# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 2 Related Research  
RFID localization can be divided into relative position localization, two-dimensional (2D) plane localization, and three-dimensional (3D) space localization. In this section, we present a review of related research [17].  
Relative Localization: Shangguan et al. [18] proposed the first study of relative localization, called 'Spatial-Temporal Phase Profiling-Based Method for Relative RFID Tag Localization (STPP)', based on the Otrack [19]. The ordering accuracy for misplaced books is about 84% and 95% for baggage handling. Wang et al. [20] propose a relative localization approach, called 'Human Movement based Relative Localization (HMRL)', which utilizes signal changes that are caused by arbitrary movement of human beings around tags. The system is efficient and convenient to deploy and the accuracy is 90.24%-93.11%. Based on research regarding STPP and HMRL, we propose PRDL: Relative Localization Method of RFID Tags via Phase and RSSI Based on Deep Learning [21]. By using deep learning, the variation characteristics of the RFID phase and RSSI are extracted with limited data accuracy conditions. The accuracy is as high as 97.3% in the case of high-density alignment tags at 1 cm.  
2D Localization: The mature localization algorithms in RFID 2D localization mainly include Landmarc, Bwire, Nvire, and Vire [9; 10], and they laid the foundation for RFID localization. Meanwhile, localization methods and algorithms that are based on Angle of Arrival (AOA), Time Different of Arrival (TDOA), Time of Arrival (TOA), and phase [11; 22; 23; 24; 25; 26] made significant contribution to the improvement of localization accuracy. Xu et al. [12] improved the Landmarc with Bayesian probability and K-Nearest Neighbor, and the localization accuracy is about 15 cm. Fu et al. [27] proposed a scheme for the localization of a moving object based on phase and laserclustering, and the localization accuracy is about 25 cm; Vighnesh Gharat et al. [28] present an indoor localization system that is based on LF RFID that is implemented using off-the-shelf components, which achieves a mean localization error of 1.53 m with a standard deviation of 0.91 m for 352 position estimations, while keeping the localization error below 2.82 m for 90% of the cases. Yang et al. [23] presented Tagoram: Real-Time Tracking of Mobile RFID Tags to High Precision Using COTS Devices, which improves the localization accuracy to the millimeter level.  
3D Localization: The scheme that is proposed by Lanxin Qiu et al. [29] leverages the spatial domain phase difference to estimate the height of objects that are inspired by the phase-based Interferometric Synthetic Aperture Radar (InSAR) height determination theory. Their experiments demonstrate a spatial median error of 0.24 m. Almaaitah Abdallah et al.[13] introduce two novel methods for 3D localization one is called Adaptive Power Multilateration (APM), which uses four RFID readers, with the localization error of 0.32 m\({}^{3}\). The other is Adaptive Power with Antenna Array (APAA), a single RFID reader that is equipped with horizontal and vertical smart antennas, with the localization error of 0.48 m\({}^{3}\). Jullawadee Maneesilp et al. [14] avoid the need of distance estimation according to received wireless signal strength or phase difference and achieve the localization error of 0.07 ft. Duan et al. [30] put forward an advanced method, called Tagspin, to locate the RFID reader using COTS tags, and the average accuracy is about 7.3 cm; Elma Zanaj et al. [15] focus on RFID tags localization by only using mobile readers without the inclusion of static readers, which can avoid the pre-deploying of static readers. Ferdews Tlili et al. [31] propose an approach that is based on MDS for 3D localization using active RFID tags in indoor environments.