Passive multiple target indoor localization based on joint interference cancellation in RFID system

Radio frequency identification (RFID) provides a simple and effective solution to the passive indoor localization. The conventional wisdom about RFID localization is utilizing reference tags. It performs well in tag or single passive target localization. However, in the passive multiple target scenario, reference tag based localization suffers from some limitations, including the array aperture, mutual coupling of reference tags, and coherent superimposition of target signals. These problems are harmless and ignored in tag or single passive target localization, but degrade the performance severely in passive multiple target scenario. Therefore, in this letter, the authors propose a joint interference cancellation method to mitigate the effect of these limitations. Uniform circle array (UCA) of reference tags were utilized to reduce the limitation of array aperture. A carefully designed relative position of adjacent reference tags and a modified channel model were combined to reduce the mutual coupling. A virtual distributed radar was utilized to reduce the false positive and false negative estimations. The system was evaluated in real indoor environment using noodles and colas as targets. The accuracy of target number estimation is 97.5%, the spatial resolution is about 50cm, and the median error of 2-D multi-target localization is about 5.5cm.

射频识别技术（RFID）为被动式室内定位提供了一种简便而高效的方法。关于RFID定位的传统观点是利用参考标签。它在标签或单个被动目标定位方面表现良好。然而，在被动多目标场景中，基于参考标签的定位受到一些限制，包括阵列孔径、参考标签间的互耦以及目标信号的相干叠加。这些问题在标签或单个被动目标定位中可能被视为无害且被忽略，但在被动多目标场景中则会严重降低性能。因此，在本信函中，作者提出了一种联合干扰消除方法来减轻这些限制的影响。利用均匀圆阵（UCA）的参考标签以降低阵列孔径的限制。通过精心设计的相邻参考标签的相对位置和改进的信道模型相结合来减少互耦。此外，运用虚拟分布式雷达以降低误报和漏报的估计。该系统在真实室内环境中使用面条和可乐作为目标进行了评估。目标数量估计的准确率为97.5%，空间分辨率约为50厘米，二维多目标定位的中值误差约为5.5厘米。