scholarly journals A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1435
Author(s):  
Yuming Gong ◽  
Zeyu Ma ◽  
Meijuan Wang ◽  
Xinyang Deng ◽  
Wen Jiang
Keyword(s):  
Sensor Fusion ◽  
Information Fusion ◽  
Target Recognition ◽  
Weighted Average ◽  
Sensor Data ◽  
Neutrosophic Set ◽  
Recognition Method ◽  
Neutrosophic Sets ◽  
Recognition Result ◽  
Complementarity Analysis

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.

Multi-Sensor Target Recognition Using VIKOR Combined with G1 Method

2014 ◽  
Vol 707 ◽  
pp. 321-324 ◽  
Author(s):  
Hui Zheng
Keyword(s):  
Information Fusion ◽  
Decision Problem ◽  
Target Recognition ◽  
Recognition Problem ◽  
Vikor Method ◽  
Recognition Method ◽  
Judgment Matrix ◽  
Better Than

The aim of this paper is to propose a new multi-sensor target recognition method based on VIKOR method. Multi-sensor target recognition is an important part of information fusion. The multi-sensor target recognition problem contains many factors, and thus it is actually a multi-attribute decision problem. The new multi-sensor target recognition method combines the VIKOR method with G1 method. G1 method is an objectively determining weights method, and it does not need to test the consistency of judgment matrix, thus it is better than AHP. A practical example is studied to demonstrate the effectiveness and feasibility of the proposed method.

Improved locating algorithm of railway tunnel personnel based on collaborative information fusion in Internet of Things

2017 ◽  
Vol 39 (4) ◽  
pp. 446-454 ◽  
Author(s):  
Ou Yang
Keyword(s):  
Internet Of Things ◽  
Information Fusion ◽  
Radio Frequency Identification ◽  
Target Location ◽  
Target Recognition ◽  
Weighted Average ◽  
Moving Target ◽  
Precise Localization ◽  
Railway Tunnel ◽  
Virtual Reference

With larger-scale of railway construction in China, there are more and more larger-scale tunnel projects in process. Tunnel engineering constructions are very difficult, high risk, and there are many unpredictable factors that may cause safety issues, such as landslides, roof caving and water bursts, threatening the construction personnel’s safety. The personnel positioning tracking systems have been studied and applied preliminarily in railway tunnel construction. It is important to know how many people are underground, and workers must sign in/out one by one at tunnel entrances when they come in or leave; a process that is time-consuming and sometimes irritating to those who line up and must exit one-by-one to sign in. Active Radio Frequency Identification (RFID) systems can respond to transponders on the personnel’s helmets or jackets to quickly identify workers coming and going without stopping to sign in and out. It can also alert management when a person enters without a transponder. Indoor moving target recognition and tracking in Internet of Things is a popular research and application topic in recent years. This paper proposes an indoor moving target recognition and tracking method based on RFID and Charge Coupled Device (CCD) collaborative information fusion. First, RFID technique and the proposed extended virtual reference elimination (extended virtual reference elimination) approach are used to recognize and to coarsely locate the target. Second, based on the coarse localization results, the monitoring/sleeping control of different CCDs will be realized. Subsequently, the background-difference method is used to detect the target in CCD monitoring image and realize precise localization with multiple angle of view fusion. Finally, with weighted average of the two localization results, the moving target location is obtained. The method combines the advantages of RFID fast recognition and localization and CCD precise localization. The experiment results indicate that the proposed collaborative information fusion method can effectively improve the accuracy and real-time performance of indoor moving target tracking.

scholarly journals An Extended EDAS Method for Multicriteria Decision-Making Based on Multivalued Neutrosophic Sets

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Lili Han ◽  
Cuiping Wei
Keyword(s):  
Decision Making ◽  
Belief Systems ◽  
Intuitionistic Fuzzy Set ◽  
Weighted Average ◽  
Multicriteria Decision Making ◽  
Parameter Analysis ◽  
Neutrosophic Set ◽  
Neutrosophic Sets ◽  
Multicriteria Decision ◽  
Comprehensive Evaluations

Neutrosophic set (NS) is a generalization of intuitionistic fuzzy set (IFS). It depicts not only the incomplete information but also the indeterminate information and inconsistent information which exist commonly in belief systems. In this paper, the evaluation based on distance from average solution (EDAS) method is extended to handle multicriteria decision-making problems with multivalued neutrosophic numbers (MVNNs). The average solution under all the criteria is calculated by the proposed convex weighted average operator of MVNNs. Then, the positive distance and the negative distance from each solution to the average solution are calculated, and the comprehensive evaluations of alternatives are obtained by integrating two kinds of distance values to get the ranking result. Finally, the rationality and efficiency of the proposed method are shown by the parameter analysis and comparisons with some existing methods.

The Cat's Eye Effect Target Recognition Method Based on Visual Attention

2019 ◽  
Vol 28 (5) ◽  
pp. 1080-1086 ◽  
Author(s):  
Xingbin Wang ◽  
Jun Zhang ◽  
Shuaihui Wang
Keyword(s):  
Visual Attention ◽  
Target Recognition ◽  
Recognition Method
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