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Multi-sensor Optimal Estimation and Fusion Algorithm

Author: XiaoLei
Tutor: LiuGuiXi
School: Xi'an University of Electronic Science and Technology
Course: Circuits and Systems
Keywords: Multi-sensor system Optimal Estimation Fusion algorithm UKF
CLC: TP212.9
Type: Master's thesis
Year: 2009
Downloads: 301
Quote: 4
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Abstract


With the development of sensor technology, communications technology , various multi-sensor system for complex applications background more and more attention in the multi-sensor system , the state fusion estimation is a very multi-sensor information fusion theory important area of ??research , information on how to take advantage of multiple sensors more accurate estimation of the state of the system is widely used in tracking the field and other fields that require accurate estimates . The research content of this paper is a multi-sensor information fusion theory in the theory of optimal estimation fusion algorithm and conducted in-depth analysis and research on the FAQ . Introduced the first practical application of the more common system structure , elaborated a comparative analysis of fusion algorithm and its performance in distributed and centralized system structure . Paper systematic study of the various measurement sensors related to the multi- sensor fusion system for measuring noise correlation matrix is determined , and the matrix diagonal matrix by similarity transformation into a multi-sensor system , given the optimal centralized , distributed estimated , and this basis , proposed a centralized optimal fusion estimated equivalent new algorithm , this algorithm can reduce the amount of computation in the case of the conditions are met , there is a certain practicality . The simulation results demonstrate the optimal fusion estimation algorithm with centralized equivalence . State fusion estimation problem for nonlinear dynamic systems , conditions under the minimum mean square error (MMSE) weighting method , and proposed weighted fusion algorithm based on unscented Kalman filter (UKF) , studies have shown that this fusion the algorithm is able to overcome the extended Kalman filter (EKF) estimated in the lack of integration in the state , you can get a more accurate state fusion estimation results , and two fusion algorithm simulation compared to verify the good performance of the based on UKF weighted fusion algorithm .

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