Systems Engineering and Electronics ›› 2018, Vol. 40 ›› Issue (1): 9-16.doi: 10.3969/j.issn.1001-506X.2018.01.02

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State prediction method of radar seeker based on the unification of local relevance vector machine model

LU Cheng1, XU Tingxue1, WANG Hong2   

  1. 1. Coastal Defense College, Naval Aeronautical and Astronautical University, Yantai 264001, China; 
    2. The 55th Institute, Joint Staff Department, Beijing 100094, China
  • Online:2018-01-08 Published:2018-01-08

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Abstract:

For the problems of complex mechanical and electrical structure of radar seeker, insufficient utilization rate of test data information and low accuracy of the traditional state prediction method based on data driven, based on the relevance vector machine (RVM) and the Dempster-Shafer (D-S) evidence theory, a state prediction method is proposed based on evidence fusion and improved local RVM (LRVM). Firstly, to improve the standard RVM regression model, variance Gauss kernel function (VGKF) is constructed to improve the global performance and generalization ability of kernel function. Then, by using the chaotic sequence local prediction selection method of the number of neighboring points of the law, the training space prediction of embedding dimension is optimized by Hannan-Quinn (H-Q) criterion. The blindness of subjective selection is avoided and the improved LRVM model is constructed. Finally, the LRVM is improved by using the homology equipment test data with approximate degradation law. Based on the D-S evidence theory, the prediction results of the two models are fused, and a united LRVM (U-LRVM) model is established. The feasibility and superiority of the proposed method are verified by an example of the correlation parameters of the seeker.

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