Improved neural network information fusion in integrated navigation system

Lu Ding; Lin Cai; Jia Bin Chen; Chun Lei Song

doi:10.1109/ICMA.2007.4303866

Improved neural network information fusion in integrated navigation system

Lu Ding^*, Lin Cai, Jia Bin Chen, Chun Lei Song

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In order to overcome the limitation of single sensor in vehicle integrated navigation system, cascade fusion architecture is proposed to enhance the reliability of location information. Our research is focus on the algorithm in decision-making level of the fusion architecture, which is used to fuse the information from Global Positioning System (GPS), Kalman filter and Map Matching (MM) to get the precise location. The proposed algorithm in this paper utilizes Particle Swarm Optimizer (PSO) to substitute the traditional Back-Propagation (BP) algorithm in training parameters of neural net. It has more generalization capability. Besides that, it converges stably and is resistant to local optima compared with traditional BP. Test result shows that the proposed algorithm can improve location accuracy by making full use of all sensors' information, and it is robust and effective.

Original language	English
Title of host publication	Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007
Pages	2049-2053
Number of pages	5
DOIs	https://doi.org/10.1109/ICMA.2007.4303866
Publication status	Published - 2007
Event	2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007 - Harbin, China Duration: 5 Aug 2007 → 8 Aug 2007

Publication series

Name	Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007

Conference

Conference	2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007
Country/Territory	China
City	Harbin
Period	5/08/07 → 8/08/07

Keywords

Information fusion
Integrated navigation system
Neural network
Particle swarm optimizer

Access to Document

10.1109/ICMA.2007.4303866

Cite this

Ding, L., Cai, L., Chen, J. B., & Song, C. L. (2007). Improved neural network information fusion in integrated navigation system. In Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007 (pp. 2049-2053). Article 4303866 (Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007). https://doi.org/10.1109/ICMA.2007.4303866

@inproceedings{3850b9bbd5db456589492a722afd77e1,

title = "Improved neural network information fusion in integrated navigation system",

abstract = "In order to overcome the limitation of single sensor in vehicle integrated navigation system, cascade fusion architecture is proposed to enhance the reliability of location information. Our research is focus on the algorithm in decision-making level of the fusion architecture, which is used to fuse the information from Global Positioning System (GPS), Kalman filter and Map Matching (MM) to get the precise location. The proposed algorithm in this paper utilizes Particle Swarm Optimizer (PSO) to substitute the traditional Back-Propagation (BP) algorithm in training parameters of neural net. It has more generalization capability. Besides that, it converges stably and is resistant to local optima compared with traditional BP. Test result shows that the proposed algorithm can improve location accuracy by making full use of all sensors' information, and it is robust and effective.",

keywords = "Information fusion, Integrated navigation system, Neural network, Particle swarm optimizer",

author = "Lu Ding and Lin Cai and Chen, {Jia Bin} and Song, {Chun Lei}",

year = "2007",

doi = "10.1109/ICMA.2007.4303866",

language = "English",

isbn = "1424408288",

series = "Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007",

pages = "2049--2053",

booktitle = "Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007",

note = "2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007 ; Conference date: 05-08-2007 Through 08-08-2007",

}

Ding, L, Cai, L, Chen, JB & Song, CL 2007, Improved neural network information fusion in integrated navigation system. in Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007., 4303866, Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007, pp. 2049-2053, 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007, Harbin, China, 5/08/07. https://doi.org/10.1109/ICMA.2007.4303866

Improved neural network information fusion in integrated navigation system. / Ding, Lu; Cai, Lin; Chen, Jia Bin et al.
Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007. 2007. p. 2049-2053 4303866 (Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Improved neural network information fusion in integrated navigation system

AU - Ding, Lu

AU - Cai, Lin

AU - Chen, Jia Bin

AU - Song, Chun Lei

PY - 2007

Y1 - 2007

N2 - In order to overcome the limitation of single sensor in vehicle integrated navigation system, cascade fusion architecture is proposed to enhance the reliability of location information. Our research is focus on the algorithm in decision-making level of the fusion architecture, which is used to fuse the information from Global Positioning System (GPS), Kalman filter and Map Matching (MM) to get the precise location. The proposed algorithm in this paper utilizes Particle Swarm Optimizer (PSO) to substitute the traditional Back-Propagation (BP) algorithm in training parameters of neural net. It has more generalization capability. Besides that, it converges stably and is resistant to local optima compared with traditional BP. Test result shows that the proposed algorithm can improve location accuracy by making full use of all sensors' information, and it is robust and effective.

AB - In order to overcome the limitation of single sensor in vehicle integrated navigation system, cascade fusion architecture is proposed to enhance the reliability of location information. Our research is focus on the algorithm in decision-making level of the fusion architecture, which is used to fuse the information from Global Positioning System (GPS), Kalman filter and Map Matching (MM) to get the precise location. The proposed algorithm in this paper utilizes Particle Swarm Optimizer (PSO) to substitute the traditional Back-Propagation (BP) algorithm in training parameters of neural net. It has more generalization capability. Besides that, it converges stably and is resistant to local optima compared with traditional BP. Test result shows that the proposed algorithm can improve location accuracy by making full use of all sensors' information, and it is robust and effective.

KW - Information fusion

KW - Integrated navigation system

KW - Neural network

KW - Particle swarm optimizer

UR - http://www.scopus.com/inward/record.url?scp=37049008254&partnerID=8YFLogxK

U2 - 10.1109/ICMA.2007.4303866

DO - 10.1109/ICMA.2007.4303866

M3 - Conference contribution

AN - SCOPUS:37049008254

SN - 1424408288

SN - 9781424408283

T3 - Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007

SP - 2049

EP - 2053

BT - Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007

T2 - 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007

Y2 - 5 August 2007 through 8 August 2007

ER -

Improved neural network information fusion in integrated navigation system

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this