中文摘要：

针对室内无线局域网环境中无线信号不稳定,以及传统支持向量回归定位算法在构建位置坐标与信号强度时的单输出导致位置坐标信息之间的关联性降低的问题,提出一种基于改进支持向量回归的室内定位方法。该算法首先对采集到的接收信号强度（RSS）指纹进行对数处理使其更符合正态分布,然后采用高斯滤波过滤掉小概率的指纹值之后构建指纹数据库;其次,为了降低单独构建x与y坐标模型的误差,提高二维位置信息与RSS之间的关联性,在训练阶段增加训练一个校正坐标z=x·y;最后,根据加权反K近邻的方法得到最优的位置坐标。实验结果表明,提出的算法可以减少室内复杂环境带来的噪声干扰,与传统的支持向量回归定位算法相比有更高的定位精度。

英文摘要：

The wireless signal of wireless local area network is unstable in the indoor environment, and the traditional support vector regression （SVR） based positioning method may lead to the reduction of the correlation between the position coordinates and signal strength. Thus, this paper proposes an improved support vector regression （ISVR） based indoor positioning method. Firstly, the logarithmic processing is conducted on the received signal strength （RSS） to make it more consistent with the normal distribution, and then the Gaussian filter is used to filter the small probability of fingerprints before building the fingerprint database. Secondly, in order to reduce the error of constructing X and Y coordinate model separately, a calibration coordinate z = x·y is trained at the training stage, which can improve the correlation relationship between RSS and X-Y position information. Finally, the optimal position coordinates are obtained by weighted inverse K- nearest neighbor （WIKNN） method. The experimental results show that the proposed algorithm can reduce the noise caused by the complicated environment in the room, and has higher positioning accuracy than the traditional support vector regression algorithm.