A parallel square-root algorithm for modified extended Kalman filter

A parallel square-root algorithm and its systolic array implementation are proposed for performing modified extended Kalman filtering (MEKF). The proposed parallel square-root algorithm is designed based on the singular value decomposition (SVD) and the Faddeev algorithm, and a very large scale inte...

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Vydané v:IEEE transactions on aerospace and electronic systems Ročník 28; číslo 1; s. 153 - 163
Hlavní autori: Lu, M., Qiao, X., Chen, G.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.01.1992
Institute of Electrical and Electronics Engineers
Predmet:
Applied sciences
Communication system control
Control systems
Exact sciences and technology
Filtering algorithms
Information, signal and communications theory
Kalman filters
Linear approximation
Mathematical methods
Nonlinear filters
Signal processing algorithms
State estimation
Telecommunications and information theory
Vectors
Very large scale integration
VLSI circuit
Kalman filter
Theoretical study
Reliability
Performance
Systolic network
Algorithm
Implementation
ISSN:0018-9251, 1557-9603
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Abstract A parallel square-root algorithm and its systolic array implementation are proposed for performing modified extended Kalman filtering (MEKF). The proposed parallel square-root algorithm is designed based on the singular value decomposition (SVD) and the Faddeev algorithm, and a very large scale integration (VLSI) systolic array architecture is developed for its implementation. Compared to other square root Kalman filtering algorithms, the proposed method is more numerically stable. The VLSI architecture described has good parallel and pipelining characteristics in applying to the MEKF and achieves higher efficiency. For n-dimensional state vector estimations, the proposed architecture consists of O(2n/sup 2/) processing elements and uses O((s+17)n) time-steps for a complete iteration at each instant, in contrast to the complexity of O((s+6)n/sup 3/) time-steps for a sequential implementation, where s approximately=log n.< >
AbstractList A parallel square-root algorithm together with its systolic arrays implementation are proposed for performing the modified extended Kalman filter (MEKF). The proposed parallel square-root algorithm is designed based on the singular value decomposition and the Faddeev algorithm, and a very large scale integration (VLSI) systolic arrays architecture is developed for its implementation. Comparing with other square root Kalman filtering algorithms existing in the literature, the proposed method is more numerically stable. Moreover, the new VLSI architecture has very nice parallel and pipelining characteristics in applying to the MEKF and achieves higher efficiency. For n-dimensional state vector estimations, the proposed architecture consists of O(2n2) processing elements and uses O(/s + 17/n) time-steps for a complete iteration at each instant, in contrast to the complexity of O(/s + 6/n3) time-steps for a sequential implementation, where s is approximately log n. (Author)
A parallel square-root algorithm and its systolic array implementation are proposed for performing modified extended Kalman filtering (MEKF). The proposed parallel square-root algorithm is designed based on the singular value decomposition (SVD) and the Faddeev algorithm, and a very large scale integration (VLSI) systolic array architecture is developed for its implementation. Compared to other square root Kalman filtering algorithms, the proposed method is more numerically stable. The VLSI architecture described has good parallel and pipelining characteristics in applying to the MEKF and achieves higher efficiency. For < e1 > n < /e1 > -dimensional state vector estimations, the proposed architecture consists of < e1 > O < /e1 > (2 < e1 > n < /e1 > (2)) processing elements and uses < e1 > O < /e1 > (( < e1 > s < /e1 > 17) < e1 > n < /e1 > ) time-steps for a complete iteration at each instant, in contrast to the complexity of < e1 > O < /e1 > (( < e1 > s < /e1 > 6) < e1 > n < /e1 > (3)) time-steps for a sequential implementation, where < e1 > s < /e1 > {approximately equal to}log < e1 > n < /e1 >
A parallel square-root algorithm and its systolic array implementation are proposed for performing modified extended Kalman filtering (MEKF). The proposed parallel square-root algorithm is designed based on the singular value decomposition (SVD) and the Faddeev algorithm, and a very large scale integration (VLSI) systolic array architecture is developed for its implementation. Compared to other square root Kalman filtering algorithms, the proposed method is more numerically stable. The VLSI architecture described has good parallel and pipelining characteristics in applying to the MEKF and achieves higher efficiency. For n-dimensional state vector estimations, the proposed architecture consists of O(2n/sup 2/) processing elements and uses O((s+17)n) time-steps for a complete iteration at each instant, in contrast to the complexity of O((s+6)n/sup 3/) time-steps for a sequential implementation, where s approximately=log n.< >
Author Qiao, X.
Lu, M.
Chen, G.
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10.1109/9.61007
10.1115/1.3662552
10.1117/12.932507
10.1016/0024-3795(86)90171-0
10.1117/12.962260
10.1109/MC.1987.1663619
10.1007/978-3-662-02666-3
10.1117/12.942012
10.1016/0005-1098(86)90104-4
10.1109/9.45155
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Issue 1
Keywords VLSI circuit
Kalman filter
Theoretical study
Reliability
Performance
Systolic network
Algorithm
Implementation
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SubjectTerms Applied sciences
Communication system control
Control systems
Exact sciences and technology
Filtering algorithms
Information, signal and communications theory
Kalman filters
Linear approximation
Mathematical methods
Nonlinear filters
Signal processing algorithms
State estimation
Telecommunications and information theory
Vectors
Very large scale integration
Title A parallel square-root algorithm for modified extended Kalman filter
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