Reliability multi-source information fusion method based on Jeffreys-Matusita distance

doi:10.23919/JSEE.2026.000156

Abstract

Abstract:

The application of Dempster-Shafer evidence theory in multi-source information fusion faces some problems including how to determine the weight of evidence in reliability multi-source information fusion, and how to solve the problem of conflict in Dempster-Shafer evidence fusion. In this paper, Jeffreys-Matusita distance function is introduced to establish the reliability index and consistency index, realize the difference measurement between evidence, determine the reliability degree of evidence, and evaluate the consistency difference of evidence. The distance measurement of multi-source information is achieved with the reassigned weight to avoid conflicts at the time of evidence fusion. In the end, the multi-source information fusion in the development of guided munitions is taken as an example to illustrate the conversion methods for expert experience information, component and subsystem information, reliability growth information, and similar product information, respectively. With the field test information, the multi-source reliability information is fused. The applicability and effectiveness of the proposed method are verified by comparing with the existing multi-source information fusion methods.

Key words: reliability, multi-source information fusion, Dempster-Shafer evidence theory, Jeffreys-Matusita distance

Lisha Zheng, Shiqian Wu, Haobang Liu, Fan Li. Reliability multi-source information fusion method based on Jeffreys-Matusita distance[J]. Journal of Systems Engineering and Electronics, 2026, 37(5): 1703-1720.

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Mass function	Proposition
Mass function	A	B	C
m₁	0.90	0	0.10
m₂	0.88	0.01	0.11
m₃	0.81	0.08	0.11
m₄	0.98	0.01	0.01
m₅	0.90	0.05	0.05

Method	A	B	C	$ \varTheta $
D-S evidence theory	0.9999	0	0.0001	0
Method in [26]	0.9540	0.0130	0.0330	0
Method in [28]	0.8851	0.0107	0.0271	0.0771
Method in [51]	0.6149	0.0180	0.0484	0.3187
Method in [52]	0.8663	0.0174	0.0472	0.0691
The proposed method	0.9244	0.0096	0.0254	0.0406

Mass function	Proposition
Mass function	A	B	C
m₁	0.90	0	0.10
m₂	0.88	0.01	0.11
m₃	0.50	0.20	0.30
m₄	0.98	0.01	0.01
m₅	0.90	0.05	0.05

Method	$ A $	$ B $	$ C $	$ \varTheta $
D-S evidence theory	1	0	0	0
Method in [34]	0.8907	0.0351	0.0742	0
Method in [38]	0.7492	0.0260	0.0547	0.1701
Method in [64]	0.5799	0.0216	0.0562	0.3423
Method in [65]	0.7128	0.0236	0.0658	0.1978
The proposed method	0.9012	0.0101	0.0295	0.0592

Mass function	Proposition
Mass function	A	B	C
m₁	0.90	0	0.10
m₂	0	0.11	0.89
m₃	0.50	0.20	0.30
m₄	0.98	0.01	0.01
m₅	0.90	0.05	0.05