基于最大熵模糊概率数据关联的室内定位算法

Indoor location algorithm based on maximum entropy fuzzy probability data association

无线传感器网络(wireless sensor network,WSN)由多个微传感器节点组成,定位技术是无线传感器网络技术的重要应用之一。目前,许多定位算法在视距(line of sight,LOS)环境下定位精度较高,但在非视距(non line of sight,NLOS)环境下的定位精度较差。为了解决这一问题,提出一种改进的基于到达时间的最大熵模糊概率数据关联算法。采用分组的思想将N个测量值分为L组,每组通过交互式多模型(interactive multi model,IMM)算法获得相应的移动节点位置估计、模型概率和协方差矩阵。然后将得到的L个位置估计,通过验证门进行非视距检测,丢弃被非视距误差污染的位置估计,利用相应的关联概率对正确的位置估计进行加权得到最终的位置估计。仿真和实验结果表明,与现有方法相比,该算法可以减轻非视距误差的影响,实现更高的定位精度。

Wireless sensor network is composed of multiple micro-sensor nodes,and positioning technology is one of the important applications of WSN.At present,many localization algorithms have high localization accuracy in line of sight(LOS)environment,but poor localization accuracy in non-line-of-sight(NLOS)environment.An improved maximum entropy fuzzy probability data association algorithm based on arrival time was proposed.The grouping idea was utilized to divide N measure-ment values into L groups,and each group obtained the corresponding mobile node position estimation,model probability and covariance matrix through the interactive multi model(IMM)algorithm.Afterwards,the obtained L position estimation was subjected to non-line-of-sight detection through a validation gate.The position estimation contaminated by non line of sight errors was discarded,and the corresponding correlation probabilities was used to weight the correct position estimates to obtain the final position estimation.Simulation and experimental results show that the proposed algorithm can reduce the influence of non line of sight errors and achieve higher positioning accuracy than the existing methods.