2 February 2018 Developing the fuzzy c-means clustering algorithm based on maximum entropy for multitarget tracking in a cluttered environment
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Abstract
For fast and more effective implementation of tracking multiple targets in a cluttered environment, we propose a multiple targets tracking (MTT) algorithm called maximum entropy fuzzy c-means clustering joint probabilistic data association that combines fuzzy c-means clustering and the joint probabilistic data association (PDA) algorithm. The algorithm uses the membership value to express the probability of the target originating from measurement. The membership value is obtained through fuzzy c-means clustering objective function optimized by the maximum entropy principle. When considering the effect of the public measurement, we use a correction factor to adjust the association probability matrix to estimate the state of the target. As this algorithm avoids confirmation matrix splitting, it can solve the high computational load problem of the joint PDA algorithm. The results of simulations and analysis conducted for tracking neighbor parallel targets and cross targets in a different density cluttered environment show that the proposed algorithm can realize MTT quickly and efficiently in a cluttered environment. Further, the performance of the proposed algorithm remains constant with increasing process noise variance. The proposed algorithm has the advantages of efficiency and low computational load, which can ensure optimum performance when tracking multiple targets in a dense cluttered environment.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Xiao Chen, Xiao Chen, Yaan Li, Yaan Li, Jing Yu, Jing Yu, Yuxing Li, Yuxing Li, } "Developing the fuzzy c-means clustering algorithm based on maximum entropy for multitarget tracking in a cluttered environment," Journal of Applied Remote Sensing 12(1), 016019 (2 February 2018). https://doi.org/10.1117/1.JRS.12.016019 . Submission: Received: 14 August 2017; Accepted: 10 January 2018
Received: 14 August 2017; Accepted: 10 January 2018; Published: 2 February 2018
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