Sensor fusion for target recognition: a review of fundamentals and a potential approach to multisensor requirements allocation

Target recognition in uncertain environments is a hot issue. Fusion rules are used to combine the sensor reports from different sources. In this situation, obtaining more information to make correct decision is an essential issue. Probability distribution is one of the most used methods to represent uncertainty information. In addition, the negation of probability distribution provides a new view to represent the uncertainty information. In this article, the existing negation of probability distribution is extended with Tsallis entropy. The main reason is that different systems have different parameter q. Some numerical examples are used to demonstrate the efficiency of the proposed method. Besides, the article also discusses the application of negation in target recognition based on sensor fusion to further demonstrate the importance of negation.

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Model-based, multi-sensor fusion and bundle-adjustment for image registration and target recognition

10.1117/12.665795 ◽

2006 ◽

Author(s):

Eugene M. Lavely ◽

Misha Barmin ◽

Vladimir Kaufman ◽

Erik Blasch

Keyword(s):

Image Registration ◽

Sensor Fusion ◽

Target Recognition ◽

Bundle Adjustment ◽

Model Based

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

Symmetry ◽

10.3390/sym12091435 ◽

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.

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