Abstract
Dempster‐Shafer evidence theory can effectively process imperfect information and is widely used in a data fusion system. However, classical Dempster‐Shafer evidence theory involves counter‐intuitive behaviors with the data of multisensor high conflict in target identification system. In order to solve this problem, an improved evidence combination method is proposed in this paper. By calculating the support degree and the belief entropy of each sensor, the proposed method combines conflict evidences. A new method is used to calculate support degree in this paper. At the same time, inspired by Deng entropy, the modified belief entropy is proposed by considering the scale of the frame of discernment (FOD) and the relative scale of the intersection between evidences with respect to FOD. Because of these two modifications, the effect has been improved in conflict data fusion. Several methods are compared and analyzed through examples. And the result suggests the proposed method can not only obtain reasonable and correct results but also have the highest fusion reliability in solving the problem of high conflict data fusion.
Highlights
The cooperative detection system with multisensor has been widely used in aerospace, medical rescue, environmental monitoring, national defense, and military fields
In order to obtain better data fusion result when dealing with high conflict data from a multisensor, a new method of high conflict data fusion is introduced
By considering the new method of calculating support degree and the modified belief entropy of every evidence, the proposed method can avoid the influence of high conflict evidence and improve the performance of measuring uncertain degree with basic probability assignment (BPA)
Summary
The cooperative detection system with multisensor has been widely used in aerospace, medical rescue, environmental monitoring, national defense, and military fields. D-S evidence theory is a useful mathematical theory for information fusion in real applications, the classical Dempster’s rule of combination and the basic probability assignment (BPA) cannot handle conflict sensor data fusion directly, which may lead to counter-intuitive behaviors [11,12,13]. In order to obtain better data fusion result when deal with high conflict data from multisensor, based on support degree and belief entropy, a new method of high conflict data fusion is introduced. The proposed method can avoid the influence of conflict evidence and improve the information validity of the fusion result by calculating the support degree and the belief entropy of each sensor.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
