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.
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
.
DOI: 10.1007/s12204-014-1556-9
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