Autonomous state space models for recursive signal estimation beyond least squares

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Publication date
2017
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04B - Conference paper
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2017 25th European Signal Processing Conference (EUSIPCO)
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Pages / Duration
341-345
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IEEE
Place of publication / Event location
New York
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Abstract
The paper addresses the problem of fitting, at any given time, a parameterized signal generated by an autonomous linear state space model (LSSM) to discrete-time observations. When the cost function is the squared error, the fitting can be accomplished based on efficient recursions. In this paper, the squared error cost is generalized to more advanced cost functions while preserving recursive computations: first, the standard sample-wise squared error is augmented with a sampledependent polynomial error; second, the sample-wise errors are localized by a window function that is itself described by an autonomous LSSM. It is further demonstrated how such a signal estimation can be extended to handle unknown additive and/or multiplicative interference. All these results rely on two facts: first, the correlation function between a given discrete-time signal and a LSSM signal can be computed by efficient recursions; second, the set of LSSM signals is a ring.
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Subject (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Project
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2017 25th European Signal Processing Conference (EUSIPCO)
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Conference start date
28.08.2017
Conference end date
02.09.2017
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ISBN
978-0-9928626-7-1
978-0-9928626-8-8
978-1-5386-0751-0
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Language
English
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No
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Published
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Closed
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Citation
ZALMAI, Nour, Reto WILDHABER und Hans-Andrea LOELIGER, 2017. Autonomous state space models for recursive signal estimation beyond least squares. In: 2017 25th European Signal Processing Conference (EUSIPCO). New York: IEEE. 2017. S. 341–345. ISBN 978-0-9928626-7-1. Verfügbar unter: https://irf.fhnw.ch/handle/11654/46832