Sensor management based on collaborative information fusion algorithm for gas source localisation

2018 ◽  
Vol 28 (4) ◽  
pp. 242
Author(s):  
Yong Zhang ◽  
Liyi Zhang ◽  
Jianfeng Han ◽  
Yi Yang ◽  
Xinyuan Ma
Keyword(s):  
Information Fusion ◽  
Sensor Management ◽  
Fusion Algorithm ◽  
Source Localisation ◽  
Gas Source

Sensor management based on collaborative information fusion algorithm for gas source localisation

2018 ◽  
Vol 28 (4) ◽  
pp. 242
Author(s):  
Xinyuan Ma ◽  
Yi Yang ◽  
Jianfeng Han ◽  
Liyi Zhang ◽  
Yong Zhang
Keyword(s):  
Information Fusion ◽  
Sensor Management ◽  
Fusion Algorithm ◽  
Source Localisation ◽  
Gas Source

Maximum weight multi-modal information fusion algorithm of electroencephalographs and face images for emotion recognition

2021 ◽  
Vol 94 ◽  
pp. 107319
Author(s):  
Mei Wang ◽  
Ziyang Huang ◽  
Yuancheng Li ◽  
Lihong Dong ◽  
Hongguang Pan
Keyword(s):  
Emotion Recognition ◽  
Information Fusion ◽  
Maximum Weight ◽  
Fusion Algorithm ◽  
Face Images

An Information Fusion Algorithm Based on Kalman Filtering

2013 ◽  
Vol 444-445 ◽  
pp. 1072-1076
Author(s):  
Xiu Hu Tan
Keyword(s):  
Mathematical Model ◽  
Information Fusion ◽  
Minimum Mean Square Error ◽  
Minimum Variance ◽  
Probability Method ◽  
Fusion Algorithm ◽  
Multisensor Systems ◽  
Optimal Information ◽  
Noise Statistics ◽  
The Mathematical Model

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

scholarly journals Multi-sensor Information Fusion Method Based on BP Neural Network

2016 ◽  
Vol 12 (05) ◽  
pp. 53 ◽  
Author(s):  
Lin Liandong
Keyword(s):  
Neural Network ◽  
Bayesian Inference ◽  
Information Fusion ◽  
System Development ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fusion Algorithm ◽  
Temperature Detection ◽  
Posterior Expectation ◽  
Sensor Information

This study aims to solve the problem of multi-sensor information fusion, which is a key issue in the multi-sensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.

An Information Fusion Algorithm Based on Chirplet-Hough Transformation

2012 ◽  
Vol 7 (19) ◽  
pp. 426-433 ◽  
Author(s):  
Deng Minghui ◽  
Zeng Qingshuang ◽  
Zhang Lanying
Keyword(s):  
Information Fusion ◽  
Fusion Algorithm ◽  
Hough Transformation
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