficient order filters, in addition to the possible multiple convergence points, we also demonstrate the existence of diverging fixed points of SMM. These diverging fixed points can be used to separate the convergence region, and identify the convergence points for each initial estimate.<<ETX>>
[1]
Stability, projection, and convergence of SMM for insufficient order adaptive IIR filters
,
1990,
IEEE International Symposium on Circuits and Systems.
[2]
L. Mcbride,et al.
A technique for the identification of linear systems
,
1965
.
[3]
W. Jenkins,et al.
A new adaptive IIR filter
,
1986
.
[4]
R. Fletcher.
Practical Methods of Optimization
,
1988
.
[5]
T. Söderström,et al.
The Steiglitz-McBride identification algorithm revisited--Convergence analysis and accuracy aspects
,
1981
.
[6]
Richard Bellman,et al.
Introduction to Matrix Analysis
,
1972
.
[7]
Hong Fan,et al.
Application of Benveniste's convergence results in the study of adaptive IIR filtering algorithms
,
1988,
IEEE Trans. Inf. Theory.
[8]
H. Fan,et al.
On 'global convergence' of Steiglitz-McBride adaptive algorithm
,
1993
.