Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system
ISA Transactions, ISSN: 0019-0578, Vol: 112, Page: 137-149
2021
- 28Citations
- 13Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations28
- Citation Indexes28
- 28
- CrossRef15
- Captures13
- Readers13
- 13
Article Description
Multi-sensor data fusion plays an irreplaceable role in actual production and application. Dempster–Shafer theory (DST) is widely used in numerous fields of information modeling and information fusion due to the flexibility and effectiveness of processing uncertain information and dealing with uncertain information without prior probabilities. However, when highly contradictory evidence is combined, it may produce results that are inconsistent with human intuition. In order to solve this problem, a hybrid method for combining belief functions based on soft likelihood functions (SLFs) and ordered weighted averaging (OWA) operators is proposed. More specifically, a soft likelihood function based on OWA operators is used to provide the possibility to fuse uncertain information compatible with each other. It can characterize the degree to which the probability information of compatible propositions in the collected evidence is affected by unknown uncertain factors. This makes the results of using the Dempster’s combination rule to fuse uncertain information from multiple sources more comprehensive and credible. Experimental results manifest that this method is reliable. Example and application show that this method has obvious advantages in solving the problem of conflict evidence fusion in multi-sensor. In particular, in target recognition, when three pieces of evidence are fused, the target recognition rate is 96.92%, etc.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0019057820305280; http://dx.doi.org/10.1016/j.isatra.2020.12.009; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85098104644&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/33349453; https://linkinghub.elsevier.com/retrieve/pii/S0019057820305280; https://dx.doi.org/10.1016/j.isatra.2020.12.009
Elsevier BV
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