tion for the tagged objects.However,according to the phase Meanwhile,in the indoor environment,the wireless signals values from one tag array,it is usually difficult to completely suffer from multipath propagations which will distort the determine the exact orientation of the tagged objects,as there phase values and lead to errors in the localization results. exist multiple possibilities of the orientation state in the 3D Many researches have focused on suppressing the negative space.To address this challenge,we attach three tag arrays to impacts caused by the multipath [10,13].PinIt [10]deploys three mutually orthogonal surfaces of the object,respectively. many reference tags in advance and exploits the similar By comparing the AoA parameters from multiple tag arrays,multipath profiles of the nearby RFIDs which experience a 3DLoc is able to accurately estimate the orientation of the similar multipath environment to pinpoint a tag's location. tagged object.We then select a target tag array vertically MobiTagbot [13]leverages the changing carrier frequency of deployed on the vertical plane,and further figure out the 3D the RFID query to detect whether the phase value is obtained position of the specified object. in severe multipath location,and only uses the phases obtained D.Contributions in low multipath locations for further localization.Similar to To the best of our knowledge,this is the first work to MobiTagbot,3DLoc uses the linear relationship of the AoA consider 3D-localization of tagged objects by using the RFID parameters to find and remove the unexpected outliers which tag arrays.We make three contributions in this paper.1)To result from multipath effect.What's more,instead of localizing perform accurate 3D localization for the tagged objects,we each tag respectively as prior solutions,3DLoc views the tags deploy tag arrays on three mutually orthogonal surfaces of the in the tag array as a whole,and designs a novel algorithm to object.By referring to the fixed layout of the tag array,we calibrate each tag's position referring to the fixed layout of the use the AoA-based schemes to accurately estimate the tagged tag array and finally estimate the location and orientation of object's orientation and 3D coordinates in the 3D space.2)To the tagged object. suppress the localization errors caused by the multipath effect, we propose a mobile scanning-based scheme and use the linear III.MODELING THE 3D LOCALIZATION relationship of the AoA parameters to remove the unexpected A.AoA-based Localization outliers from the estimated results via continuous sampling. The phase value is a common metric in the RFID local- 3)We have implemented a prototype system with the COTS ization system.It reflects the phase rotation between the tag's RFID system,and evaluated the actual performance in the backscattered signal and the signal sent by the antenna.Let d real complex environment.The experimental results show that be the distance between the tag and the reader antenna,then 3DLoc achieves the mean accuracy of 10cm in the free space the backscattered signal traverse a round trip of 2d.Besides and 15.3cm in the multipath environment for the tagged object. the phase rotation over distance,the antenna's transceiver and II.RELATED WORK the tag's reflection characteristic will also introduce additional phase rotations,denoted as A and or,respectively.Hence, Many approaches have been proposed in RFID localization the total phase rotation can be expressed as: system.RSSI information is widely used for localization [1-5],but it is limited for accurate absolute localization 2d 2不 +A+OT mod 2m since RSSI-based approaches are not sensitive enough to (1) distance change.Different from RSSI,the tag's phase value is distance sensitive:the phase difference obtained at two In an RF localizing system,phases obtained at different antenna positions reflects different distances from the tag to the positions are related to the tag's angles of arrival for the corresponding antenna position.Current RF-based localization antenna at different positions.As illustrated in Fig.2,the solutions have great interest in using the phase values to locate antenna interrogates a tag at two different positions x and the tagged objects,and they mainly fall into distance-based x2,with phase readings o and 2.The distances from the methods [6-8].AoA-based methods [9-11],and holography- tag to i and x2 are di and d2,and Ax is the distance of based methods [12,13].For example,BackPos [7]infers 12.According to Eq.1,for the same tag and the same the distance difference from the phases detected by antennas. antenna,and 2 share the same A and or,thus the phase and uses a hyperbolic-based method for localization.RF. difference Ad1.2=1-2 is related to the distance difference IDraw [11]leverages the phase differences as well,but it △d1,2=d1-d2,as: uses an AoA-based method to reconstruct the gestures of a user.Tagoram [12]uses the holography-based method to △p1,2=2m· 2△d2+2kT (2) calculate the possibility of each point being the RF source in the 2D surveillance plane and selects the most likely position where k is an integer which ensures that Ad12 is within the as the tag's location.These above solutions only address the range [0,2].As can be seen from Fig.2,when the tag is 2D localization problem,but fail to provide 3D coordinates relatively far from the antenna,△di,2≈△x·cos9,where 6 of the objects.3DLoc creatively proposes a 3D localization is the angle of arrival of the tag at z(the midpoint of 2). approach:it estimates the rough orientation of the object first, Combined with Eq.2,cos can be expressed as: then calculates the location of the object from the phases of 入（△p1.2-2kπ） the tags in the target tag array. 4π△xtion for the tagged objects. However, according to the phase values from one tag array, it is usually difficult to completely determine the exact orientation of the tagged objects, as there exist multiple possibilities of the orientation state in the 3D space. To address this challenge, we attach three tag arrays to three mutually orthogonal surfaces of the object, respectively. By comparing the AoA parameters from multiple tag arrays, 3DLoc is able to accurately estimate the orientation of the tagged object. We then select a target tag array vertically deployed on the vertical plane, and further figure out the 3D position of the specified object. D. Contributions To the best of our knowledge, this is the first work to consider 3D-localization of tagged objects by using the RFID tag arrays. We make three contributions in this paper. 1) To perform accurate 3D localization for the tagged objects, we deploy tag arrays on three mutually orthogonal surfaces of the object. By referring to the fixed layout of the tag array, we use the AoA-based schemes to accurately estimate the tagged object’s orientation and 3D coordinates in the 3D space. 2) To suppress the localization errors caused by the multipath effect, we propose a mobile scanning-based scheme and use the linear relationship of the AoA parameters to remove the unexpected outliers from the estimated results via continuous sampling. 3) We have implemented a prototype system with the COTS RFID system, and evaluated the actual performance in the real complex environment. The experimental results show that 3DLoc achieves the mean accuracy of 10cm in the free space and 15.3cm in the multipath environment for the tagged object. II. RELATED WORK Many approaches have been proposed in RFID localization system. RSSI information is widely used for localization [1–5], but it is limited for accurate absolute localization since RSSI-based approaches are not sensitive enough to distance change. Different from RSSI, the tag’s phase value is distance sensitive: the phase difference obtained at two antenna positions reflects different distances from the tag to the corresponding antenna position. Current RF-based localization solutions have great interest in using the phase values to locate the tagged objects, and they mainly fall into distance-based methods [6–8], AoA-based methods [9–11], and holography￾based methods [12, 13]. For example, BackPos [7] infers the distance difference from the phases detected by antennas, and uses a hyperbolic-based method for localization. RF￾IDraw [11] leverages the phase differences as well, but it uses an AoA-based method to reconstruct the gestures of a user. Tagoram [12] uses the holography-based method to calculate the possibility of each point being the RF source in the 2D surveillance plane and selects the most likely position as the tag’s location. These above solutions only address the 2D localization problem, but fail to provide 3D coordinates of the objects. 3DLoc creatively proposes a 3D localization approach: it estimates the rough orientation of the object first, then calculates the location of the object from the phases of the tags in the target tag array. Meanwhile, in the indoor environment, the wireless signals suffer from multipath propagations which will distort the phase values and lead to errors in the localization results. Many researches have focused on suppressing the negative impacts caused by the multipath [10, 13]. PinIt [10] deploys many reference tags in advance and exploits the similar multipath profiles of the nearby RFIDs which experience a similar multipath environment to pinpoint a tag’s location. MobiTagbot [13] leverages the changing carrier frequency of the RFID query to detect whether the phase value is obtained in severe multipath location, and only uses the phases obtained in low multipath locations for further localization. Similar to MobiTagbot, 3DLoc uses the linear relationship of the AoA parameters to find and remove the unexpected outliers which result from multipath effect. What’s more, instead of localizing each tag respectively as prior solutions, 3DLoc views the tags in the tag array as a whole, and designs a novel algorithm to calibrate each tag’s position referring to the fixed layout of the tag array and finally estimate the location and orientation of the tagged object. III. MODELING THE 3D LOCALIZATION A. AoA-based Localization The phase value is a common metric in the RFID local￾ization system. It reflects the phase rotation between the tag’s backscattered signal and the signal sent by the antenna. Let d be the distance between the tag and the reader antenna, then the backscattered signal traverse a round trip of 2d. Besides the phase rotation over distance, the antenna’s transceiver and the tag’s reflection characteristic will also introduce additional phase rotations, denoted as ϕA and ϕT , respectively. Hence, the total phase rotation ϕ can be expressed as: ϕ = ( 2π · 2d λ + ϕA + ϕT ) mod 2π (1) In an RF localizing system, phases obtained at different positions are related to the tag’s angles of arrival for the antenna at different positions. As illustrated in Fig.2, the antenna interrogates a tag at two different positions x1 and x2, with phase readings ϕ1 and ϕ2. The distances from the tag to x1 and x2 are d1 and d2, and ∆x is the distance of |x1x2|. According to Eq.1, for the same tag and the same antenna, ϕ1 and ϕ2 share the same ϕA and ϕT , thus the phase difference ∆ϕ1,2 = ϕ1−ϕ2 is related to the distance difference ∆d1,2 = d1 − d2, as: ∆ϕ1,2 = 2π · 2∆d1,2 λ + 2kπ (2) where k is an integer which ensures that ∆ϕ1,2 is within the range [0, 2π]. As can be seen from Fig.2, when the tag is relatively far from the antenna, ∆d1,2 ≈ ∆x · cos θ, where θ is the angle of arrival of the tag at x (the midpoint of x1x2). Combined with Eq.2, cos θ can be expressed as: cos θ = λ (∆ϕ1,2 − 2kπ) 4π∆x