Abstract
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.
Highlights
Extracting meaningful and advanced information from fragmentary information acquired by many basic information sources became possible through multi-sensor data fusion
Basic probability assignment (BPA) plays a key role when Fuzzy Theory and Dempster-Shafer Theory (DST) are used in multi-sensor data fusion
The process of multi-sensor data fusion based on DST depends on basic probability assignment (BPA), and data fusion and context induction are difficult to process without BPA
Summary
Extracting meaningful and advanced information from fragmentary information acquired by many basic information sources became possible through multi-sensor data fusion. Fussy Theory and DempsterShafer Theory (DST) are used for multi-sensor fusion data. These theories provide a way to express the uncertain and obscure real world with mathematical logic. Basic probability assignment (BPA) plays a key role when Fuzzy Theory and DST are used in multi-sensor data fusion. Context awareness from multisensors is widely used by modeling data acquired prior to events. It is difficult, if not impossible, to have all information for all contexts prior to events in the real world. A method to determine BPA is necessary for context awareness by sensor data acquired at a certain site without prior information. The purpose of this study was that a moving observer would recognize a moving object only by sensor data without any prior information of the environments
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