摘要
基于接收信号强度RSSI(received signal strength indicator)测距定位算法被广泛应用。针对WSNs(wireless sensor networks)中对定位算法要求定位精度高、功耗小的需求,提出了一种贝叶斯(Bayesian)优化RSSI和迭代最小二乘的室内定位算法。首先分析了RSSI测距模型;然后考虑室内环境对RSSI值的影响,采用贝叶斯概率模型处理RSSI的测量值,并筛选出"大概率"的RSSI值,再进行均值处理,从而获取精确的测距数据;最后利用最小二乘法估计未知节点位置,并依据Crossbow公司生产的Telos系列TelosB节点设计了测距实验,对获取的多组数据进行分析。实验结果表明,该算法具有低的平均定位误差和稳定的定位精度。
Ranging-localization algorithm based received signal strength indicator（RSSI）is widely used.An indoor optimized RSSI localization algorithm based on Bayesian probability model is proposed for the WSNs positioning algorithm which requires high precision and small power consumption demands.The models of ranging based on RSSI are analyzed.Considering the indoor environment＇s influence on the RSSI value,Bayesian probability model is applied to deal with data of RSSI.The ＂ big probability＂ RSSI values are selected and averaged to obtain precise ranging data.The position of unknown node is estimated by the least square.The ranging experiment is designed by Crossbow＇s TelosB nodes by analyzed the number of data from experiment.Simulation results show that the proposed algorithm has low average localization error and stable localization accuracy.
出处
《中国科技论文》
CAS
北大核心
2015年第20期2377-2381,共5页
China Sciencepaper
基金
国家电网科技项目(14ZA0339)
