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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Parent work
2017 25th European Signal Processing Conference (EUSIPCO)
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Issue / Number
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.
Keywords
Subject (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Event
2017 25th European Signal Processing Conference (EUSIPCO)
Exhibition start date
Exhibition end date
Conference start date
28.08.2017
Conference end date
02.09.2017
Date of the last check
ISBN
978-0-9928626-7-1
978-0-9928626-8-8
978-1-5386-0751-0
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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Publication status
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