In order to expand the scope of exterior trajectory measurement system, it is required to make full use of various heterologous measuring elements to establish a joint positioning model with multi-system measurement information to make up the traditional positioning method. Meanwhile, there must be many redundant observations in the tracking measurement. It will improve the accuracy and continuity of target tracking measurement and optimize the comprehensive measurement level of the measurement and control system and unit efficiency by making rational use of these redundant measurements and processing with data fusion algorithm. The measuring angle and range of optical measuring unit and constraint equation of GNSS pseudo range measurement information were discussed in this paper. Optical and GNSS joint measurement model was established. The least square solution of this nonlinear equation was obtained with Newton iteration method. Finally, simulation data was used to verify the result. The result shows that the target position can be calculated by making use of the measured information of optical and GNSS unit in the minimum positioning condition, and the accuracy of fusion calculation result will improve correspondingly when there are many redundant measurement elements.
For an optical measurement data location abnormal in aerospace Monitoring and Control network, Establish a fusion algorithm based on fuzzy support, combined with the measurement data of the same arc tracking device. This method can effectively calculated the contribution of dynamic weight value, Through reasonable weight distribution, the accurate determination of flight target parameters can be achieved, and the impact of abnormal data on ballistic parameters is avoided, and the characteristics of flight targets are actually described. By application , this method overcomes the weight arithmetic mean method, such as the disadvantages of ballistic parameters is still abnormal after fusion, reduce the uncertainty of the ballistic parameters, restrain the deviation and improve the accuracy of data processing.