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Ranging-Free UHF-RFID Robot Positioning Through Phase Measurements of Passive Tags

机译:无需阶段测量无源标签的无需UHF-RFID机器人定位

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The phase of the signals backscattered by the ultrahigh frequency radio-frequency identification (UHF-RFID) tags is generally more insensitive to multipath propagation than the received signal strength indicator (RSSI). However, signal phase measurements are inherently ambiguous and could be further affected by the unknown phase offsets added by the transponders. As a result, the localization of an agent by using only signal phase measurements looks infeasible. In this article, it is shown instead that the design of a dynamic position estimator (e.g., a Kalman filter) based only on the signal phase measurement is actually possible. To this end, the necessary conditions to ensure the theoretical local nonlinear observability are first demonstrated. However, a system that is locally observable guarantees the convergence of the localization algorithm only if the actual initial agent position is approximately known a priori. Therefore, the second part of the analysis covers the global observability, which ensures convergence starting from any initial condition in the state space. It is important to emphasize that complete observability holds only in theory. In fact, measurement uncertainty may greatly affect position estimation convergence. The validity of the analysis and the practicality of this localization approach are further confirmed by numerical simulations based on an unscented Kalman filter (UKF).
机译:通过超高频率射频识别(UHF-RFID)标签反向散射的信号的相位通常对比接收的信号强度指示器(RSSI)的多径传播更不敏感。然而,信号相位测量本质上是模糊的,并且可以进一步受转发器添加的未知相位偏移的影响。结果,仅使用信号相位测量的代理的定位看起来不可行。在本文中,示出了仅基于信号相位测量的动态位置估计器(例如,卡尔曼滤波器)的设计实际上是可能的。为此,首先证明确保理论局部非线性可观察性的必要条件。但是,当实际初始代理位置约为已知<斜体> a先验时,局部可观察到的系统仅保证本地化算法的收敛性才能保证本地化算法的融合。因此,分析的第二部分涵盖了全局可观察性,可确保从状态空间中的任何初始条件开始的会聚。重要的是要强调,完全可观察性仅在理论上持有。实际上,测量不确定性可能极大地影响位置估计会聚。通过基于Unscented Kalman滤波器(UKF)的数值模拟,进一步证实了分析和本地化方法的实用性的有效性。

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