Moving object in Markov stochastic processes: spacial characteristic analysis and approximate approach research

In this paper, space approach of moving object in a kind of Markov stochastic process is studied. A state transfer function of Markov stochastic model in space-time network is deduced by mathematic method firstly. The space-time network is defined as a three-dimensional space which is formed from moving objects and their trajectories, and the corresponding distance space is constructed. Then the fixed point theorem is presented. A self-mapping operator in the distance space is attained by the analysis of state transfer function. On the basis of the above theory, the moving object can be mapped from the former node to the target one by themselves. All of these are proved by theorems and emulational experiments. Moreover, we attempt to use the method of space decompounds with granularity in moving object. The moving object can be approached better through the mapping of fixed point, and the requirements of moving objects are satisfied in real time. The relevanr experiments also validate the feasibility and validity of space approach idea.