State Estimation in Linear Dynamical Systems By Partial Update Kalman Filtering

In this letter, we develop a partial update Kalman filtering (PUKF) algorithm to solve the state of a discrete-time linear stochastic dynamical system. In the proposed algorithm, only a subset of the state vector is updated at every iteration, which reduces its computational complexity, compared to...

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Published inCircuits, systems, and signal processing Vol. 41; no. 2; pp. 1188 - 1200
Main Authors Pourasad, Yaghoub, Vahidpour, Vahid, Rastegarnia, Amir, Ghorbanzadeh, Parviz, Sanei, Saeid
Format Journal Article
LanguageEnglish
Published New York Springer US 01.02.2022
Springer Nature B.V
Subjects
Algorithms
Circuits and Systems
Discrete time systems
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Iterative methods
Kalman filters
Short Paper
Signal,Image and Speech Processing
State estimation
State vectors
Partial update
Kalman filtering
Convergence analysis
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Summary:In this letter, we develop a partial update Kalman filtering (PUKF) algorithm to solve the state of a discrete-time linear stochastic dynamical system. In the proposed algorithm, only a subset of the state vector is updated at every iteration, which reduces its computational complexity, compared to the original KF algorithm. The required conditions for the stability of the algorithm are discussed. A closed-form expression for steady-state mean-square deviation is also derived. Numerical examples are used to validate the correctness of the provided analysis. They also reveal the PUKF algorithm exhibits a trade-off between the estimation accuracy and the computational load which is extremely profitable in practical applications.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-021-01815-5