Multisensor Data Fusion Fuzzy Similarity-based of Several Kalman Filters | ||||
| The International Conference on Electrical Engineering | ||||
| Article 178, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Page 1-14 PDF (338.3 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2008.34630 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Ahmed E. Abdalla | ||||
| Egyptian Armed Forces. | ||||
| Abstract | ||||
| Abstract: The main objective of this work is the development of an intelligent multisensor integration and fusion model that uses fuzzy similarity-based data fusion of several Kalman filters outputs. First, the estimation of sensors outputs are calculated using a set of Kalman filters with pre-estimated measurement noise. Using fuzzy set theory, the fuzzy similarity between the predicted data is extracted to determine the importance weight of each sensor. Weights assigned to different sensors measurement data to reflect the confidence in the sensor's behavior and performance and to realize the multi-sensor data fusion. According to the algorithm theory, its application software is developed using MATLAB. This work has wide applications especially in the development of radar target tracking, smart structural health monitoring systems, biomedical imaging, and robotics control. The applied example proves that the algorithm can give priority to the high-reliability and stability sensors. Moreover, it reflects the efficiency and feasibility to real-time data processing and monitoring. | ||||
| Keywords | ||||
| Kalman Fiter; Fuzzy similarity measurement; and multisensor data fusion | ||||
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