On distance estimation based on radio propagation models and outlier detection for indoor localization in Wireless Geosensor Networks
On distance estimation based on radio propagation models and outlier detection for indoor localization in Wireless Geosensor Networks
复制标题
DOI：
10.1109/ipin.2010.5647102
复制
发表时间：
2010-11
期刊：
影响因子：
--
通讯作者：
A. Born;Mario Schwiede;R. Bill
中科院分区：
文献类型：
--
作者：
A. Born;Mario Schwiede;R. Bill
无线地理传感器网络（GSN）中精确位置的确定需要使用例如距离测量。这些由接收信号强度（RSS）测量得出的距离观测本身是不准确的。此外，一般来说，使用RSS的距离观测没有考虑障碍物。在这篇正在进行的论文中，我们提出了一种新的方法和首次模拟，以纠正室内场景中受障碍物影响的错误RSS测量。该技术与已知的“定位异常校正”（ACL）算法相结合，其中传感器测量用于改进确定的传感器节点位置，并检测和消除异常值。因此，本文将提出一种新的“扩展定位异常校正”算法（extended Anomaly Correction in Localization, eACL）。
The determination of a precise position in Wireless Geosensor Networks (GSN) requires the use of e.g. distance measurements. These distance observations derived by Received Signal Strength (RSS) measurements are inherently inaccurate. Furthermore, in general, the distance observations using RSS do not take obstacles into account. In this work in progress paper we present a new approach and first simulations to correct erroneous RSS measurements affected by obstacles in indoor scenarios. This technique is combined with the known “Anomaly Correction in Localization” (ACL) algorithm where sensor measurements are used to improve the determined sensor node positions and to detect and to eliminate outliers. Therefore, the new “extended Anomaly Correction in Localization” algorithm (eACL) will be formulated.