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12 October 2015 An improved method of fuzzy support degree based on uncertainty analysis
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Abstract
Most multisensor association algorithms based on fuzzy set theory forms the opinion of fuzzy proposition using a simple triangular function. It does not take the randomness of measurements into account. Otherwise, the variance of sensors supposed to be known in the triangular function, but in fact the exact variance is difficult to acquire. This paper discuss about two situations with known and unknown variance of sensors. First, with known variance and known mean. This paper proposes a method, which use the probability ratio to calculate the fuzzy support degree. The interaction between the two objects is considered. Second, with unknown variance and known mean value, we replace the sample mean in the gray auto correlation function with the real sensor mean value to analysis the uncertainty which is the correlation coefficient between targets and measurements actually. In this way, it can deal with the case of small sample. Finally, form the opinion about the fuzzy proposition in terms of weighting the opinion of all the sensors based on the result of uncertainty analysis. Sufficient simulations on some typical scenarios are performed, and the results indicate that the method presented is efficient.
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Yuan Huang, Jing Wu, Lihua Wu, and Weidong Sheng "An improved method of fuzzy support degree based on uncertainty analysis", Proc. SPIE 9639, Sensors, Systems, and Next-Generation Satellites XIX, 963922 (12 October 2015); https://doi.org/10.1117/12.2194273
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