Scalable Distributed State Estimation for a Class of State-Saturated Systems Subject to Quantization Effects

Abstract

This paper investigates the problem of a scalable distributed state estimation for a class of discrete time-variant systems with state-saturation, quantization effects, and two redundant channels over a sensor network. In transmission data from a sensor to its estimator, two phenomena are considered together. First, the data of each sensor is transmitted to its estimator through two redundant communication channels. Second, innovation data is quantized before being used by the estimator. These phenomena are beneficial in alleviating the negative effects on measurements and reducing the energy consumption and bandwidth. In the structure of proposed filter consensus is used on estimations in which consensus is first achieved on the prediction estimation, then the accuracy of computed estimation is improved by two recursive equations. The parameters of the proposed filter are obtained for each sensor node by employing an upper bound for common error covariance, therefore less computational burden is required. Eventually, the comparative simulation results are presented to show that our method has better performance compared with a rival one recently published.

Partial‐nodes‐based state estimation for state‐saturated complex networks with random mixed couplings under dynamic event‐triggered protocol

“…[10][11][12][13] However, the state variables may be limited in a bounded set during solving such problem due to the device suffers some physical restrictions and so on. 14 As it is well recognized, fixed saturation will lead to nonlinearity characteristics, which would severely restrict the application of existing SE scheme, 15,16 which motivates the current study on the state-saturated SE problem. In Reference 17, the fusion SE strategy has been proposed for discrete CNs under state saturation.…”

Event‐triggered resilient distributed extended Kalman filter with consensus on estimation

“…As a popular method, consensus-based distributed filters can be mainly classified into the following three categories: (i) consensus on estimation (CE) filter has been considered by adding a consensus term with a predefined gain in the traditional Kalman filter. [2][3][4] The main drawback of the CE filter is that the covariance information is not employed efficiently in a distributed manner (ii) consensus on measurement (CM) filter in which the consensus is obtained on the local measurements and innovation covariances. However, it requires high communication costs and it does not assure convergence unless the number of consensus iterations are sufficiently large.…”