Identification of a class of signals imbedded in high measurement noise

Kyung Joo; Tamal Bose

doi:10.1016/0016-0032(93)90061-X

Identification of a class of signals imbedded in high measurement noise

Kyung Joo
, Tamal Bose

Research output: Contribution to journal › Article › peer-review

Abstract

The Kalman filtering algorithm is used to identify a class of signals imbedded in high amplitude measurement noise. The considered class of signals is first modeled empirically as a nonlinear equation. The equation is then linearized and formulated as a Kalman filtering state estimation problem. Computer simulations yield excellent results for a variety of examples, a couple of which are presented in this paper.

Original language	English (US)
Pages (from-to)	995-1003
Number of pages	9
Journal	Journal of the Franklin Institute
Volume	330
Issue number	6
DOIs	https://doi.org/10.1016/0016-0032(93)90061-X
State	Published - Nov 1993
Externally published	Yes

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications
Applied Mathematics

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10.1016/0016-0032(93)90061-X

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abstract = "The Kalman filtering algorithm is used to identify a class of signals imbedded in high amplitude measurement noise. The considered class of signals is first modeled empirically as a nonlinear equation. The equation is then linearized and formulated as a Kalman filtering state estimation problem. Computer simulations yield excellent results for a variety of examples, a couple of which are presented in this paper.",

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Identification of a class of signals imbedded in high measurement noise

Abstract

ASJC Scopus subject areas

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