Abstract
With the rapid development of the Internet of Things (IoT) technology, location based service in context awareness has received increasing attention. As one of the main localization technologies, UHF RFID technology has been widely used in many fields of life and industry due to its advantages. In this article, we introduce a RFID-based system RF-SML, which is a method for quickly and accurately locating static objects via the tag and mobile reader. Specifically, the method utilizes the idea of multi-granularity in order to find the high-probability region of the target position by reconstructing the reflection coefficient of the scene in the coarse-grained localization stage. Subsequently, in the fine-grained localization stage, the grid is traversed in this area to calculate the corresponding evaluation factor to determine the final position result, thereby reducing the time-consuming of localization calculation. At the same time, it uses phase calibration to remove the phase offsets that are caused by the hardware device and the antenna phase center, thereby obtaining higher localization accuracy. We conduct experiments to verify the performance of RF-SML with commercial-off-the-shelf (COTS) RFID equipment. The results show that the proposed method can efficiently achieve the centimeter-level positioning of objects.
Highlights
In the process of the information technology revolution of Industry 4.0, the development and popularization of Internet of Things (IoT) requires smart devices to identify and localize objects more accurately and effectively [1]
The results show that the localization performance of RF-oto is better than SARFID and Tagoram
It can be seen that the proposed method reduces the computational cost by up to about 98.6% when compared with other methods in the two-dimensional (2D) search space, thereby reducing the localization latency and achieving the computational time of the millisecond level
Summary
In the process of the information technology revolution of Industry 4.0, the development and popularization of IoT requires smart devices to identify and localize objects more accurately and effectively [1]. Since Robert Miesen et al [21] combined SAR with RFID technology, most of current SAR indoor localization researches have focused on the grid-based holographic imaging method It first uses priori information such as moving trajectory to generate a feature vector for each grid point via a signal propagation model. We propose RF-SML, a synthetic aperture multi-granularity method for deriving the tag position accurately with low-time-consuming on UHF-RFID system It is a localization algorithm which could solve the problem of the huge calculation amount in the grid-matching method. The key contribution of this paper is that RF-SML takes advantage of reflection coefficient model and multi-granularity method to realize fast centimeter-level localization It breaks through the uniform grid granularity limitation of target scene, which can be applied to real-time systems with moderate computational efforts.
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