CAJ | 학술논문

802.11无线局域网技术的广泛普及,给无线室内定位系统带来了良好的发展契机.提出了一种基于支持向量回归的802.11无线室内定位方法.该方法主要包括离线训练和在线定位两个阶段.离线阶段的主要工作是得到精确的位置预测模型；在线阶段的主要工作是根据移动设备的接收信号强度(received signal strength,简称RSS)进行在线定位.由于存在室内环境复杂、信道拥塞、障碍物影响和节点的通信半径有限等问题,移动设备的接收信号强度易受干扰,复杂多变.针对以上问题,离线阶段对接收信号强度信息进行统计分析,得出数据过滤规则,对训练数据集进行过滤,以此提高训练样本质量,从而提高支持向量回归预测模型的质量.在线阶段使用连续K次测量定位法获取信号强度信息,保证训练样本与在线输入信息之间的一致性,提高最终的定位精度.通过实验对该定位方法进行了综合对比分析,实验结果表明：与常用概率定位法、神经网络法相比,该方法具有更高的定位精度,同时具有对移动设备的存储容量及其计算能力要求较低的特点.
802.11무선국역망기술적엄범보급,급무선실내정위계통대래료량호적발전계궤.제출료일충기우지지향량회귀적802.11무선실내정위방법.해방법주요포괄리선훈련화재선정위량개계단.리선계단적주요공작시득도정학적위치예측모형；재선계단적주요공작시근거이동설비적접수신호강도(received signal strength,간칭RSS)진행재선정위.유우존재실내배경복잡、신도옹새、장애물영향화절점적통신반경유한등문제,이동설비적접수신호강도역수간우,복잡다변.침대이상문제,리선계단대접수신호강도신식진행통계분석,득출수거과려규칙,대훈련수거집진행과려,이차제고훈련양본질량,종이제고지지향량회귀예측모형적질량.재선계단사용련속K차측량정위법획취신호강도신식,보증훈련양본여재선수입신식지간적일치성,제고최종적정위정도.통과실험대해정위방법진행료종합대비분석,실험결과표명：여상용개솔정위법、신경망락법상비,해방법구유경고적정위정도,동시구유대이동설비적존저용량급기계산능력요구교저적특점.
The widespread of the 802.11-based wireless LAN technology brings a good opportunity for the development of the indoor positioning system based on 802.11. In this paper, a 802.11-based indoor positioning method using support vector regression (SVR) is presented. The method consists of two periods: offline training period and online location period. The accurate position prediction model is achieved in the offline training period by SVR, and the exact position is determined in the online location period according to the received signal strength (RSS) of the mobile devices. Due to the complex indoor environment, wireless channel congestion, obstructions and limitation of node communication range, the RSS is vulnerable and changeable. To address the above issues, corresponding data filtering rules obtained through statistical analysis are applied in offline training period to improve the quality of training sample, and thus improve the quality of prediction model. In the online location period,k-times continuous measurement is utilized to obtain the high quality input of the received signal strength, which guarantees the consistency with the training samples and improves the position accuracy of mobile devices. Performance evaluation and comprehensive analysis are done through intensive experiments, and the results show that the presented method has a higher positioning accuracy when compared with the probability positioning method and neutral network positioning method, and its demand for the storage capacity and computing power of the mobile devices is also low at the same time.