A fuzzy-Bayesian approach to target recognition based on multisensor fusion
The Bayesian approach is widely used in automatic target recognition (ATR) systems based on multisensor fusion technology. Problems in data fusion systems are complex by nature and can often be characterized by not only randomness but also fuzziness. However, in general, current Bayesian methods can...
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| Veröffentlicht in: | Journal of computer & systems sciences international 2006-01, Vol.45 (1), p.114-119 |
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| creator | Yong, Deng Wen-Kang, Shi |
| description | The Bayesian approach is widely used in automatic target recognition (ATR) systems based on multisensor fusion technology. Problems in data fusion systems are complex by nature and can often be characterized by not only randomness but also fuzziness. However, in general, current Bayesian methods can only account for randomness. To accommodate complex natural problems with both types of uncertainties, it is profitable to improve the existing approach by incorporating fuzzy theory into classical techniques. In this paper, after representing both the individual attribute of the target in the model database and the sensor observation or report as the fuzzy membership function, a likelihood function is constructed to deal with fuzzy data collected by each sensor. A similarity measure is introduced to determine the agreement degree of each sensor. Based on the similarity measure, a consensus fusion approach (CFA) is developed to generate a global likelihood from the individual attribute likelihood for the whole sensor reports. A numerical example is illustrated to show the target recognition application of the fuzzy-Bayesian approach.[PUBLICATION ABSTRACT] |
| doi_str_mv | 10.1134/S1064230706010126 |
| format | Article |
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| subjects | Fuzzy Fuzzy logic Fuzzy set theory Mathematical analysis Mathematical models Sensors Similarity Similarity measures Studies Target recognition |
| title | A fuzzy-Bayesian approach to target recognition based on multisensor fusion |
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