Research on the Relationship between Information Fusion Method and Information Failure Mode in Integrated Navigation System

Han Binzi(School of Electrical Engineering, Naval University of Engineering, Wuhan, Hubei)
Hu Baiqing(School of Electrical Engineering, Naval University of Engineering, Wuhan, Hubei)

Abstract

Abstract：On the basis of the basic principles of weighted fusion, Kalman filtering and BP neural networks, the basic principles of information fusion methods used in integrated navigation systems are expounded. Through the analysis of the basic principles, the association of information fusion methods commonly used in integrated navigation systems and information failure modes is obtained: the information fault mode of weighted fusion method The model is closely related to the specific weight allocation method, which depends on the fault mode of the sensor or sub-system in which the weight is dominant; the information fault mode of the Kalman filtering information fusion method is a continuous mutation fault corresponding to the nonlinear time interval of the system; the information fault mode of the BP neural network method is gradual with time. The information failure mode of the BP neural network method is a slowly varying fault that gradually accumulates over time. Starting from the complexity associated with the information fusion method and the information failure mode, it is pointed out that in order to systematically express the relationship between the information fusion method and the information failure mode, further research can be carried out.

Keywords

Keywords: Integrated navigation system; Information fusion; Information failure mode

References

Tianlai Xu. Research and System Implementation of Vehicle Integrated Navigation Information Fusion Algorithm[D]. Harbin: Institute of Flight Dynamics and Control, 2007: 3-12. (in Chinese)

Yongyuan Qin. Kalman Filtering and Integrated Navigation Principle[M]. Xi'an: Northwestern Polytechnical University Press,1998. (in Chinese)

Xueyuan Lin, Rongbing Li, Qingwei Gao. Integrated Navigation and Information Fusion Method[M]. National Defence Industry Press,2017. (in Chinese)

Huaqiang Zhang, Dongxing Li, Guoqiang Zhang.Application of Hybrid Detection X2 Method in Fault Detection of Integrated Navigation System[J].Journal of Chinese Inertial Technology,2016,24 (5):696-700. (in Chinese)

Fang Zhou, Liyan Han.Overview of Multi-sensor Information Fusion Technology[J].Telemetry Remote Control,2006,27(3). (DOI:10.3969/j.issn.2095-1000.2006.03.001)

Xin Wen, Xingwang Zhang, Yaping Zhu, Xinzhu Li. Intelligent Fault Diagnosis Technology: MATLAB Application[M]. Beijing: Beijing University of Aeronautics and Astronautics Press, 2015. (in Chinese)

Chongquan Zhong, Liyong Zhang, Suying Yang, Wenhao Zhao. Multi-sensor Grouping Weighted Fusion Algorithm[J].Journal of Dalian University of Technology, 2002, 42(2): 242-245. (DOI:10.3321/j.issn:1000-8608.2002.02.024)

Shengli Wu, Yaxin Bi, Xiaoqin Zeng, Lixin Han. Assigning Appropriate Weights for the Linear Combination Data Fusion Method inInformation Retrieval[J].InformationProcessingandManagement,2009, 45: 413-426. (in Chinese)

Wu S, McCleanS. Data Fusion with Correlation Weights.Proceedings of the 27th European Conference on Information Retrieval, 2005, 275-286. (in Chinese)

Wei Li, Pengju He, Shesheng Gao. Research on Multi-sensor Weighted Information Fusion Algorithm[J].Journal of Northwestern Polytechnical University, 2010, 28(5):674-678. (DOI:10.3969/j.issn.1000-2758.2010.05.007)

Jiaxing Sun, Xiaolin Zhang, Bing Hou. Application of ABC Optimized BP Neural Network Algorithm in Integrated Navigation[J].Telemetry Remote Control,2016,37(5):40-48. (DOI:10.3969/j.issn.2095-1000.2016.05.008)

DOI: http://dx.doi.org/10.26549/met.v2i2.853