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Geometrical constrained least squares estimation in wireless location systems

机译:无线定位系统中的几何约束最小二乘估计

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Received signal strength indication (RSSI) is used in most cases of the localization system for its advantages of simplicity, requiring no additional hardware and low-cost. However, due to the irregular fluctuation, there are certain measured RSSIs which do not follow the empirical path loss model, while estimating the location of the blind node. So it's quite difficult to achieve high location accuracy. In this paper, according to the geometrical constraint between each distance, we propose a geometrical constrained least square estimation (GC-LSE) algorithm. This algorithm can both deal with the large errors that are mainly caused by the environmental change, and small errors that come from the devices' difference and operation mistakes. By reducing these errors we can greatly improve the location performance. With the numerical results from practical experiments, GC-LSE algorithm is proved to have a substantial improvement on location accuracy.
机译:定位系统的大多数情况下都使用接收信号强度指示(RSSI),因为它具有简单性,无需额外硬件和低成本的优点。但是,由于不规则波动,在估计盲节点的位置时,某些测量的RSSI不遵循经验路径损耗模型。因此,很难获得很高的定位精度。在本文中,根据每个距离之间的几何约束,我们提出了一种几何约束最小二乘估计(GC-LSE)算法。该算法既可以处理主要由环境变化引起的大错误,又可以处理由于设备差异和操作错误而引起的小错误。通过减少这些错误,我们可以大大提高定位性能。结合实际实验的数值结果,证明了GC-LSE算法在定位精度上有实质性的提高。

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