基于RSSI测距改进的多区域自适应室内定位方法  

An improved multi-regional self-adaptive indoor positioning method based on RSSI

作　　者：王蕊超 邵景峰[1] 白晓波[1] 马创涛 WANG Ruichao SHAO Jingfeng BAI Xiaobo MA Chuangtao(School of Management, Xi＇an Polytechnic University, Xi＇an 710048, China)

机构地区：[1]西安工程大学管理学院,陕西西安710048

出　　处：《西安工程大学学报》2017年第3期402-410,共9页Journal of Xi’an Polytechnic University

基　　金：国家科技支撑计划基金资助项目(2014BAF07B01);中国纺织工业联合会应用基础研究基金资助项目(J201508);陕西省教育厅服务地方科学研究基金资助项目(16JF009)

摘　　要：借助接收信号强度指示(RSSI)测距的室内定位算法和卡尔曼滤波算法,将目标区域按照室内结构特点划分为多个子区域环境,在每个区域构建基于环境参数库的对数正态阴影扩展模型,同时设计与该扩展模型相匹配的硬件系统结构.利用卡尔曼滤波算法对该模型中所需变量参考距离处接收到的RSSI值进行过滤,并借助极大似然估计进行目标定位,在此基础上提出了一种基于RSSI测距改进的多区域环境自适应室内定位方法.验证结果表明,改进的多区域自适应定位方法与现有的均值法、多区域均值法相比,其室内定位误差分别降低了22.41%和15.1%,且可靠度达到71.43%,充分说明改进的多区域自适应定位方法可有效解决室内定位方法过度依赖外部环境导致定位精准度不高的问题.To solve the problem that the positioning accuracy is low because of the existed method being over-reliance on the external environment, an improved multi-regional environment adaptive indoor positioning method was proposed.The method was based on the indoor location algorithm and the Kalman filter algorithm with the received signal strength indicator（RSSI）.The target area is divided into multiple sub-regional areas according to the indoor structure characteristics, the log-normal shadow expansion model is constructed based on the environmental parameter database, and the hardware system structure which matches the extended model is designed. And then, the RSSI value received from reference distance was filtered by using Kalman filter algorithm, and target positioning was realized via maximum likelihood estimation. As verified by experiment, the results show that the adaptive method has better positioning accuracy than the existed mean method, and multi-regional mean method, and its positioning error is reduced by 22.41% and 15.1%, respectively. The reliability of the adaptive method is up to 71.43%. All these result data have explained that the method is effective to solve the problem of low positioning accuracy for the indoor positioning.

关 键 词：室内定位 环境自适应 对数正态阴影模型 卡尔曼滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]