中文摘要：

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璃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．

英文摘要：

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.