A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set
To improve the efficiency, accuracy, and intelligence of target detection and recognition, multi-sensor information fusion technology has broad application prospects in many aspects. Compared with single sensor, multi-sensor data contains more target information and effective fusion of multi-source...
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doaj-7854323730374d0fa31360a0adc26ece2020-11-25T03:44:00ZengMDPI AGSymmetry2073-89942020-08-01121435143510.3390/sym12091435A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic SetYuming Gong0Zeyu Ma1Meijuan Wang2Xinyang Deng3Wen Jiang4School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaTo improve the efficiency, accuracy, and intelligence of target detection and recognition, multi-sensor information fusion technology has broad application prospects in many aspects. Compared with single sensor, multi-sensor data contains more target information and effective fusion of multi-source information can improve the accuracy of target recognition. However, the recognition capabilities of different sensors are different during target recognition, and the complementarity between sensors needs to be analyzed during information fusion. This paper proposes a multi-sensor fusion recognition method based on complementarity analysis and neutrosophic set. The proposed method mainly has two parts: complementarity analysis and data fusion. Complementarity analysis applies the trained multi-sensor to extract the features of the verification set into the sensor, and obtain the recognition result of the verification set. Based on recognition result, the multi-sensor complementarity vector is obtained. Then the sensor output the recognition probability and the complementarity vector are used to generate multiple neutrosophic sets. Next, the generated neutrosophic sets are merged within the group through the simplified neutrosophic weighted average (SNWA) operator. Finally, the neutrosophic set is converted into crisp number, and the maximum value is the recognition result. The practicality and effectiveness of the proposed method in this paper are demonstrated through examples.https://www.mdpi.com/2073-8994/12/9/1435neutrosophic settarget recognitioncomplementarity analysisdata fusion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuming Gong Zeyu Ma Meijuan Wang Xinyang Deng Wen Jiang |
spellingShingle |
Yuming Gong Zeyu Ma Meijuan Wang Xinyang Deng Wen Jiang A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set Symmetry neutrosophic set target recognition complementarity analysis data fusion |
author_facet |
Yuming Gong Zeyu Ma Meijuan Wang Xinyang Deng Wen Jiang |
author_sort |
Yuming Gong |
title |
A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set |
title_short |
A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set |
title_full |
A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set |
title_fullStr |
A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set |
title_full_unstemmed |
A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set |
title_sort |
new multi-sensor fusion target recognition method based on complementarity analysis and neutrosophic set |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2020-08-01 |
description |
To improve the efficiency, accuracy, and intelligence of target detection and recognition, multi-sensor information fusion technology has broad application prospects in many aspects. Compared with single sensor, multi-sensor data contains more target information and effective fusion of multi-source information can improve the accuracy of target recognition. However, the recognition capabilities of different sensors are different during target recognition, and the complementarity between sensors needs to be analyzed during information fusion. This paper proposes a multi-sensor fusion recognition method based on complementarity analysis and neutrosophic set. The proposed method mainly has two parts: complementarity analysis and data fusion. Complementarity analysis applies the trained multi-sensor to extract the features of the verification set into the sensor, and obtain the recognition result of the verification set. Based on recognition result, the multi-sensor complementarity vector is obtained. Then the sensor output the recognition probability and the complementarity vector are used to generate multiple neutrosophic sets. Next, the generated neutrosophic sets are merged within the group through the simplified neutrosophic weighted average (SNWA) operator. Finally, the neutrosophic set is converted into crisp number, and the maximum value is the recognition result. The practicality and effectiveness of the proposed method in this paper are demonstrated through examples. |
topic |
neutrosophic set target recognition complementarity analysis data fusion |
url |
https://www.mdpi.com/2073-8994/12/9/1435 |
work_keys_str_mv |
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