| Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model |
| |
| 作者姓名： | 陈志锋 蔡云泽 |
| |
| 作者单位： | Department of Automation, Shanghai Jiaotong University |
| |
| 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： | utilized verified uncertain simply interacting augmented eigenvalue acceleration iteration priori |
Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model |
| |
| CHEN Zhi-feng
,
CAI Yun-ze.Data Fusion Algorithm for Multi-Sensor Dynamic System Based on Interacting Multiple Model[J].Journal of Shanghai Jiaotong university,2015,20(3):265-272. |
| |
| Authors: | CHEN Zhi-feng CAI Yun-ze |
| |
| Institution: | Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China |
| |
| 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. |
| |
| Keywords: | multi-sensor cross-correlated noises augmented fusion interacting multiple model (IMM) |
| 本文献已被 CNKI 等数据库收录！ |
