Title

State Estimation in Distributed Parameter Systems via Least Squares and Invariant Embedding

Document Type

Article

Publication Date

6-1972

Abstract

Estimation of states in noisy dynamical systems is a problem whose solution is of significant importance in various scientific disciplines. Algorithms for filtering, smoothing and prediction estimates of lumped parameter system states have been derived by Kalman and Bucy [5], Bryson and Frazier [l], Cox [3], and Detchmendy and Sridhar [4]. The techniques utilized for generating these algorithms include orthogonal projection theory [5], maximum likelihood estimate [3], and the classical least squares error criterion combined with an invariant embedding technique [4].

Comments

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DOI

10.1016/0022-247X(72)90070-4

Source Publication

Journal of Mathematical Analysis and Applications

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