performance in some feasible sensing conditions,our spin- In this paper,we make three main contributions as follows. ning antenna-based solution seeks to sufficiently suppress the 1)To the best of our knowledge,we are the first to thoroughly ambient signal interferences and extracts the most distinctive investigate the characteristics of the spinning linearly polarized features,by actively spinning the antenna to create the optimal antenna in the RFID system,with empirical studies and sensing condition.We build a model to investigate the received building models,which further facilitate the motion tracking signal strength indicator(RSSI)and phase variation along with in the 3D space.2)We propose to leverage the tag array the spinning of the antenna.Besides,we further extend the with different tag orientations for the 3D motion tracking, model of a single tag to the tag array,which investigates the e and build corresponding models to derive the translation and relationship between the RF-signal features and the posture of the rotation based on the RSSI and the phase extracted from the tag array,including the position and the orientation.Based the spinning linearly polarized antenna.3)We implement a on the above model,we design corresponding solutions to prototype system of a spinning antenna with the COTS RFID extract the distinctive RSSI and phase values from the RF- and evaluate its performance in the real environment.The signal variation.Our solution tracks the translation of the experiments show that our system can achieve an average tag array according to the extracted phase features,and the accuracy of 13.6cm in the translation tracking,and an average rotation of the tag array according to the extracted RSSI accuracy of 8.3 in the rotation tracking in the 3D space. variation.In this way,we are able to accurately track the II.RELATED WORK motion of tagged object in the 3D space.Fig.1 gives a Computer Vision-based Approach.Based on the accurate illustration of our spinning antenna-based solution by attaching captured images and videos,the CV-based approaches are the tag array onto the tennis racket. widely used to track the motion or recognize the gesture There are two key challenges to address in this paper.The of either objects or human subjects [3,5].However,these first challenge is to accurately estimate the 3D motion of the approaches are easily affected by the poor light condition or tag array,including the translation and the rotation,which has the object occlusion in the non-line-of-sight (NLOS)condition. six degrees of freedom.Since the change of any degree of In contrast,we leverage the RFID technology,which uses the freedom usually leads to the variation of all signal features backscatter communication to read the passive RFID tags,so including the RSSI and phase,it is conventionally impractical that our system has no requirements of the light condition. to efficiently figure out the exact motion state from the com- Sensor-based Approach.Built-in sensors in wearable de- plex state space.To address this challenge,we spin the linearly vices,e.g.,the accelerometer and the gyroscope,can be uti- polarized antenna to continuously interrogate the tag array for lized to reconstruct the gesture trace [12,14.16].For example, the motion tracking.By leveraging the matching/mismatching ArmTrack [10]proposes to track the posture of the entire arm property of the linearly polarized antenna,i.e.,in comparison solely relying on the smartwatch.However,the motion sensors to the circularly polarized antenna,the phase variation around in wearable devices have limited battery life and high cost,and the matching direction is more stable,and the RSSI variation the tracking accuracy is relatively low due to the noise of the in the mismatching direction is more distinctive,we are able measurement data.Some specific sensors with high accuracy to find more distinctive features to estimate the orientation and sensing capability are too heavy to provide comfortable user position.Moreover,by actively spinning the antenna,we can experience.In contrast,we attach a paper-like passive tag array effectively create the optimal sensing conditions and extract onto the target to track the 3D motion,which is battery-free, the distinctive signal features including the RSSI and the low-cost,light-weight and portable. phase,to perform the accurate tracking of the 3D motion. RFID-based Approach.Recently,several studies have pro- The second challenge is to tackle the variation of signal posed to utilize the RFID technique to track the motion of features when spinning the antenna,and use these features tagged objects [1,6,9,11,15,17].Wang et al.[11]track to derive six degrees of freedom for the tag array,since the moving tagged finger in a 2D plane with multiple fixed the relationship between the signal feature variations and the antennas.Shangguan et al.[9]track the 2D moving trace of 3D motion with the spinning linearly polarized antenna has the tagged object to obtain user feedbacks with only one fixed never been sufficiently investigated before.To address this antenna.These solutions focus on tracking the moving trace challenge,we conduct empirical studies and learn that,during in the 2D space,but they are not suitable for the orientation the spinning process,the RSSI is reduced to the minimum tracking in the 3D space.Tagyro [13]tracks the 3D orientation when the linear polarization of the antenna is mismatching the of a tagged objects based on the phase differences of the tags tag orientation,and the phase keeps stable around the matching via multiple antennas.Liu et al.[7]leverage multiple spinning direction of the antenna.Furthermore,we build a model to linearly polarized antenna to estimate the 3D orientation of one depict the relationship between the signal feature variations tag in a specified 2D plane.However,these methods only can and the matching/mismatching direction of the antenna-tag tracks the 3D orientation and are unable to track the absolute pair.We further extend the model to the tag array.Based on translation of the object in the 3D space simultaneously. this model,according to the distinctive RSSI variation.we use Different from the prior work,we focus on tracking the rigid the mismatching direction of each antenna-tag pair to estimate motion of the tag array in the 3D space via only one spinning the rotation of the tag array,and use the stable phase features linear polarized antenna,including both the translation and the to estimate the translation of the tag array rotation.which has remained unresolved so far. 2performance in some feasible sensing conditions, our spin￾ning antenna-based solution seeks to sufficiently suppress the ambient signal interferences and extracts the most distinctive features, by actively spinning the antenna to create the optimal sensing condition. We build a model to investigate the received signal strength indicator (RSSI) and phase variation along with the spinning of the antenna. Besides, we further extend the model of a single tag to the tag array, which investigates the relationship between the RF-signal features and the posture of the tag array, including the position and the orientation. Based on the above model, we design corresponding solutions to extract the distinctive RSSI and phase values from the RF￾signal variation. Our solution tracks the translation of the tag array according to the extracted phase features, and the rotation of the tag array according to the extracted RSSI variation. In this way, we are able to accurately track the motion of tagged object in the 3D space. Fig.1 gives a illustration of our spinning antenna-based solution by attaching the tag array onto the tennis racket. There are two key challenges to address in this paper. The first challenge is to accurately estimate the 3D motion of the tag array, including the translation and the rotation, which has six degrees of freedom. Since the change of any degree of freedom usually leads to the variation of all signal features including the RSSI and phase, it is conventionally impractical to efficiently figure out the exact motion state from the com￾plex state space. To address this challenge, we spin the linearly polarized antenna to continuously interrogate the tag array for the motion tracking. By leveraging the matching/mismatching property of the linearly polarized antenna, i.e., in comparison to the circularly polarized antenna, the phase variation around the matching direction is more stable, and the RSSI variation in the mismatching direction is more distinctive, we are able to find more distinctive features to estimate the orientation and position. Moreover, by actively spinning the antenna, we can effectively create the optimal sensing conditions and extract the distinctive signal features including the RSSI and the phase, to perform the accurate tracking of the 3D motion. The second challenge is to tackle the variation of signal features when spinning the antenna, and use these features to derive six degrees of freedom for the tag array, since the relationship between the signal feature variations and the 3D motion with the spinning linearly polarized antenna has never been sufficiently investigated before. To address this challenge, we conduct empirical studies and learn that, during the spinning process, the RSSI is reduced to the minimum when the linear polarization of the antenna is mismatching the tag orientation, and the phase keeps stable around the matching direction of the antenna. Furthermore, we build a model to depict the relationship between the signal feature variations and the matching/mismatching direction of the antenna-tag pair. We further extend the model to the tag array. Based on this model, according to the distinctive RSSI variation, we use the mismatching direction of each antenna-tag pair to estimate the rotation of the tag array, and use the stable phase features to estimate the translation of the tag array. In this paper, we make three main contributions as follows. 1) To the best of our knowledge, we are the first to thoroughly investigate the characteristics of the spinning linearly polarized antenna in the RFID system, with empirical studies and building models, which further facilitate the motion tracking in the 3D space. 2) We propose to leverage the tag array with different tag orientations for the 3D motion tracking, and build corresponding models to derive the translation and the rotation based on the RSSI and the phase extracted from the spinning linearly polarized antenna. 3) We implement a prototype system of a spinning antenna with the COTS RFID and evaluate its performance in the real environment. The experiments show that our system can achieve an average accuracy of 13.6cm in the translation tracking, and an average accuracy of 8.3 ◦ in the rotation tracking in the 3D space. II. RELATED WORK Computer Vision-based Approach. Based on the accurate captured images and videos, the CV-based approaches are widely used to track the motion or recognize the gesture of either objects or human subjects [3, 5]. However, these approaches are easily affected by the poor light condition or the object occlusion in the non-line-of-sight (NLOS) condition. In contrast, we leverage the RFID technology, which uses the backscatter communication to read the passive RFID tags, so that our system has no requirements of the light condition. Sensor-based Approach. Built-in sensors in wearable de￾vices, e.g., the accelerometer and the gyroscope, can be uti￾lized to reconstruct the gesture trace [12, 14, 16]. For example, ArmTrack [10] proposes to track the posture of the entire arm solely relying on the smartwatch. However, the motion sensors in wearable devices have limited battery life and high cost, and the tracking accuracy is relatively low due to the noise of the measurement data. Some specific sensors with high accuracy sensing capability are too heavy to provide comfortable user experience. In contrast, we attach a paper-like passive tag array onto the target to track the 3D motion, which is battery-free, low-cost, light-weight and portable. RFID-based Approach. Recently, several studies have pro￾posed to utilize the RFID technique to track the motion of tagged objects [1, 6, 9, 11, 15, 17]. Wang et al. [11] track the moving tagged finger in a 2D plane with multiple fixed antennas. Shangguan et al. [9] track the 2D moving trace of the tagged object to obtain user feedbacks with only one fixed antenna. These solutions focus on tracking the moving trace in the 2D space, but they are not suitable for the orientation tracking in the 3D space. Tagyro [13] tracks the 3D orientation of a tagged objects based on the phase differences of the tags via multiple antennas. Liu et al. [7] leverage multiple spinning linearly polarized antenna to estimate the 3D orientation of one tag in a specified 2D plane. However, these methods only can tracks the 3D orientation and are unable to track the absolute translation of the object in the 3D space simultaneously. Different from the prior work, we focus on tracking the rigid motion of the tag array in the 3D space via only one spinning linear polarized antenna, including both the translation and the rotation, which has remained unresolved so far. 2