A novel model is developed to describe possible random delays and losses of measurements transmitted from a sensor to a filter by a group of Bernoulli distributed random variables. Based on the new ...developed model, an optimal linear filter dependent on the probabilities is presented in the linear minimum variance sense by the innovation analysis approach when packets are not time-stamped. The solution to the optimal linear filter is given in terms of a Riccati difference equation and a Lyapunov difference equation. A sufficient condition for the existence of the steady-state filter is given. At last, the optimal filter is given by Kalman filter when packets are time-stamped.
This paper is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with random sensor delays, packet dropouts and uncertain observations. We develop a ...unified model to describe the mixed uncertainties of random delays, packet dropouts and uncertain observations by three Bernoulli distributed random variables with known distributions. Based on the proposed model, the optimal linear estimators that only depend on probabilities are developed via an innovation analysis approach. Their solutions are given in terms of a Riccati equation and a Lyapunov equation. They can deal with the optimal linear filtering, prediction and smoothing for systems with random sensor delays, packet dropouts and uncertain observations in a unified framework. Simulation results show the effectiveness of the proposed optimal linear estimators.
For discrete-time stochastic linear systems with bounded random measurement delays and packet dropouts, the optimal estimators including filter, predictor and smoother are developed in the linear ...minimum variance sense based on the innovation analysis approach. Some binary distributed random variables with known distributions are employed to describe the phenomenon of random delays and packet dropouts. Compared with the augmented approach, the computational cost is reduced. Furthermore, the proposed algorithm also gives a suboptimal estimate for systems with unbounded delays and packet dropouts by selecting a sufficient large upper bound. A simulation shows the effectiveness of the proposed algorithms.
In this study, distributed security estimation problems for networked stochastic uncertain systems subject to stochastic deception attacks are investigated. In sensor networks, the measurement data ...of sensor nodes may be attacked maliciously in the process of data exchange between sensors. When the attack rates and noise variances for the stochastic deception attack signals are known, many measurement data received from neighbour nodes are compressed by a weighted measurement fusion algorithm based on the least-squares method at each sensor node. A distributed optimal filter in the linear minimum variance criterion is presented based on compressed measurement data. It has the same estimation accuracy as and lower computational cost than that based on uncompressed measurement data. When the attack rates and noise variances of the stochastic deception attack signals are unknown, a correlation function method is employed to identify them. Then, a distributed self-tuning filter is obtained by substituting the identified results into the distributed optimal filtering algorithm. The convergence of the presented algorithms is analyzed. A simulation example verifies the effectiveness of the proposed algorithms.
A simplified order-reduced sub-region model and the analysis method are proposed to analyze structural damage caused by local failure under sudden disasters. The method firstly divides a complex ...building into sub-regions. Secondly it evaluates the failure degree of sub-regions according to the state of internal components, then analyzes the failure process from macro regional perspective. Finally it summarizes the law of collapse evolution so as to provide guidance for emergency management.
This paper is concerned with the estimation problem for discrete-time stochastic linear systems with multiple packet dropouts. Based on a recently developed model for multiple-packet dropouts, the ...original system is transferred to a stochastic parameter system by augmentation of the state and measurement. The optimal full-order linear filter of the form of employing the received outputs at the current and last time instants is investigated. The solution to the optimal linear filter is given in terms of a Riccati difference equation governed by packet arrival rate. The optimal filter is reduced to the standard Kalman filter when there are no packet dropouts. The steady-state filter is also studied. A sufficient condition for the existence of the steady-state filter is given and the asymptotic stability of the optimal filter is analyzed. At last, a reduced-order filter is investigated.
This paper is concerned with the optimal estimation problem for discrete-time stochastic systems with finite-step auto- and cross-correlated noises and multiple packet dropouts induced by the ...unreliable networks. When a packet transmitted from the sensor to the data processing center is lost, its predictor is used as the compensation. The optimal linear estimators including filter, predictor and smoother that depend on the packet arriving rate are proposed in the linear minimum variance sense via an innovative analysis approach. They are computed in terms of the solutions of some auto- and cross-covariance matrices. A tracking system example is given to demonstrate the effectiveness of the proposed algorithms.
In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving ...rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.
For linear discrete-time stochastic systems measured by multiple sensors, where different sensors are subject to mixed uncertainties of random delays, packet dropouts and/or uncertain observations, ...the centralized fusion linear optimal estimators in the linear minimum variance sense are presented via the innovation analysis approach, which is a general and useful tool to find the optimal linear estimate. The stability of the proposed estimators is analyzed. A sufficient condition for the existence of the centralized fusion steady-state estimators is given. For a single sensor case, the proposed estimators have the simpler forms and the lower computational cost compared to the existing literature, since a lower dimension parameterized system is constructed and the colored noise is avoided. A simulation example verifies the effectiveness of the proposed estimators.
This paper presents the distributed estimation fusion algorithms for a class of multirate multisensor systems with measurement delays. Different sensors uniformly sample measurements with different ...sampling rates and time delays. First, a new state-space model at the measurement sampling points is constructed and the original time delayed system is transformed to a delay-free one with correlated noises in limited time intervals to obtain the real-time estimation. Based on the new state-space model, the optimal local filter at the measurement sampling points is obtained for each single-sensor subsystem, and then the local estimator at the state update points is derived by using the predictor based on the filter at the measurement sampling points. Then, the estimation error cross-covariance matrices between any two local estimators are derived. Finally, the real-time distributed fusion estimator at the state update points is obtained based on the optimal fusion criterion weighed by matrices in the linear unbiased minimum variance sense. Besides, to avoid the correlation between system and measurement noises in the developed state-space model, a simple alternative but nonreal-time estimation fusion algorithm is also presented by employing dummy measurements. A numerical example is given to illustrate the effectiveness of the proposed algorithms.
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