Multi-sensors Information Fusion State Estimation For Unreliable Network Transmission Systems

Nowadays,computer technology and sensor technology are developing rapidly.That's what makes multi-sensor information fusion technology as a research hotspot.It has attracted a lot of attention and applied to many fields,such as the identification,the intelligent robot,the criminal investigation,the industrial monitoring,the remote sensing technology,and etc.Compared with the single sensor,the multi-sensor information fusion estimation has its own unique advantages.It has considerable development prospects.In this paper,we mainly investigate the multi-sensor information fusion state estimation for the systems with time-delays,packet losses,or multi-rate characteristics.Some efficient techniques,such as the observation reorganization technique,the binary probability method,and the lifting technology,are introduced in this thesis to deal with the time delay,packet losses,and multi-rate characteristics,respectively.Three kinds of distributed estimation methods are designed based on the weighted algorithm,the consensus algorithm,and the sequential algorithm subject to different unreliable network transmission systems.The simulation verification and convergence analysis of the algorithms are given,and the good estimation results are achieved.The main completed works are listed as follows:Firstly,the multi-rate system and the system with random delay are modeled.The multi-rate system is transformed into a single rate system by using the lifting technique,while the random observation delay system is transformed into a delay-free one via the observation reorganization technique.The established system model is combined with sequential fusion algorithm for the first time.The gain in the algorithm keeps the same dimension as the original system.The algorithm have small calculation and high precision,and the gain is the same as the original system.The simulation examples are given to illustrate the validity of the proposed algorithms.Secondly,we investigate the distributed state estimation for the random delay systems based on weighted algorithms.Three weighted fusion algorithms are summarized.And the scalar weighted fusion algorithm is used for the first time to combine the previously obtained time-delay systems,which the observation was reorganized.Finally,a distributed state estimation algorithm based on scalar weighted is proposed.The convergence and stability analysis of the algorithm are given.And a simulation example is presented to illustrate the efficiency of the proposed estimation method.Thirdly,we investigate the distributed state estimation for the systems with packet losses based on the consensus method.The related knowledge of graph theory is introduced,and the packet loss system is modeled.Combining it with the consensus method innovatively,a distributed state estimation is proposed.A detailed proof of the convergence and stability of the algorithm are given.Then the simulation example is given to show the efficiency of the method.Finally,we investigate the adaptive filter for the systems with observation delays and addition noises,where the statistical characteristics of the noises are unknown.Consider the practical systems in which the statistic characteristics of the system noise and observation noise are unknown and the observation are suffered transmission delays,we first transform the multi-channel observation delay system into the multi-channel delay free system by the reorganization observation method.Then based on the transformed delay free system and combined with the maximum likelihood estimation and white noise estimation method,two kinds of adaptive filters are developed.Finally,the simulation examples are given to illustrate the efficiency of the proposed method.