Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks
Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks
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DOI：
10.1109/tmc.2012.143
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发表时间：
2013-09-01
影响因子：
7.9
通讯作者：
Korenberg, Michael J.
中科院分区：
文献类型：
--
作者：
Atia, Mohamed M.;Noureldin, Aboelmagd;Korenberg, Michael J.
上下文感知和基于位置的服务在移动计算环境中具有重要意义。虽然指纹识别在无线局域网(WLAN)中提供了准确的室内定位，但离线现场测量的困难和动态环境的变化阻碍了它的实际实施和商业采用。本文介绍了一种新颖的基于客户/服务器的系统，该系统无需额外的网络硬件或有关区域的先验知识，也无需耗时的离线测量，即可动态估计并连续校准室内定位的精细无线电地图。引入了一种改进的贝叶斯回归算法来估计所有位置上的后验信号强度概率分布，该算法基于来自无线局域网接入点(AP)的在线观测，假设高斯先验集中在对数通损均值之上。为了不断适应动态变化，贝叶斯核参数根据最近的AP观测结果不断更新和遗传优化。通过一种快速的特征约简算法进一步优化无线电地图，以选择信息量最大的AP。此外，该系统还提供可靠的完整性监控(精度测量)。在IEEE 802.11网络上的两个不同的实验表明，动态无线电地图提供了2-3M的精度，这与最新的离线无线电地图的结果相当。结果还表明，估计精度度量与实际定位精度是一致的。
Context-awareness and Location-Based-Services are of great importance in mobile computing environments. Although fingerprinting provides accurate indoor positioning in Wireless Local Area Networks (WLAN), difficulty of offline site surveys and the dynamic environment changes prevent it from being practically implemented and commercially adopted. This paper introduces a novel client/server-based system that dynamically estimates and continuously calibrates a fine radio map for indoor positioning without extra network hardware or prior knowledge about the area and without time-consuming offline surveys. A modified Bayesian regression algorithm is introduced to estimate a posterior signal strength probability distribution over all locations based on online observations from WLAN access points (AP) assuming Gaussian prior centered over a logarithmic pass loss mean. To continuously adapt to dynamic changes, Bayesian kernels parameters are continuously updated and optimized genetically based on recent APs observations. The radio map is further optimized by a fast features reduction algorithm to select the most informative APs. Additionally, the system provides reliable integrity monitor (accuracy measure). Two different experiments on IEEE 802.11 networks show that the dynamic radio map provides 2-3m accuracy, which is comparable to results of an up-to-date offline radio map. Also results show the consistency of estimated accuracy measure with actual positioning accuracy.