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A Method to Determine Basic Probability Assignment in Context Awareness of a Moving Object

机译:确定运动物体上下文感知中的基本概率分配的方法

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Determining basic probability assignment (BPA) is essential in multisensor data fusion by using Fussy Theory or Dempster-Shafer Theory (DST). The study presented a method to determine BPA through sensor data only reported by sensors without depending on preset information data modeled prior to actual events. This was used to determine BPA for multi-sensor data fusion so that a pedestrian, who walked or moved, could recognize a moving object. The method resulted from the study was to evaluate the changes of each sensor measurement as time passed. Each BPA of each focal element was normalized to evaluate the aspects of the changes by time and to meet the basic characteristics of BPA in DST. That is, BPA of each focal element after evaluating sensor data was ranged between 0 and 1, and the total amount of all focal elements was 1. The study showed that a pedestrian could recognize a moving object with the method of determining BPA through multi-sensor data fusion conducted in the study.
机译:通过使用模糊理论或Dempster-Shafer理论(DST),确定基本概率分配(BPA)对于多传感器数据融合至关重要。该研究提出了一种仅通过传感器报告的传感器数据来确定BPA的方法,而无需依赖于在实际事件之前建模的预设信息数据。这用于确定用于多传感器数据融合的BPA,以便步行或移动的行人可以识别移动的对象。研究得出的方法是评估每个传感器测量值随时间的变化。将每个焦点元素的每个BPA进行归一化,以评估随时间变化的方面,并满足DST中BPA的基本特征。也就是说,评估传感器数据后每个焦点元素的BPA介于0和1之间,所有焦点元素的总数为1。研究表明,行人可以通过多点BPA确定方法来识别运动对象。在研究中进行传感器数据融合。

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