基于多边约束的数据融合网络定位

无线传感网络定位方法近几年来受到广泛的关注。分布式算法是传感网络定位的一个主要要求,它不仅能减小计算和通信负载,同时对络的非连通性有很好的鲁棒性,对测量噪声克服也有一个很好的实现方案。虽然多种测量技术都能实现位置估计,但都存在某些不足。数据融合是一个很好的解决方案。提出了一个数据融合的结构和模糊优化数据融合算法。对每个节点的TOA和RSS数据进行融合。仿真表明位置估计的精度能够得到显著的改进,同时算法的复杂度也得到了降低。

Multilateral Constraint-based Data Fusionfor Sensor Network Localization

Wireless location methods in sensor network have received significant attention in recent years. An important requirement for location in WSN is a distributed approach that minimizes computation,and especially communication,overhead,and is robust to survive disconnection,measurement noise and also has a reasonable implementation cost. Various technique offers solutions to the position estimation problem However,each method has limitations that prevent its adoption as a universal solution. Data fusion is a perfect solution. A data fusion architecture and a Fuzzy Optimization Data Fusion (FODF) was explored,which fused distance measurement data from TOA and RSS. Simulation results show that the overall accuracy of location estimation in wireless location systems can be significantly improved when the fuzzy optimization data fusion methods are used.