The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysi...The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.展开更多
By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets ...By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.展开更多
In this paper, distributed Kalman filter design is studied for linear dynamics with unknown measurement noise variance, which modeled by Wishart distribution. To solve the problem in a multi-agent network, a distribut...In this paper, distributed Kalman filter design is studied for linear dynamics with unknown measurement noise variance, which modeled by Wishart distribution. To solve the problem in a multi-agent network, a distributed adaptive Kalman filter is proposed with the help of variational Bayesian, where the posterior distribution of joint state and noise variance is approximated by a free-form distribution. The con vergence of the proposed algorithm is proved in two main steps: n oise statistics is estimated, where each age nt only use its local information in variational Bayesian expectation (VB-E) step, and state is estimated by a consensus algorithm in variational Bayesian maximum (VB-M) step. Finally, a distributed target tracking problem is investigated with simulations for illustration.展开更多
We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis.The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous ...We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis.The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous dynamical models. The sufficient and necessary condition for the observability of the whole system is given with detailed proof. By local information and measurement communication, we design a novel distributed suboptimal estimator based on the Kalman filtering technique for comparison with a centralized optimal estimator. We present sufficient conditions for its convergence with respect to the topology of the network and the numerical solutions of n linear matrix inequality（LMI） equations combining system parameters. Finally, we perform several numerical simulations to verify the effectiveness of the given algorithms.展开更多
This paper presents the design of a new event-triggered Kalman consensus filter(ET-KCF)algorithm for use over a wireless sensor network(WSN).This algorithm is based on information freshness,which is calculated as the ...This paper presents the design of a new event-triggered Kalman consensus filter(ET-KCF)algorithm for use over a wireless sensor network(WSN).This algorithm is based on information freshness,which is calculated as the age of information(Aol)of the sampled data.The proposed algorithm integrates the traditional event-triggered mechanism,information freshness calculation method,and Kalman consensus filter(KCF)algorithm to estimate the concentrations of pollutants in the aircraft more efficiently.The proposed method also considers the influence of data packet loss and the aircraft's loss of communication path over the WSN,and presents an Aol-freshness-based threshold selection method for the ET-KCF algorithm,which compares the packet Aol to the minimum average Aol of the system.This method can obviously reduce the energy consumption because the transmission of expired information is reduced.Finally,the convergence of the algorithm is proved using the Lyapunov stability theory and matrix theory.Simulation results show that this algorithm has better fault tolerance compared to the existing KCF and lower power consumption than other ET-KCFs.展开更多
基金supported in part by Sichuan Science and Technology Program under Grant No.2025ZNSFSC151in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27030201+1 种基金the Natural Science Foundation of China under Grant No.U21B6001in part by the Natural Science Foundation of Tianjin under Grant No.24JCQNJC01930.
文摘The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.
文摘By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.
基金National Natural Science Foundation of China (Nos. 61733018, 61573344).
文摘In this paper, distributed Kalman filter design is studied for linear dynamics with unknown measurement noise variance, which modeled by Wishart distribution. To solve the problem in a multi-agent network, a distributed adaptive Kalman filter is proposed with the help of variational Bayesian, where the posterior distribution of joint state and noise variance is approximated by a free-form distribution. The con vergence of the proposed algorithm is proved in two main steps: n oise statistics is estimated, where each age nt only use its local information in variational Bayesian expectation (VB-E) step, and state is estimated by a consensus algorithm in variational Bayesian maximum (VB-M) step. Finally, a distributed target tracking problem is investigated with simulations for illustration.
基金supported by the National Natural Science Foundation of China(No.61503335)the Key Laboratory of System Control and Information Processing,China(No.Scip201504)
文摘We study the distributed Kalman filtering problem in relative sensing networks with rigorous analysis.The relative sensing network is modeled by an undirected graph while nodes in this network are running homogeneous dynamical models. The sufficient and necessary condition for the observability of the whole system is given with detailed proof. By local information and measurement communication, we design a novel distributed suboptimal estimator based on the Kalman filtering technique for comparison with a centralized optimal estimator. We present sufficient conditions for its convergence with respect to the topology of the network and the numerical solutions of n linear matrix inequality（LMI） equations combining system parameters. Finally, we perform several numerical simulations to verify the effectiveness of the given algorithms.
基金Project supported by the Civil Aviation Science and Technology Project(No.MHRD20150220)the Fundamental Research Funds for the Central Universities,China(No.3122017003)the Natural Sciences and Engineering Research Council of Canada。
文摘This paper presents the design of a new event-triggered Kalman consensus filter(ET-KCF)algorithm for use over a wireless sensor network(WSN).This algorithm is based on information freshness,which is calculated as the age of information(Aol)of the sampled data.The proposed algorithm integrates the traditional event-triggered mechanism,information freshness calculation method,and Kalman consensus filter(KCF)algorithm to estimate the concentrations of pollutants in the aircraft more efficiently.The proposed method also considers the influence of data packet loss and the aircraft's loss of communication path over the WSN,and presents an Aol-freshness-based threshold selection method for the ET-KCF algorithm,which compares the packet Aol to the minimum average Aol of the system.This method can obviously reduce the energy consumption because the transmission of expired information is reduced.Finally,the convergence of the algorithm is proved using the Lyapunov stability theory and matrix theory.Simulation results show that this algorithm has better fault tolerance compared to the existing KCF and lower power consumption than other ET-KCFs.
