Volume 30 Issue 11
Nov.  2015
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Article Navigation >  Journal of Aerospace Power  > 2015  >  30(11): 2779-2786
ZHANG Xi-shan, HUANG Kao-li, YAN Peng-cheng, LIAN Guang-yao, LI Zhi-yu. Testability prior information fusion method based on uncertainty measure and supporting degree[J]. Journal of Aerospace Power, 2015, 30(11): 2779-2786. doi: 10.13224/j.cnki.jasp.2015.11.028
Citation: ZHANG Xi-shan, HUANG Kao-li, YAN Peng-cheng, LIAN Guang-yao, LI Zhi-yu. Testability prior information fusion method based on uncertainty measure and supporting degree[J]. Journal of Aerospace Power, 2015, 30(11): 2779-2786. doi: 10.13224/j.cnki.jasp.2015.11.028
shu

Testability prior information fusion method based on uncertainty measure and supporting degree

doi: 10.13224/j.cnki.jasp.2015.11.028

  • 1. Department Four, Ordnance Engineering College, Shijiazhuang 050003, China;

  • 2. Research Room One, Ordnance Technological Research Institute, General Armament Department, the Chinese People's Liberation Army, Shijiazhuang 050003, China;

  • 3. 66010 Unit, the Chinese People's Liberation Army, Shijiazhuang 050003, China

  • Received Date: 2014-04-23
  • Publish Date: 2015-11-28
    Abstract
  • To overcome the shortcoming of subjectivity and unicity in those fusion methods, the uncertainty measure and supporting degree of prior distribution was introduced; and the prior information weighting fusion was proposed on the basis of the uncertainty measure combined with the supporting degree. First, the uncertainty of testability prior information was described using β distribution and the prior distribution hyper-parameter was determined by different prior information. Then, the uncertainty measure and supporting degree was proposed as the weight coefficient of prior information, and the corresponding fusion algorithm was designed. Finally, using the mixed prior distribution and combining with the testability field test data, the posterior results of Bayes fusion evaluation were caculated. This method considered both the proximity of the measure parameter to the real situation, and also the level of supporting degree between different prior information. The example shows that the lower limit of confidence estimation based on the proposed method increases 0.7% than the traditional method at the same confidence.

     

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