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For linear discrete-time stochastic systems with multiple random measurement delays and packet dropouts, we give a new augmented method by introducing measurement outputs into the augmented state vector. Based on this augmented state vector, the optimal linear estimators including filter, predictor and smoother are developed in the linear minimum variance(More)
Using Kalman filtering theory, based on the autoregressive moving average (ARMA) innovation model, the white noise estimator and the measurement predictor, a distributed information fusion Wiener estimator is presented for multichannel ARMA signal with multiple sensors by the matrix weighting fusion algorithm in the linear minimum variance sense. It has the(More)
  • Sun Shuli
  • 2008 27th Chinese Control Conference
  • 2008
Using the projection theory and modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model and white noise estimators, the reduced-order Wiener state estimators for descriptor system with MA colored observation noise and multi-observation lags are presented. They can handle the prediction, filtering and smoothing(More)
This paper studies the distributed optimal fusion estimation problem for multi-sensors systems with multiple measurement delays and packet dropouts. Firstly, we define a new augmented state vector with a lower dimension. Based on the defined state, the local filter (LF) is given. Then, by applying the weighted fusion algorithms in the linear minimum(More)
This paper is concerned with the distributed fusion filtering problem for multi-sensor linear discrete-time stochastic systems with one-step random sensor delay, packet dropout and uncertain observation. Three kinds of uncertain phenomena in measurement data are described by three Bernoulli distributed random variables. Based on the innovation approach, the(More)
Based on three fusion estimation algorithms weighted by matrices, diagonal matrices and scalars, distributed information fusion Kalman filters for system state and bias are given for stochastic systems with unknown stochastic system bias, respectively. When the noise statistical information is unknown, a distributed identification algorithm is given by(More)
Based on the optimal weighted fusion estimation algorithm in the linear minimum variance sense, a distributed information fusion suboptimal filter is given for a multi-sensor multi-delay system. The suboptimal estimation error cross-covariance matrix between any two sensor subsystems for the multi-delay system is derived. The proposed distributed suboptimal(More)
During the operation process of the high voltage circuit breaker, the changes of vibration signals reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode(More)
In sensor networks, sensor measurements may be uncertain due to the impact of environment and different performances of sensors. In this paper, the cross-covariance matrix of prediction errors between any two sensor subsystems is derived for stochastic discrete-time linear systems with uncertain observations by using projection theory. Based on the linear(More)
In order to reduce the influence of relative position deviation and angle deviation between minutia point sets caused by external factors when finger vein images are obtained, a finger vein recognition method using elastic registration is presented. The proposed algorithm is based on an improved neighborhood direction template and oriented filter template(More)