Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model

doi:10.1007/s12204-014-1556-9

上海交通大学学报（英文版） ›› 2015, Vol. 20 ›› Issue (3): 265-272.doi: 10.1007/s12204-014-1556-9

Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model

CHEN Zhi-feng (陈志锋), CAI Yun-ze* (蔡云泽)

(Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China)

出版日期:2015-06-27 发布日期:2015-06-11
通讯作者: CAI Yun-ze (蔡云泽) E-mail:yzcai@sjtu.edu.cn

Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model

CHEN Zhi-feng (陈志锋), CAI Yun-ze* (蔡云泽)

(Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China)

Online:2015-06-27 Published:2015-06-11
Contact: CAI Yun-ze (蔡云泽) E-mail:yzcai@sjtu.edu.cn

摘要/Abstract

摘要： This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model (IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.

关键词: multi-sensor, cross-correlated noises, augmented fusion, interacting multiple model (IMM)

Abstract: This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model (IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.

Key words: multi-sensor, cross-correlated noises, augmented fusion, interacting multiple model (IMM)

中图分类号:

TP 274.2

CHEN Zhi-feng (陈志锋), CAI Yun-ze* (蔡云泽). Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model[J]. 上海交通大学学报（英文版）, 2015, 20(3): 265-272.

CHEN Zhi-feng (陈志锋), CAI Yun-ze* (蔡云泽). Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model[J]. Journal of shanghai Jiaotong University (Science), 2015, 20(3): 265-272.

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Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model

Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model

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