Radio map updated method based on subscriber locations in indoor WLAN localization
Radio map updated method based on subscriber locations in indoor WLAN localization
复制标题
室内WLAN定位中基于用户位置的无线电地图更新方法
DOI：
10.1109/jsee.2015.00131
复制
发表时间：
2015-12-01
影响因子：
2.1
通讯作者：
Ma, Lin
中科院分区：
文献类型：
--
作者：
Xia, Ying;Zhang, Zhongzhao;Ma, Lin
随着无线局域网（WLAN）技术的迅猛发展，室内定位系统的一个重要目标是在降低在线校准工作量以克服信号时变问题的同时，提高定位精度。本文提出了一种新颖的指纹定位算法，即基于隐马尔可夫模型（HMM）的自适应无线电地图更新方法。研究表明，通过利用一系列易于获取的用户轨迹，该算法能够借助这些数据更新已标注的校准数据，从而进一步提高位置估计精度。该算法融合了机器学习、信息增益理论以及指纹识别技术。通过在真实的室内WLAN环境中收集数据并对算法进行测试，实验结果表明，与广泛使用的K近邻算法相比，所提算法在大幅减少校准工作量的同时，还能提高定位精度。
With the rapid development of wireless local area network(WLAN) technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model(HMM), is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm,the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.