Multi-Sensor Information Fusion Algorithm with Central Level Architecture for Intelligent Vehicle Environmental Perception System

2016 ◽  
Author(s):  
Siyu Chen ◽  
Libo Huang ◽  
Jie Bai ◽  
Haitao Jiang ◽  
Liang Chang
Keyword(s):  
Information Fusion ◽  
Environmental Perception ◽  
Intelligent Vehicle ◽  
Fusion Algorithm ◽  
Perception System ◽  
Sensor Information ◽  
Central Level

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.

scholarly journals Based on Multi-sensor Information Fusion Algorithm of TPMS Research

2012 ◽  
Vol 25 ◽  
pp. 786-792 ◽  
Author(s):  
Zhou Yulan ◽  
Zang Yanhong ◽  
Lin Yahong
Keyword(s):  
Information Fusion ◽  
Fusion Algorithm ◽  
Sensor Information

The Application of Distributed Multi-Sensor Information Fusion Technology in Underwater Vehicle

2012 ◽  
Vol 532-533 ◽  
pp. 1006-1010 ◽  
Author(s):  
Ye Li ◽  
Yan Qing Jiang
Keyword(s):  
Mathematical Model ◽  
Kalman Filtering ◽  
Information Fusion ◽  
Underwater Vehicle ◽  
System Structure ◽  
Experimental Result ◽  
Fusion Technology ◽  
Fusion Algorithm ◽  
Single Sensor ◽  
Sensor Information

The application of distributed multi-sensor information fusion technology in accurate positioning of Underwater Vehicle was introduced in this paper. According to the system structure of Distributed multi-sensor in an AUV “T1”, this article establishes the Kalman filtering mathematical model, accomplishes the fusion algorithm based on Kalman filtering and a numerical simulation. The experimental result shows that the application of fusion algorithm based on Kalman filtering can avoid the limitations of a single sensor, reduce its uncertainty impact and increase the confidence level of data.

The Application of Fusion Algorithm Based on Matrix Analysis in Evidence Theory

2014 ◽  
Vol 687-691 ◽  
pp. 1412-1415
Author(s):  
You Zhi Zhang ◽  
Yu Dong Qi ◽  
Han Li Wang
Keyword(s):  
Time Delay ◽  
Information Fusion ◽  
Evidence Theory ◽  
Matrix Analysis ◽  
Fusion Algorithm ◽  
C Language ◽  
Evidence Combination ◽  
Delay Increase ◽  
Recursive Calculation ◽  
Sensor Information

This paper directly adopts evidence reasoning formula to calculate sensor information fusion result. The amount of calculation and calculation time delay increase with the increasing number of target found, uses two recursive calculation ways of evidence combination to calculate results, and proposes a fusion algorithm based on matrix analysis, using matlab software and C language programming to realize the method and calculate by an example. The results prove that the fusion result calculated by the method gets the same result as that of evidence reasoning synthesis formula, but the time needed for calculation will be reduced.

Sign in / Sign up

Export Citation Format

Share Document