13 March 2013 Improvement of strong tracking Kalman filter based on fuzzy forgetting factor
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In the strong tracking Kalman filter algorithm with multiple suboptimal fading factors, the optimum filter tracking performance cannot been achieved when the forgetting factor in estimation formula of state error covariance matrix takes an inappropriate value. In this paper, an estimation method of error variance matrix on the basis of fuzzy forgetting factor was proposed. Using the fuzzy logic controller to monitor fuzzy similarity coefficient and state estimation variance, this method regulates fuzzy forgetting factor according to fuzzy rules, and then adjusts suboptimal multiple fading factors to improve the tracking precision of the filter in the strong tracking Kalman filter algorithm. The simulation result proves the effectiveness of the algorithm.
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Yong-jun Zhang, Yong-jun Zhang, Zhi-gang Yang, Zhi-gang Yang, Jing Wang, Jing Wang, } "Improvement of strong tracking Kalman filter based on fuzzy forgetting factor", Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87840T (13 March 2013); doi: 10.1117/12.2013920; https://doi.org/10.1117/12.2013920


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