作　　者： ; ; ; ;

机构地区： 北京邮电大学软件学院

出　　处： 《计算机研究与发展》 2012年第2期243-252,共10页

摘　　要： 目前基于WiFi射频指纹定位技术有望成为大规模城区室内外全空间定位的首选.针对RSS信号时变特性严重影响WiFi定位精度和鲁棒性的问题,提出了一种基于公共信标集的高精度射频指纹定位算法.该算法把目标定位看成贝叶斯估计问题,通过采用高斯混合模型更加准确地表征复杂训练指纹的信号特征,以及使用基于Markov链的状态转移模型和基于后验概率的自适应网格集选择机制,充分利用目标的历史状态信息和环境布局信息,不仅减少了定位搜索网格空间,而且还抑制了移动过程中不可能发生的位置跳变,提高了定位精度和鲁棒性.实验结果表明,所提定位算法以90%的概率可获得3m以内的定位误差,其定位性能明显优于传统单一高斯模型. WiFi fingerprinting localization is currently the most promising method to building largescale urban localization systems for both indoor and outdoor environments. To reduce the negative effect caused by the fluctuation of the received signal strength （RSS） and improve the positioning accuracy and robustness, an accurate radio fingerprinting localization algorithm based on common beacons is presented in this paper. It formulates the object localization as a Bayesian estimation problem. By employing Gaussian mixture model to accurately represent the complex training fingerprint pattern, and using a Markov-ehain state transition model and an adaptive grid collection selection method based on the posterior probability to exploit the object＇s past states and environment layout information, the algorithm not only can reduce the size of grid search space, but also can restrict the impossible position jump during the moving process and improves the localization accuracy and robustness. Practical experimental results show that the proposed positioning method can achieve localization error within 3 m with 90 percent probability, which works much better than the single Gaussian localization model.

关 键 词： 室内定位 射频指纹 贝叶斯估计 高斯混合模型 链

领　　域： [自动化与计算机技术] [自动化与计算机技术] [自动化与计算机技术] [自动化与计算机技术]