Abstract
Time series data fusion is important in real applications such as target recognition based on sensors’ information. The existing credibility decay model (CDM) is not efficient in the situation when the time interval between data from sensors is too long. To address this issue, a new method based on the ordered weighted aggregation operator (OWA) is presented in this paper. With the improvement to use the Q function in the OWA, the effect of time interval on the final fusion result is decreased. The application in target recognition based on time series data fusion illustrates the efficiency of the new method. The proposed method has promising aspects in time series data fusion.
Highlights
Evidence theory, called Dempster Shafer evidence theory [1,2], plays an important role in data fusion because of the efficiency to deal with uncertainty [3,4,5,6,7]
In credibility decay model (CDM), after the data is first transformed to the basic probability distribution (BPA) on the discernment frame at time ti, the dynamic credibility αi,j calculated from Equation (8) is used to discount old BPA according to Equation (4)
To overcome the shortcomings of existing credibility decay model, a new method based on ordered weight aggregation (OWA) operator is presented
Summary
Called Dempster Shafer evidence theory [1,2], plays an important role in data fusion because of the efficiency to deal with uncertainty [3,4,5,6,7]. In CDM, there is a lack of valid discussion on the relationship of time interval between old and new evidences and the credibility of old evidence To address this issue, a new method is proposed based on ordered weight aggregation (OWA). The new decay model can identify the target using the information collected from sensors considering the role of old evidences in data fusion from the perspective of linguistic “anding” and “oring”, rather than time intervals, which is unreasonably used in original CDM.
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