Optimal state estimation in high noise

B. D.O. Anderson; J. B. Moore

doi:10.1016/S0019-9958(68)90814-0

Optimal state estimation in high noise

B. D.O. Anderson^*, J. B. Moore

^*Corresponding author for this work

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

Abstract

The problem is examined of estimating the state of a linear dynamical system in the presence of high measurement noise. It is concluded that optimal filter design may be simplified to the extent that it need not depend on the solution of a matrix Riccati differential equation, but only on the solution of a matrix linear differential equation. For a related problem, that of estimating a signal s(t) given noisy measurements s(t) + w(t) where the noise is large and the covariance of s(t) is known, optimal filter design is immediate.

Original language	English
Pages (from-to)	286-294
Number of pages	9
Journal	Information and control
Volume	13
Issue number	4
DOIs	https://doi.org/10.1016/S0019-9958(68)90814-0
Publication status	Published - Oct 1968
Externally published	Yes

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title = "Optimal state estimation in high noise",

abstract = "The problem is examined of estimating the state of a linear dynamical system in the presence of high measurement noise. It is concluded that optimal filter design may be simplified to the extent that it need not depend on the solution of a matrix Riccati differential equation, but only on the solution of a matrix linear differential equation. For a related problem, that of estimating a signal s(t) given noisy measurements s(t) + w(t) where the noise is large and the covariance of s(t) is known, optimal filter design is immediate.",

author = "Anderson, {B. D.O.} and Moore, {J. B.}",

year = "1968",

month = oct,

doi = "10.1016/S0019-9958(68)90814-0",

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AU - Moore, J. B.

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N2 - The problem is examined of estimating the state of a linear dynamical system in the presence of high measurement noise. It is concluded that optimal filter design may be simplified to the extent that it need not depend on the solution of a matrix Riccati differential equation, but only on the solution of a matrix linear differential equation. For a related problem, that of estimating a signal s(t) given noisy measurements s(t) + w(t) where the noise is large and the covariance of s(t) is known, optimal filter design is immediate.

AB - The problem is examined of estimating the state of a linear dynamical system in the presence of high measurement noise. It is concluded that optimal filter design may be simplified to the extent that it need not depend on the solution of a matrix Riccati differential equation, but only on the solution of a matrix linear differential equation. For a related problem, that of estimating a signal s(t) given noisy measurements s(t) + w(t) where the noise is large and the covariance of s(t) is known, optimal filter design is immediate.

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U2 - 10.1016/S0019-9958(68)90814-0

DO - 10.1016/S0019-9958(68)90814-0

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SN - 0019-9958

VL - 13

SP - 286

EP - 294

JO - Information and control

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IS - 4

ER -

Optimal state estimation in high noise

Abstract

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