摘要
随着位置感知技术的发展,室内定位的需求变得日益强烈。目前室内定位技术在线性环境下的应用较少,本文针对线性不稳定环境下定位耗时的问题,提出了短时路径记忆辅助的加权K最近邻算法SPM-WKNN(weighted Knearest neighbor of short time path memory)来提高定位效率。针对无线访问接入点(AP-access point)变化大的问题,本文提出了基于无线AP相关系数的接入点分簇机制,减小无线AP变化所带来的影响,提高定位精度。通过理论分析及仿真表明本文提出的SPM-WKNN算法和接入点分簇机制相对于原有算法在线性不稳定环境下可以有效缩短定位时间,提高定位精度。
With the development of location-aware technology,the requirement of indoor localization becomes stronger.Indoor localization technology is rarely used under the linear environment so far.The SPM-WKNN algorithm（weighted K nearest neighbor of short time path memory）is proposed to improve the efficiency of positioning in linear unstable environment.And in order to solve the problem of large variation of the wireless AP（access point）,a wireless AP clustering mechanism is proposed which based on wireless AP correlation coefficient.The results of theoretical analysis and simulation show that the SPM-WKNN algorithm aroused in this article effectively reduced the positioning time and improved the positioning accuracy in the linear unstable environment.
出处
《电子测量技术》
2015年第12期121-124,共4页
Electronic Measurement Technology
