基于无源超高频RFID的农产品包装智能定位方法

射频识别（radio frequency identification,RFID）技术为工业物联网（industrial internet of things）带来了巨大的进步,作为实现智能仓储的关键技术之一,广泛应用于库存管理和智能定位等场景,然而现有的绝对/相对定位方法易受仓储环境、包装材料、货架材质等因素影响。为了进一步提升室内定位精度,该研究提出了一种基于接收信号强度指示器（receive signal strength indicator,RSSI）和测量相位融合的无源RFID定位方法（RFID positioning based on received signal strength indicator and phase measurement, RP-RaP）。首先,使用MATLAB 软件进行仿真模拟,在已知测量相位统计学分布的前提下,采用最大似然估计法对标签进行水平定位,同时基于双天线阅读器所测得的RSSI差值对标签进行垂直定位,实现了无源超高频RFID标签的水平和垂直定位仿真。其次,以农产品包装场景为例,在仓库中搭建射频定位测试系统,通过滑轨搭载射频阅读器及天线,对货架物品上的贴附标签进行水平和垂直定位分析,最后将无源标签分别贴附于金属盒、油桶、纸箱、面粉袋和大米袋,并以未贴附标签的测量结果作为对比。试验结果表明,与传统的室内定位算法LANDMARC相比,RP-RaP定位精度明显提升,平均水平和垂直定位精度分别达到94.6%和94.3%,基于接收信号强度指示器和测量相位融合的定位方法有效提升了农产品包装定位精度。研究结果可为大型农产品仓储智能化管理与应用提供参考。

Intelligent positioning method for the packaging of agricultural products based on passive ultra high frequency(UHF) RFID

Location-based services (LBS) are gradually shifting from "outdoor-oriented" to "indoor-outdoor coexistence" in recent years, with the development of positioning technology. Radiofrequency identification (RFID) has brought tremendous progress to the Industrial Internet of Things (IoT). Radio frequency signals can be used to locate indoor objects or people, considering the intelligent identification of target objects. The key technology has also been widely used in inventory management, intelligent positioning, and warehousing, due to the miniaturization and low power consumption. However, the existing absolute/relative RFID positioning has been easily affected by the warehousing environment, packaging materials, and shelf materials, leading to low positioning accuracy. In this study, a passive RFID positioning was proposed to fusion the received signal strength indicator and phase measurement (RP-RaP). Firstly, MATLAB software was used to simulate the actual situation of the warehouse. A wireless channel model was established to simulate the phase integer ambiguity. RSSI analysis was investigated to explore the impact of path loss factor n on positioning accuracy. The values were taken from 2 to 4, in order to obtain the root mean square error parameter of positioning. The RFIF tags were deployed to simulate the given statistical distribution of the measured phase, according to the "ring" and "corridor" types. The maximum likelihood estimation was used for the horizontal positioning of the labels. The RSSI difference was measured by the tilted reader dual antenna for the vertical positioning of the labels. The horizontal and vertical positioning simulation was achieved in the passive ultra-high frequency RFID tags. Secondly, taking the packaging scenario of agricultural products as an example, a radio frequency positioning testing system was set up in the warehouse. The warehouses were mostly shelved to consider the space utilization in reality. The corridor-type label distribution was selected for experimental testing. An RF reader and antenna were installed on the slide rail. The horizontal and vertical positioning analysis was performed on the attached labels on the shelf items. The experimental results showed that the RP-RaP significantly improved the positioning accuracy, with an average horizontal and vertical positioning accuracy of 94.6% and 94.3%, respectively, compared with the traditional indoor positioning (LANDMARC). The positioning with the received signal strength indicator and measurement phase fusion effectively improved the label positioning accuracy in agricultural product packaging scenarios. Several influencing factors on positioning accuracy were discussed, including different materials attached to the label, rotation of the relative angle between the label and the antenna, the shape of the label, and the spacing between the labels. Experimental verification was conducted on the phase and RSSI data under the above conditions. The results indicated that the attachment of metal and liquid packaging to the tag was significant fluctuations for the backscattered phase and RSSI signal, in cases of severe deformation of the tag. This finding can provide a strong basis to further improve the accuracy of RFID indoor positioning.