Relative location estimation in wireless sensor networks
Relative location estimation in wireless sensor networks
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DOI：
10.1109/tsp.2003.814469
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发表时间：
2003-08-01
影响因子：
5.4
通讯作者：
O'Dea, RJ
中科院分区：
文献类型：
--
作者：
Patwari, N;Hero, AO;O'Dea, RJ
无线传感器网络中的自配置问题是一个一般性的估计问题，我们研究通过Cramer-Rao界（CRB）。具体来说，我们考虑传感器位置估计时，传感器测量接收信号强度（RSS）或到达时间（TOA）之间的自己和相邻的传感器。网络中的一小部分传感器具有已知的位置，而其余的位置必须估计。我们推导出CRB和最大似然估计（MLEs）高斯和对数正态模型下的TOA和RSS测量，分别。在室内办公区域进行的广泛的TOA和RSS测量活动说明了MLE性能。最后，相对位置估计算法在无线传感器网络实验平台上实现，并部署在室内和室外环境中。测量和测试台实验表明，使用TOA时存在1 m RMS位置误差，使用RSS时存在1至2 m RMS位置误差。
Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.