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CLC number: TP13

On-line Access: 2012-01-19

Received: 2011-06-11

Revision Accepted: 2011-10-08

Crosschecked: 2012-01-06

Cited: 5

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.2 P.118-130


Quantized innovations Kalman filter: stability and modification with scaling quantization

Author(s):  Jian Xu, Jian-xun Li, Sheng Xu

Affiliation(s):  Science and Technology on Avionics Integration Laboratory, Shanghai Jiao Tong University, Shanghai 200240, China; more

Corresponding email(s):   xujian2001-1@163.com, lijx@sjtu.edu.cn, xusheng2007-1@163.com

Key Words:  Kalman filtering, Quantized innovation, Stability, Scaling quantization, Wireless sensor network

Jian Xu, Jian-xun Li, Sheng Xu. Quantized innovations Kalman filter: stability and modification with scaling quantization[J]. Journal of Zhejiang University Science C, 2012, 13(2): 118-130.

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author="Jian Xu, Jian-xun Li, Sheng Xu",
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%T Quantized innovations Kalman filter: stability and modification with scaling quantization
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%A Jian-xun Li
%A Sheng Xu
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100161

T1 - Quantized innovations Kalman filter: stability and modification with scaling quantization
A1 - Jian Xu
A1 - Jian-xun Li
A1 - Sheng Xu
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 2
SP - 118
EP - 130
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100161

The stability of quantized innovations kalman filtering (QIKF) is analyzed. In the analysis, the correlation between quantization errors and measurement noises is considered. By taking the quantization errors as a random perturbation in the observation system, the QIKF for the original system is equivalent to a Kalman-like filtering for the equivalent state-observation system. Thus, the estimate error covariance matrix of QIKF can be more exactly analyzed. The boundedness of the estimate error covariance matrix of QIKF is obtained under some weak conditions. The design of the number of quantized levels is discussed to guarantee the stability of QIKF. To overcome the instability and divergence of QIKF when the number of quantization levels is small, we propose a Kalman filter using scaling quantized innovations. Numerical simulations show the validity of the theorems and algorithms.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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