Research On State Estimation Algorithm With Correlated Noise System In Network Environment

In recent years,research on the networked systems has attracted considerable attention due to their wide application domains such as space exploration,target tracking and chemical technology,industrial monitoring,communication and other areas.Since the transmission of signals depends on the communication network,systems are unavoidably affected by the network environment,and networked-induced delay,fading measurements,packet dropouts and other stochastic phenomena are common in data transmission of practical networked systems.In addition,suppose that the systems are discretized versions of continuous systems with noises,the environment where the systems are polluted by the same noise source,the system models are transformed and other factors,the noises are often correlated or even finite-step correlated.Based on the questions above,this paper studies optimal estimators for networked systems with correlated noises.The main research includes the following aspects:Firstly,for a class of discrete time-varying linear systems with fading measurements and correlated noises,the fading probabilities are regulated by probability mass functions in a given interval.Furthermore,time-delay exists in the system state and observation simultaneously.Additionally,the multiplicative noises are considered to describe the uncertainty of the state.Based on the projection theory,the linear minimum variance optimal linear estimators are presented in the paper.Compared with traditional augmented state method,the new algorithm is finite-dimensionally computable and does not increase computational and storage load with time when the delay is large.Furthermore,for the complex NCSs with fading measurements,finite-step correlated noises and multi-step random measurement delays,the correlations between the noise and state,the noise and observation,the noise and innovation,the innovation and state,and the innovation and observation are analyzed based on the step number of correlated noises.Moreover,the recursive formulas of correlation matrices are derived.The linear minimum variance optimal linear estimators,including filter,predictor and smoother,are presented by using projection theory.Besides,this paper also focus on the optimal linear estimation problems for a class of networked descriptor systems with multiple packet dropouts,measurement multiplicative noises and finite-step correlated process and measurement noises.Based on the fast-slow subsystem decomposition approach(FSD),the descriptor system is transformed into two reduced-order linear nonsingular subsystems with finite-step correlated noises.Optimal linear estimators including filter,predictor and smoother with corresponding estimation error covariance matrices for the states and noises of new systems are presented by using the innovation analysis approach.Then,the optimal linear estimators of the original descriptor system are obtained.Finally,several simulation examples show the effectiveness of the proposed algorithms in this paper.