Abstract
In this paper, a fuzzy Kalman filter is proposed to combat the model-set adaptation problem since it is found to be able to extract more exactly dynamic information. The fuzzy Kalman filter uses a set of fuzzy rules to adaptively control the noise covariance and that makes it more suitable for real radar tracking. The proposed fuzzy Kalman filter is then combined with an IMM algorithm, hence, a fuzzy IMM (FIMM) algorithm is obtained. The performance of the FIMM algorithm is compared with that of an adaptive IMM (AIMM) algorithm using real radar target tracking data. Simulation result shows that the FIMM algorithm outperforms the AIMM algorithm in terms of both the root mean square prediction error and the number of track loss. Soc. Inf. Fusion.
Original language | English |
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Title of host publication | Proceedings of the 3rd International Conference on Information Fusion, FUSION 2000 |
Publisher | IEEE Computer Society |
DOIs | |
Publication status | Published - 1 Jan 2000 |
Event | 3rd International Conference on Information Fusion, FUSION 2000 - Paris, France Duration: 10 Jul 2000 → 13 Jul 2000 |
Conference
Conference | 3rd International Conference on Information Fusion, FUSION 2000 |
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Country/Territory | France |
City | Paris |
Period | 10/07/00 → 13/07/00 |
Keywords
- adaptive IMM algorithm
- fuzzy Kalman filter
- IMM algorithm
- model-set adaptation
- Target tracking
ASJC Scopus subject areas
- Information Systems