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Distributed fusion system with unknown input filtering and application

Author: BaiJinHua
Tutor: SunShuLi
School: Heilongjiang University
Course: Control Theory and Control Engineering
Keywords: Unknown input Random deviations Distributed Fusion Estimation Self-tuning fusion estimation Cross- covariance matrix
CLC: TP212.9
Type: Master's thesis
Year: 2008
Downloads: 52
Quote: 3
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Abstract


Unknown input , interference or bias random system state estimation issues of control , communication , signal processing and fault diagnosis . Multi - sensor environment , different sensors may be affected by the impact of different interference input . Study with unknown inputs system state and input estimation problem in terms of theory and engineering practice has important significance . In this paper, with unknown input system distributed information fusion state estimation algorithm with random deviations the Information Fusion estimators design, with the design of unknown input information fusion valuation , with unknown noise statistics with random system deviation the valuation of self- correction information fusion design . Discrete linear stochastic system with random deviations , augmented by the dynamic system into a special case of the multi- model multi-sensor system . Based on a common linear minimum variance optimal weighted fusion estimation algorithm , respectively distributed optimal weighted fusion Kalman state filter system deviation filter . When a system containing an unknown noise statistics distributed noise statistical identification algorithm is given based on the correlation function , and thus gives the state integrated structure with two pieces of self - correction information fusion Kalman filter and the system deviation filter . Discrete linear stochastic system with unknown input , not unknown input conditions of any a priori information , does not depend on the unknown input linear unbiased minimum variance state filters . When systems with multiple sensors to derive the formula of the cross-covariance matrix between any two local estimation error , based on the linear minimum variance optimal weighted fusion algorithm with unknown input discrete system or / and sensor linear systems , distributed Optimal weighted information fusion state filter . Sensor without deviation , with random deviations or I do not know any a priori information about the unknown input multi-sensor system , push got any local estimation error cross between two sensor covariance matrix , and then gives the linear minimum variance fusion state filter. Fault detection technology used in the multi-sensor systems , distributed information fusion estimation algorithm with fault detection , and gives the corresponding distributed fusion structure .

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