● ●0pbr8 民otatiee5 cale:-8.+ RFID angle (a)The deployment of multiple tagged objects (b)Variation of the depth value (c)Variation of the RSSI value Figure 7.The experiment results of continuous scanning Extract Depth via Continuous Scanning multi-path fading and path loss.However,since most mid-and In this section,we present our approach to extract the depth low-end COTS RFID systems can only extract the RSSI from series via continuous scanning,so as to derive both the vertical RF-signals,we need to figure out a solution based on RSSI. distance and the horizontal distance of the tagged objects. In this section,we present our approach to pair the tags with objects according to the correlations between the depth and During the continuous scanning,we continuously rotate the RSSI in continuous scanning. depth camera from the angle of-0 to +0 and use it to scan the multiple tagged objects.During this process,as the vertical According to the observations from Figure 4,with different distance between the specified objects and the depth camera is offset degrees from the tag to the center of antenna beam.the continuously changing,the depth values collected from these RSSI changes with a fixed variation pattern.This implies that, objects are also continuously changing.We conduct exper- if we conduct the continuous scanning to the tagged objects. iments to validate this judgment.As shown in Figure 7(a). the RSSI from the tag always reaches the maximum value we arbitrarily deploy multiple tagged objects within the ef- when the antenna is right facing towards the tag.This varia- fective scanning range,the coordinates of these objects are tion pattern of RSSI is quite similar to the depth value,since also labeled.We continuously rotate the depth camera from they both reach the peak value when the tagged object is at the angle of-40 to +40 and collect the depth values from the perpendicular point of depth camera/RFID antenna.We multiple tagged objects for every 5~6 degrees.Figure 7(b) further conduct experiments to validate the above judgment. shows the experiment results.We use the method of quadratic Using the delpoyment in Figure 7(a),we continuously rotate curve fitting to connect the depth values as a curve for a certain the RFID antenna from the angle of-40 to +40 and collect object.We find that the series of depth values for each object the RSSI from multiple tags.As shown in Figure 7(c),the vari- actually form a convex curve with a peak value.This peak ation of RSSI for each tag has very similar features as depth: value denotes the snapshot when the vertical distance reaches during the continuous scanning,the RSSI first increases to a the maximum value.It appears only when the perpendicular maximum value.and then further decreases to a certain value. bisector of the depth camera crosses the specified object,since The only difference is that the RSSI is inversely corresponding the vertical distance reaches the value of the absolute distance with the depth value for any specified object.i.e.,the larger the between the object and the depth camera,which is the theo- RSSI,the smaller the depth.Therefore,we can also label each retical upper bound it can achieve.In other words,the peak tag with the coordinate of its peak value,i.e.,(0,r),where value appears when the depth camera is right facing towards represents the rotation angle and r represents the RSSI.We the object,we call this perpendicular point. can respectively use the RSSI r and the rotation angle 0 to distinguish the tags in vertical and horizontal dimensions In this way,according to the peak value of depth,we are able to further distinguish multiple objects with the same vertical Therefore,in order to pair multiple tags with multiple objects, distance but different positions.The solution is as follows: we propose a matching solution in Algorithm 1.Our goal is to After the system finishes continuous scanning,it extracts the find a matching between two disjoint sets O and 7 according peak value from the curve of each object's depth value.Then, to the correlation of their measurements.After we extract we label each object with the coordinate of its peak value,i.e.. the vector from the measured data,for any object O;with (0,d),where 0 represents the rotation angle and d represents vector (0,di),we first select the candidate tags for pairing the depth value.Therefore,as the depth d denotes the verti according to the angle 0.We set all tags as pairing candidates cal distance of objects,we can use the depth to distinguish with their angles in the range[a:-δ，a+6](δ=5°in our the objects in the vertical dimension:as the rotation angle 0 implementation).Then we further compare their values in denotes the angle for the camera to meet the perpendicular RSSI and depth.As the RSSI and the depth are measured in point,we can use the angle to distinguish the objects in the different dimensions,e.g.,the depth value is linearly correlated horizontal dimension.They can be easily distinguished from to the distance,while the RSSI is nonlinearly correlated to the horizontal dimension. the distance,it is not reasonable to compare them directly We thus match each object to a candidate tag based on their Pair the Tags with Objects according to Depth and RSSI relative rank in RSSI and depth.After that,since multiple It is known that the RSSI is not a very reliable metric to accu- objects may be matched to one tag,we make the tag select the rately measure the distance between the tags and the antennas. object with the closest rank as the final pair.This process then iterates until all the objects and tags are paired. as it is easy to be impacted by the environmental factors likeRFID Antenna y(m) 0.5 1.5 1 2 2.5 x(m) Object1 (0,0.85) Object2 (-0.23,1.2) Object3 (0.35,1.3) Object4 (-0.6,1.8) Object5 (0.6,1.8) 3D-Camera Rotation Scale:[-ș,+ș] (a) The deployment of multiple tagged objects −40 −20 0 20 40 500 1000 1500 2000 Rotation angle Depth value(mm) Object1 Object2 Object3 Object4 Object5 (b) Variation of the depth value −40 −20 0 20 40 −65 −60 −55 −50 −45 −40 Rotation angle RSSI value(dBm) Tag1 Tag2 Tag3 Tag4 Tag5 (c) Variation of the RSSI value Figure 7. The experiment results of continuous scanning Extract Depth via Continuous Scanning In this section, we present our approach to extract the depth series via continuous scanning, so as to derive both the vertical distance and the horizontal distance of the tagged objects. During the continuous scanning, we continuously rotate the depth camera from the angle of −θ to +θ and use it to scan the multiple tagged objects. During this process, as the vertical distance between the specified objects and the depth camera is continuously changing, the depth values collected from these objects are also continuously changing. We conduct exper￾iments to validate this judgment. As shown in Figure 7(a), we arbitrarily deploy multiple tagged objects within the ef￾fective scanning range, the coordinates of these objects are also labeled. We continuously rotate the depth camera from the angle of −40◦ to +40◦ and collect the depth values from multiple tagged objects for every 5∼6 degrees. Figure 7(b) shows the experiment results. We use the method of quadratic curve fitting to connect the depth values as a curve for a certain object. We find that the series of depth values for each object actually form a convex curve with a peak value. This peak value denotes the snapshot when the vertical distance reaches the maximum value. It appears only when the perpendicular bisector of the depth camera crosses the specified object, since the vertical distance reaches the value of the absolute distance between the object and the depth camera, which is the theo￾retical upper bound it can achieve. In other words, the peak value appears when the depth camera is right facing towards the object, we call this perpendicular point. In this way, according to the peak value of depth, we are able to further distinguish multiple objects with the same vertical distance but different positions. The solution is as follows: After the system finishes continuous scanning, it extracts the peak value from the curve of each object’s depth value. Then, we label each object with the coordinate of its peak value, i.e., hθ,di, where θ represents the rotation angle and d represents the depth value. Therefore, as the depth d denotes the verti￾cal distance of objects, we can use the depth to distinguish the objects in the vertical dimension; as the rotation angle θ denotes the angle for the camera to meet the perpendicular point, we can use the angle to distinguish the objects in the horizontal dimension. They can be easily distinguished from the horizontal dimension. Pair the Tags with Objects according to Depth and RSSI It is known that the RSSI is not a very reliable metric to accu￾rately measure the distance between the tags and the antennas, as it is easy to be impacted by the environmental factors like multi-path fading and path loss. However, since most mid-and low-end COTS RFID systems can only extract the RSSI from RF-signals, we need to figure out a solution based on RSSI. In this section, we present our approach to pair the tags with objects according to the correlations between the depth and RSSI in continuous scanning. According to the observations from Figure 4, with different offset degrees from the tag to the center of antenna beam, the RSSI changes with a fixed variation pattern. This implies that, if we conduct the continuous scanning to the tagged objects, the RSSI from the tag always reaches the maximum value when the antenna is right facing towards the tag. This varia￾tion pattern of RSSI is quite similar to the depth value, since they both reach the peak value when the tagged object is at the perpendicular point of depth camera/RFID antenna. We further conduct experiments to validate the above judgment. Using the delpoyment in Figure 7(a), we continuously rotate the RFID antenna from the angle of −40◦ to +40◦ and collect the RSSI from multiple tags. As shown in Figure 7(c), the vari￾ation of RSSI for each tag has very similar features as depth: during the continuous scanning, the RSSI first increases to a maximum value, and then further decreases to a certain value. The only difference is that the RSSI is inversely corresponding with the depth value for any specified object, i.e., the larger the RSSI, the smaller the depth. Therefore, we can also label each tag with the coordinate of its peak value, i.e., hθ,ri, where θ represents the rotation angle and r represents the RSSI. We can respectively use the RSSI r and the rotation angle θ to distinguish the tags in vertical and horizontal dimensions. Therefore, in order to pair multiple tags with multiple objects, we propose a matching solution in Algorithm 1. Our goal is to find a matching between two disjoint sets O and T according to the correlation of their measurements. After we extract the vector from the measured data, for any object Oi with vector hθi ,dii, we first select the candidate tags for pairing according to the angle θi . We set all tags as pairing candidates with their angles in the range [θi −δ,θi +δ] (δ = 5 ◦ in our implementation). Then we further compare their values in RSSI and depth. As the RSSI and the depth are measured in different dimensions, e.g., the depth value is linearly correlated to the distance, while the RSSI is nonlinearly correlated to the distance, it is not reasonable to compare them directly. We thus match each object to a candidate tag based on their relative rank in RSSI and depth. After that, since multiple objects may be matched to one tag, we make the tag select the object with the closest rank as the final pair. This process then iterates until all the objects and tags are paired