分布式多传感器结构中的数据融合方法

Data fusion method for the configuration of distributed multisensor

在分布式结构中,为了提高单个传感器的测量精度,为数据处理打下基础,首先对每一个传感器进行时间上的分批估计,降低误差的影响,得到各个传感器的局部决策值;接着对方差超过一定数值的数据进行基于相对距离的再处理;最后在最优融合原则下,运行加权自适应算法对各个局部决策值进行融合.数据分析结果表明,处理后的数据更接近测量真实值.

At first, division estimation was applied to the measured data from every single sensor in the system of distributed multi-sensor to improve the accuracy of the single sensor and establish the foundation for next data process, so the effect of error was reduced and the value of local decision-making was obtained. Secondly, further processing based on relative distance was applied to the data whose covariance overruns some value, which has been pre-supposed by man. At last, the adaptive weighted fusion algorithm was applied to all the values of local decision-making according to the optimal weight distribution principle. The analysis shows that the fusion result of this method is closer to the true data than that of the traditional one.