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A Fuzzy-Bayesian Approach to Target Recognition Based on Multisensor Fusion

机译：基于多传感器融合的模糊贝叶斯目标识别方法

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摘要

The Bayesian approach is widely used in automatic target recognition (ATR) systems based on multisensor fusion technology. Problems in data fusion systems are complex by nature and can often be characterized by not only randomness but also fuzziness. However, in general, current Bayesian methods can only account for randomness. To accommodate complex natural problems with both types of uncertainties, it is profitable to improve the existing approach by incorporating fuzzy theory into classical techniques. In this paper, after representing both the individual attribute of the target in the model database and the sensor observation or report as the fuzzy membership function, a likelihood function is constructed to deal with fuzzy data collected by each sensor. A similarity measure is introduced to determine the agreement degree of each sensor. Based on the similarity measure, a consensus fusion approach (CFA) is developed to generate a global likelihood from the individual attribute likelihood for the whole sensor reports. A numerical example is illustrated to show the target recognition application of the fuzzy-Bayesian approach.
机译：贝叶斯方法广泛用于基于多传感器融合技术的自动目标识别（ATR）系统。数据融合系统中的问题本质上是复杂的，通常不仅具有随机性，而且具有模糊性。但是，一般而言，当前的贝叶斯方法只能考虑随机性。为了适应两种不确定性的复杂自然问题，通过将模糊理论纳入经典技术来改进现有方法是有利的。在本文中，将模型数据库中目标的各个属性和传感器的观测或报告都表示为模糊隶属度函数后，构造了似然函数来处理每个传感器收集的模糊数据。引入相似性度量以确定每个传感器的一致性程度。基于相似性度量，开发了一种共识融合方法（CFA），可以从整个传感器报告的单个属性可能性中生成全局可能性。数值例子说明了模糊贝叶斯方法在目标识别中的应用。

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