基于RSSI的KNN—PIT室内自适应定位算法  

Indoor adaptive KNN-PIT positioning algorithm based on RSSI

作　　者：唐瑞[1] 孙冰洁[1] 周礼争 余敏[2] 

机构地区：[1]江西师范大学软件学院,江西南昌330022 [2]江西师范大学计算机信息工程学院,江西南昌330022

出　　处：《传感器与微系统》2015年第7期128-131,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(41374039);中国-波兰国际科技合作项目(35-14)

摘　　要：针对基于接收信号强度指示(RSSI)的K最近邻(KNN)算法在室内定位精度较低的问题,提出一种改进的KNN—三角形内点(KNN—PIT)室内定位算法。根据室内空间结构特征,建立具有类标号的位置指纹库。引入虚拟参考点,利用PIT原理进一步约束目标点的定位区域,自适应地使用定位算法进行定位。综合运用高斯滤波、均值滤波技术,降低离线和在线阶段的信号随机误差。结果表明:改进后的KNN—PIT定位算法可以更好地估计用户的实际位置,降低定位误差,定位精度提高12.5%。Aiming at problem of KNN algorithm low precision of indoor positioning based on RSSI,an improved KNN—PIT indoor positioning algorithm is put forward. According to structure feature of indoor space,establish location fingerprint database which has class labels. Introduce virtual reference points,use theory of PIT to further constrain localization area of target point,use positioning algorithm adaptively to carry out positioning.Comprehensively use Gauss filtering and mean filtering to reduce random errors of signal in online and offline stages. Results show that the improved KNN—PIT algorithm can better estimate the user＇s actual location,and decrease significantly localization errors,positioning precision is improved by 12. 5 %.

关 键 词：室内定位 K最近邻 三角形内点 虚拟参考点 自适应 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]