Research Of Data Fusion Networks System Spatio-Temporal Synchronization

Data fusion and dynamical networks are developing very quickly nowadays. These two techniques are involved in natural science, social science and almost all other fields. Furthermore this technique has showed its wide prospects of application. In recent years, the discovery of small-world and scale-free characteristics in dynamical networks induces new challenges and opportunities for data fusion dynamical networks. Data fusion and dynamic networks are supplement for each other. Data fusion is based on dynamical networks and data fusion can improve efficiencies of dynamical networks. When data fusion and dynamic networks are combined, we must fix the spatio-temporal synchronization. Time registration and spatial registration are discussed separately in data fusion. Synchronization in dynamic networks means the stability of the nodes.Firstly, in this article we discuss time synchronization of data fusion. Time synchronization algorithm under the condition of different sample period and unfixed sample period among different sensors is studied. We use extrapolation to synchronize the point data to the public period point of fusion centre. We can fix optimization fusion center period based on the least mean square of registration error. When there are interrupted points between the sample data of each sensor, we preprocess the discontinuity point data and reset the fusion centre period. The simulation of the time synchronization is presented.Secondly, we spatially synchronize the data fusion system and the Gauss- Krüger coordinates is used to change measured data from coordinate frame to data fusion coordinate frame. The Gauss- Krüger coordinates take ellipse characteristic into consideration, so it can improve the measuring accuracy and simulations are presented. Also the comparison between Gauss-Krüger coordinate and right angle coordinate is presented.Thirdly, the synchronization theory of scale-free dynamical networks is discussed. We establish the scale-free dynamical networks model and design parameters for the scale-free dynamical networks model which include coupling frame matrix, inner linked matrix and coupling strength. State synchronization of the scale-free dynamical networks means state vector of every nodes results in the same solution with time. Scale-free dynamical networks state synchronization can be characterized by the second-large characteristic value of the coupling configuration matrix when we discuss the node synchronization theory. Simulations of the synchronization theory are presented, through which we can study the synchronization, robustness and fragility of scale-free dynamical networks.Finally, combining data fusion and dynamical networks we study the spatio-temporal synchronization. We design the state vector, coupling strength, coupling configuration matrix and inner linked matrix that are involved. We hope to discuss spatio-temporal synchronization of data fusion based on dynamical networks model instead of discussing the time registration and spatial registration separately. In this paper we discuss data fusion and dynamical networks spatio-temporal synchronization theory in terms of ideology and make foundations for future research.