Multi-sensor Information Fusion Method Based on BP Neural Network
DOI:
https://doi.org/10.3991/ijoe.v12i05.5738Keywords:
Information fusion, BP neural network, Bayesian inference, Probability spaceAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.