西安工程大学学报2017，Vol.31Issue(3)：402-410,9.DOI:10.13338/j.issn.1674-649x.2017.03.019
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
摘要
Abstract
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Key words
indoor positioning/environment self-adaptation/log-normal shadowing model/Kalman filter algorithm分类
信息技术与安全科学引用本文复制引用
王蕊超,邵景峰,白晓波,马创涛..基于RSSI测距改进的多区域自适应室内定位方法[J].西安工程大学学报,2017,31(3):402-410,9.基金项目
国家科技支撑计划基金资助项目(2014BAF07B01) （2014BAF07B01）
中国纺织工业联合会应用基础研究基金资助项目(J201508) （J201508）
陕西省教育厅服务地方科学研究基金资助项目(16JF009) （16JF009）
