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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Liu Zhi Wang Minghui |
| Copyright Year | 2010 |
| Abstract | aiming at the disadvantages of high computation overhead and bad extensibility in matrix weighted fusion method, a multi-sensor fusion algorithm with feedback based on fuzzy c-means (FCM) clustering and maximun entropy principle (MEP) is proposed in this paper. This algorithm combined FCM and MEP to calculate fusion matrix weight of local state estimates considering every component of state vector integratedly. What's more, this algoritm has a good real-time performance due to less matrix computation and good extensibility which show it can directly be applied into tracking system comprising more than two sensors. It is proved by experiments and results that the tracking accuracy of fusion estimate is higher than that of matrix weighted fusion method. |
| Starting Page | 80 |
| Ending Page | 83 |
| File Size | 366240 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424483334 |
| DOI | 10.1109/ISDEA.2010.84 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-10-13 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Maximum Entropy Principle Target tracking Data Fusion Multi-Sensor Sensor fusion Fuzzy C-Means Entropy Sensor systems Covariance matrix Component Optimization |
| Content Type | Text |
| Resource Type | Article |
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