Optimal Weight and Parameter Estimation of Multi‐structure and Unequal‐Precision Data Fusion. Issue 6 (1st November 2017)

• Record Type: Journal Article
• Authors: Wang, Jiongqi; He, Zhangming; Zhou, Haiyin; Li, Shuxing; Zhou, Xuanying
• Is Part Of: Chinese journal of electronics. Volume 26:Issue 6(2017)
• Page Start: 1245
• Page End: 1253
• Publication Date: 2017-11-01
• Subjects: Data fusion -- Multi‐structure -- Regression model -- Bayes estimation -- Optimal weight -- Parameter estimation.; Gaussian processes -- Markov processes -- parameter estimation -- regression analysis -- sensor fusion; optimal weight estimation -- parameter estimation -- multi‐structure data fusion -- unequal‐precision data fusion -- data process precision -- Gauss‐Markov theorem -- nonlinear regression model; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2017.09.030
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : Measured data fusion process is an effective way to improve the data process precision. In this paper, the fusion weight is firstly introduced, and then we study the optimal weight and parameter estimation using multistructure and unequal‐precision data fusion. For the linear regression model, it is theoretically proved that the optimal weight is only related to the data measure precision, which is consistent with the classical Gauss‐Markov theorem. For the nonlinear regression model, we analyze the method for calculating the optimal weight theoretically, and then provide the algorithm for the optimal weight and the parameter estimation for the actual data fusion.