Method Of Multi-sensor Data Fusion Based On Kalman Filtering - Weighting Factor

The Data fusion is a synthesis processing from multi-sensor or multi-source information, to draw a more accurate and credible conclusion. After compare and analysis the multi-sensor data fusion's model function and model structure, and combine the real application, the data fusion technology based on position level is confirmed to used in this dissertation. It can improve tracking stabilization of photoelectricity(OE) tracking and measuring system and then enhance the accurateness of the observation report, reliably provide stable spatial goal data sampling for the off line data processing, thus enhance powerful guarantee for the real-time and off line data processing precision. Considering the concrete instance of the OE tracking and measuring system, it takes the weighted average method as the data fusion method after analyzing the data fusion structure. It pretreats the angle data of the optical sensors and radar sensor, provides time-identical targets data for the weighted fusion node, weights and fuses these data, sends the result to the control system and then helps to control the movement of equipment, and processes the fusion result with Kalman filter to do one step extrapolation. It separately carries on the Kalman filter to the goal in three coordinate axes components, thus obtains one step of extrapolation estimate of the goal and then transforms to the sphere coordinate of the equipment. It links and elects superiorly from the extrapolated data of the weighted fusion result and radar data in the time, gets the reference value of the fusion algorithm (the value is the benchmark that the weighted averaging method calculates the weight). When they appears on the pretreatment stage, pretreatment ensure the reliability of the data, solves the problem of picking up the fake target information on the weighted average stage which can not solve in the pretreatment stage, finally chooses the best value from the filter estimated values of radar data and weighted fusion data. It ensures the requested reference data of the weight -caculating method. It concludes that the smoothness and credibility of the fusion data are...