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

Abstract

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.

MABAC method for multiple attribute group decision making under single-valued neutrosophic sets and applications to performance evaluation of sustainable microfinance groups lending

“…The MADM is a kind of very complex optimization decision-making problem, which often occurs in the field of decision engineering, economics, military, and public management [1][2][3][4][5]. In the process of MADM, the value of decision-making attributes often has information uncertainty, and the data is difficult to estimate, predict, and evaluate [6][7][8][9][10][11][12][13]. This is more general than the decision attribute value is accurate data and clear information.…”

Target Recognition Algorithm Based on Optical Sensor Data Fusion

“…Experimental Design. The optical sensor target recognition experiment is based on the distance between the target identifier and the unmanned cart measured by the sensor, and the sensor performance directly affects the accuracy of the measured distance [29][30][31][32][33]. To test the performance of the sensor, experimental verification of the performance of individual sensors is required.…”

CBA: Multi Source Fusion Model for Fast and Intelligent Target Intention Identification

“…With the in-depth development and extensive application of artifical intelligence (AI) technology, researchers have found that the deep learning network technology can simulate the cognitive model and reasoning process of commanders [9][10][11][12], which provides a good theoretical basis for target intention recognition. For example, Xue et al constructed a panoramic convolution long and short term memory (PCLSTM) neural network model to reduce the neural network parameters from the time series, thus improving the intention recognition accuracy [13].…”