Quantized innovations Kalman filter: stability and modification with scaling quantization

Jian XU; Jian-xun LI; Sheng XU

doi:10.1631/jzus.C1100161

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Quantized innovations Kalman filter: stability and modification with scaling quantization

Computer & Automation | Updated：2022-12-09

- Quantized innovations Kalman filter: stability and modification with scaling quantization
- Quantized innovations Kalman filter: stability and modification with scaling quantization
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 2, Pages: 118-130(2012)
- Affiliations：
  
  1.Science and Technology on Avionics Integration Laboratory, Shanghai Jiao Tong University, Shanghai 200240, China
  2.MOE Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China
  3.School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
- Author bio：
  
  [ "Jian XU, E-mail: xujian2001-1@163.com" ]
  [ "Jian-xun LI, E-mail: lijx@sjtu.edu.cn" ]
  [ "Sheng XU, E-mail: xusheng2007-1@163.com" ]
- Funds：
- DOI：10.1631/jzus.C1100161
  CLC： TP13
- Received：11 June 2011，
  
  Revised：2012-;1-;6，
  
  Published：2012-02
- Accepted：
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Jian XU, Jian-xun LI, Sheng XU. Quantized innovations Kalman filter: stability and modification with scaling quantization[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(2): 118-130.
DOI：

Jian XU, Jian-xun LI, Sheng XU. Quantized innovations Kalman filter: stability and modification with scaling quantization[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(2): 118-130. DOI： 10.1631/jzus.C1100161.

摘要

Abstract

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.

关键词

Keywords

references

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