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A Tractable Analysis of the Improvement in Unique Localizability Through Collaboration

机译:通过协作改善独特定位能力的可行分析

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In this paper, we mathematically characterize the improvement in device localizability achieved by allowing collaboration among devices. Depending on the detection sensitivity of the receivers in the devices, it is not unusual for a device to be localized to lack a sufficient number of detectable positioning signals from localized devices to determine its location without ambiguity (i.e., to be uniquely localizable). This occurrence is well-known to be a limiting factor in localization performance, especially in communications systems. In cellular positioning, e.g., cellular network designers call this the hearability problem. We study the conditions required for unique localizability and use tools from stochastic geometry to derive accurate analytic expressions for the probabilities of meeting these conditions in the noncollaborative and collaborative cases. We consider the scenario without shadowing, the scenario with shadowing and universal frequency reuse, and, finally, the shadowing scenario with random frequency reuse. The results from the latter scenario, which apply particularly to cellular networks, reveal that collaboration between two devices separated by only a short distance yields drastic improvements in both devices’ abilities to uniquely determine their positions. The results from this analysis are very promising and motivate delving further into techniques which enhance cellular positioning with small-scale collaborative ranging observations among nearby devices.
机译:在本文中,我们在数学上描述了通过允许设备之间进行协作而实现的设备可定位性改进。取决于设备中接收器的检测灵敏度,定位设备缺少来自定位设备的足够数量的可检测到的定位信号来确定其位置而没有歧义(即,可唯一地定位)是很正常的。众所周知,这种情况是本地化性能的限制因素,尤其是在通信系统中。在蜂窝定位中,例如,蜂窝网络设计者将此称为可听性问题。我们研究了独特的可本地化性所需的条件,并使用随机几何工具来为非协作和协作情况下满足这些条件的概率得出准确的分析表达式。我们考虑不带阴影的场景,带阴影和通用频率重用的场景,最后考虑带随机频率重用的阴影场景。后一种情况的结果(特别适用于蜂窝网络)表明,仅相隔很短距离的两个设备之间的协作极大地提高了两个设备唯一确定其位置的能力。该分析的结果非常有前途,并且有动机进一步探索通过附近设备之间的小规模协作测距观测来增强蜂窝定位的技术。

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