Open AccessOptimal Weight and Parameter Estimation of Multi‐structure and Unequal‐Precision Data Fusion
Author(s) -
Wang Jiongqi,
He Zhangming,
Zhou Haiyin,
Li Shuxing,
Zhou Xuanying
Publication year - 2017
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.09.030
Subject(s) - estimation , fusion , computer science , sensor fusion , statistics , algorithm , mathematics , econometrics , artificial intelligence , economics , philosophy , linguistics , management
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.