Nielsen John, Nielsen Christopher
Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N-1N4, Canada.
Sensors (Basel). 2016 Sep 24;16(10):1570. doi: 10.3390/s16101570.
Currently there is almost ubiquitous availability of wireless signaling for data communications within commercial building complexes resulting in receiver signal strength (RSS) observables that are typically sufficient for generating viable location estimates of mobile wireless devices. However, while RSS observables are generally plentiful, achieving an accurate estimation of location is difficult due to several factors affecting the electromagnetic coupling between the mobile antenna and the building access points that are not modeled and hence contribute to the overall estimation uncertainty. Such uncertainty is typically mitigated with a moderate redundancy of RSS sensor observations in combination with other constraints imposed on the mobile trajectory. In this paper, the Fisher Information (FI) of a set of RSS sensor observations in the context of variables related to the mobile location is developed. This provides a practical method of determining the potential location accuracy for the given set of wireless signals available. Furthermore, the information value of individual RSS measurements can be quantified and the RSS observables weighted accordingly in estimation combining algorithms. The practical utility of using FI in this context was demonstrated experimentally with an extensive set of RSS measurements recorded in an office complex. The resulting deviation of the mobile location estimation based on application of weighted likelihood processing to the experimental RSS data was shown to agree closely with the Cramer Rao bound determined from the FI analysis.
目前,在商业建筑综合体内,用于数据通信的无线信号几乎无处不在,这使得接收信号强度(RSS)观测值通常足以生成移动无线设备的可行位置估计。然而,尽管RSS观测值通常很丰富,但由于影响移动天线与建筑物接入点之间电磁耦合的几个因素未被建模,从而导致整体估计存在不确定性,因此实现准确的位置估计很困难。这种不确定性通常通过RSS传感器观测的适度冗余以及对移动轨迹施加的其他约束来减轻。在本文中,针对与移动位置相关的变量,开发了一组RSS传感器观测的费希尔信息(FI)。这提供了一种确定给定可用无线信号集潜在位置精度的实用方法。此外,单个RSS测量的信息值可以量化,并且在估计组合算法中相应地对RSS观测值进行加权。通过在办公综合体内记录的大量RSS测量数据,通过实验证明了在此背景下使用FI的实际效用。基于对实验RSS数据应用加权似然处理得出的移动位置估计偏差,与从FI分析确定的克拉美罗界密切吻合。