An estimation algorithm for AR models with closely located lightly damped low frequency poles

We present a pole estimation algorithm which is based on an overdetermined adaptive IIR filter with an additional postprocessing stage to extract the pole locations from the adaptive weights. The adaptive filtering algorithm used, is a pseudo-linear regression algorithm which is solved by a time-rec...

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Vydáno v:	1997 IEEE International Conference on Acoustics, Speech, and Signal Processing Ročník 5; s. 3821 - 3824 vol.5
Hlavní autoři:	Harteneck, M., Stewart, R.W., McWhirter, J.G., Proudler, I.K.
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	Washington DC IEEE 1997 IEEE Computer Society Press
Témata:	Adaptive filters Applied sciences Array signal processing Brain modeling Detection, estimation, filtering, equalization, prediction Exact sciences and technology Filtering algorithms Frequency estimation IIR filters Information, signal and communications theory Polynomials Predictive models Sensor arrays Signal and communications theory Signal processing algorithms Signal, noise Telecommunications and information theory Infinite impulse response filter Autoregressive model Adaptive filter Signal estimation
ISBN:	0818679190, 9780818679193
ISSN:	1520-6149
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Abstract	We present a pole estimation algorithm which is based on an overdetermined adaptive IIR filter with an additional postprocessing stage to extract the pole locations from the adaptive weights. The adaptive filtering algorithm used, is a pseudo-linear regression algorithm which is solved by a time-recursive QR decomposition. Two pole classification schemes are presented to separate the true poles and the superfluous poles. The classification schemes are based on the occurrence of pole-zero cancelation and on the pole movement in the z-plane. Floating point simulations are presented to demonstrate the performance of the proposed algorithm.
AbstractList	We present a pole estimation algorithm which is based on an overdetermined adaptive IIR filter with an additional postprocessing stage to extract the pole locations from the adaptive weights. The adaptive filtering algorithm used, is a pseudo-linear regression algorithm which is solved by a time-recursive QR decomposition. Two pole classification schemes are presented to separate the true poles and the superfluous poles. The classification schemes are based on the occurrence of pole-zero cancelation and on the pole movement in the z-plane. Floating point simulations are presented to demonstrate the performance of the proposed algorithm.
Author	Harteneck, M. McWhirter, J.G. Stewart, R.W. Proudler, I.K.
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SubjectTerms	Adaptive filters Applied sciences Array signal processing Brain modeling Detection, estimation, filtering, equalization, prediction Exact sciences and technology Filtering algorithms Frequency estimation IIR filters Information, signal and communications theory Polynomials Predictive models Sensor arrays Signal and communications theory Signal processing algorithms Signal, noise Telecommunications and information theory
Title	An estimation algorithm for AR models with closely located lightly damped low frequency poles
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