期刊
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
卷 43, 期 2, 页码 385-398出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2010.502601
关键词
constructed measurements; Kalman filtering; distributed data fusion; feedback; performance analysis
类别
资金
- National 973 Programme of China [51334020202-2, 51334020204-2]
In this article, we study the distributed Kalman filtering fusion problem for a linear dynamic system with multiple sensors and cross-correlated noises. For the assumed linear dynamic system, based on the newly constructed measurements whose measurement noises are uncorrelated, we derive a distributed Kalman filtering fusion algorithm without feedback, and prove that it is an optimal distributed Kalman filtering fusion algorithm. Then, for the same linear dynamic system, also based on the newly constructed measurements, a distributed Kalman filtering fusion algorithm with feedback is proposed. A rigorous performance analysis is dedicated to the distributed fusion algorithm with feedback, which shows that the distributed fusion algorithm with feedback is also an optimal distributed Kalman filtering fusion algorithm; the P matrices are still the estimate error covariance matrices for local filters; the feedback does reduce the estimate error covariance of each local filter. Simulation results are provided to demonstrate the validity of the newly proposed fusion algorithms and the performance analysis.
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