基于平均值迭代的多目标数据融合算法

摘要

针对多平台多目标量测数据的通信、量测等方面的不确定性,提出了一种基于平均值迭代法的多平台集中式数据融合算法.该方法首先采用最近邻法进行多平台多传感器数据关联,进而利用关联后数据的平均值循环迭代更新测量数据中的最大、最小值,直到满足规定的精度要求,从而实现多平台多传感器数据融合.避免了一些传统算法的复杂计算过程,提高了计算速度,且能满足融合结果的精度需求.通过仿真实例,验证了该算法的可行性和有效性.%Concerning the uncertainty in multi-sensor communications and data measurement, a multi-platform centralized algorithm of mean value iteration based on the traditional meanvalue was presented. This method utilized the nearest neighbor analysis to have the measurement data correlation firstly, then utilized the meanvalue algorithm to circularly replace the maximal and minimal values in measurement data, until they reached the required precision to achieve the multi-platform data fusion. This method avoided complex computational process, increased computation speed, and also ensured precision of the results. Finally, the simulation result proves the validity of the method.