which is related to time delay.K and T represents different VIII.CONCLUSION thresholds in RSSI.Thereinto,r concerns the rough distance We have presented a system for locating objects without to start estimating angle and k is used to terminate the whole reference points based on RFID from the experimental point process,which are related to the environment and demand of view.This system is suited for a typical scenario,i.e., of accuracy.All the parameters have an overall effect on locating an object from a specific point.We propose an the performance of localization,which can be determined iterative pattern based localization method according to several according to the accuracy demand and the environment. performance experiments and conduct the system experiment in the realistic environment.Our experiments show that our VII.PERFORMANCE EVALUATION method can achieve average error within 49cm and reduce We evaluate the performance of our system in realistic the total navigation time by 33%compared to the baseline solution environment.Our experiment platform is based mainly on the robot,as shown in Fig.10.We deploy different tag- ACKNOWLEDGMENTS attached objects in the room with the same height as the This work is supported in part by National Natural Science antenna and examine our system by locating each objects 50 Foundation of China under Grant No.61100196.61073028. times.To reveal the advantage of the pattern based method,we 61321491,91218302;JiangSu Natural Science Foundation compare with a baseline solution,described below.We rotate under Grant No.BK2011559. the antenna a whole circle and scan the field meanwhile.Then the antenna approaches along the angle representing the largest REFERENCES RSSI value iteratively until locating the object.At the same [1]B.Ferris,D.Fox,and N.D.Lawrence,"Wifi-slam using gaussian time,we rectify the angle to improve the accuracy. 12Pangnnytm based o As shown in Fig.11,without any help of reference tags, discriminant-adaptive neural network in ieee 802.11 environments," near 80%objects localization error is within 60cm.which is Neural Networks,IEEE Transactions on,2008. accurate enough for realistic application.And Fig.12 shows [3]S.-H.Fang,T.-N.Lin,and K.-C.Lee,"A novel algorithm for multipath that about 80%objects are located within 60 seconds,which fingerprinting in indoor wlan environments,"Wireless Communications. IEEE Transactions on,2008. is bearable dealing with realistic problem.When compared [4]L.Aalto,N.Gothlin,J.Korhonen,and T.Ojala,"Bluetooth and wap with the baseline solution,pattern based method reduces the push based location-aware mobile advertising system,"in Proceedings average navigation time by 33%.In regard to the accuracy, of the 2nd international conference on Mobile systems,applications,and services,2004. because the distance measurement is mainly based on the [5]J.Blumenthal,R.Grossmann,F.Golatowski,and D.Timmermann, RSSI samples with the smallest tag-antenna distance,the two "Weighted centroid localization in zigbee-based sensor networks,"in methods share similar localization error. Intelligent Signal Processing.2007.WISP 2007.IEEE International Symposium on,2007. In order to deeply understanding the time delay,we analyze [6]L.M.Ni,Y.Liu,Y.C.Lau,and A.P.Patil,"Landmarc:indoor location query times individually.As shown in Fig.13,the overall sensing using active rfid,"Wireless networks,2004. query times is less than 60 times,which costs less than 10 [7]J.Wang and D.Katabi,"Dude,where's my card?:Rfid positioning that works with multipath and non-line of sight,"in Proceedings of the ACM seconds in reality.Besides,we note coarse-rotation occupies SIGCOMM 2013 conference on SIGCOMM.2013. the most query times.This is because in realistic environment, [8]P.V.Nikitin,R.Martinez.S.Ramamurthy.H.Leland,G.Spiess,and the RSSI value is not stable caused by multipath effect, K.Rao."Phase based spatial identification of uhf rfid tags,"in RFID, 2010 IEEE International Conference on,2010. which results in several iterative angle estimate processes. [9]S.Azzouzi,M.Cremer,U.Dettmar,R.Kronberger,and T.Knie, And fine-grained rotation only occupies about 10 times,which "New measurement results for the localization of uhf rfid transponders proves quadratic function fitting plays an important role.In the using an angle of arrival (aoa)approach,"in RFID (RFID).2011 IEEE International Conference on,2011. baseline solution,continuous scanning leads to hundreds query [10] C.Hekimian-Williams,B.Grant,X.Liu,Z.Zhang,and P.Kumar, times,which is abridged in the figure. "Accurate localization of rfid tags using phase difference,"in RFID. 2010 IEEE International Conference on.2010. Beside the performance of localization as a whole,we [11]J.Hightower,R.Want,and G.Borriello,"Spoton:An indoor 3d evaluate the performance of iterative routine.As shown in location sensing technology based on rf signal strength,"UW CSE00. Fig.14,when we re-estimate the angle iteratively,we can 02-02,University of Washington,Department of Computer Science and Engineering.Seattle.WA. reduce the angle error,which means the iterative routine [12]D.Hahnel,W.Burgard,D.Fox,K.Fishkin,and M.Philipose,"Mapping effectively improve the accuracy.Because most localizations and localization with rfid technology,"in Robotics and Automation are done in the first three iterations.the forth iteration contains Proceedings.ICRA'04.2004 IEEE International Conference on.2004. [13]R.Miesen,F.Kirsch,and M.Vossiek,"Holographic localization of fewer samples leading to bigger error slightly. passive uhf rfid transponders."in RFID (RFID).2011 IEEE International In Fig.15,we compare the estimate distance in each Conference on,2011. approaching step with the real position of objects.In the [14]T.Deyle,H.Nguyen,M.Reynolds,and C.C.Kemp."Rf vision:Rfid receive signal strength indicator (rssi)images for sensor fusion and figure,x axis represents the real tag-antenna distance range. mobile manipulation,"in Intelligent Robots and Systems.2009.IROS As distance decreases,the estimate distance error decreases, 2009.IEEE/RSJ International Conference on,2009. which is due to the RSSI at close tag-antenna distance can [15]A.Nemmaluri,M.D.Corner,and P.Shenoy,"Sherlock:automatically locating objects for humans,"in Proceedings of the 6th international provide more reliable RSSI information for localization.This conference on Mobile systems,applications,and services,2008. is the main advantage of the iterative routine.which is related to time delay. κ and τ represents different thresholds in RSSI. Thereinto, τ concerns the rough distance to start estimating angle and κ is used to terminate the whole process, which are related to the environment and demand of accuracy. All the parameters have an overall effect on the performance of localization, which can be determined according to the accuracy demand and the environment. VII. PERFORMANCE EVALUATION We evaluate the performance of our system in realistic environment. Our experiment platform is based mainly on the robot, as shown in Fig. 10. We deploy different tag￾attached objects in the room with the same height as the antenna and examine our system by locating each objects 50 times. To reveal the advantage of the pattern based method, we compare with a baseline solution, described below. We rotate the antenna a whole circle and scan the field meanwhile. Then the antenna approaches along the angle representing the largest RSSI value iteratively until locating the object. At the same time,we rectify the angle to improve the accuracy. As shown in Fig. 11, without any help of reference tags, near 80% objects localization error is within 60cm, which is accurate enough for realistic application. And Fig. 12 shows that about 80% objects are located within 60 seconds, which is bearable dealing with realistic problem. When compared with the baseline solution, pattern based method reduces the average navigation time by 33%. In regard to the accuracy, because the distance measurement is mainly based on the RSSI samples with the smallest tag-antenna distance, the two methods share similar localization error. In order to deeply understanding the time delay, we analyze query times individually. As shown in Fig. 13, the overall query times is less than 60 times, which costs less than 10 seconds in reality. Besides, we note coarse-rotation occupies the most query times. This is because in realistic environment, the RSSI value is not stable caused by multipath effect, which results in several iterative angle estimate processes. And fine-grained rotation only occupies about 10 times, which proves quadratic function fitting plays an important role. In the baseline solution, continuous scanning leads to hundreds query times, which is abridged in the figure. Beside the performance of localization as a whole, we evaluate the performance of iterative routine. As shown in Fig.14, when we re-estimate the angle iteratively, we can reduce the angle error, which means the iterative routine effectively improve the accuracy. Because most localizations are done in the first three iterations, the forth iteration contains fewer samples leading to bigger error slightly. In Fig. 15, we compare the estimate distance in each approaching step with the real position of objects . In the figure, x axis represents the real tag-antenna distance range. As distance decreases, the estimate distance error decreases, which is due to the RSSI at close tag-antenna distance can provide more reliable RSSI information for localization. This is the main advantage of the iterative routine. VIII. CONCLUSION We have presented a system for locating objects without reference points based on RFID from the experimental point of view. This system is suited for a typical scenario, i.e., locating an object from a specific point. We propose an iterative pattern based localization method according to several performance experiments and conduct the system experiment in the realistic environment. Our experiments show that our method can achieve average error within 49cm and reduce the total navigation time by 33% compared to the baseline solution. ACKNOWLEDGMENTS This work is supported in part by National Natural Science Foundation of China under Grant No. 61100196, 61073028, 61321491, 91218302; JiangSu Natural Science Foundation under Grant No. BK2011559. REFERENCES [1] B. Ferris, D. Fox, and N. D. Lawrence, “Wifi-slam using gaussian process latent variable models.” in IJCAI, 2007. [2] S.-H. Fang and T.-N. Lin, “Indoor location system based on discriminant-adaptive neural network in ieee 802.11 environments,” Neural Networks, IEEE Transactions on, 2008. [3] S.-H. Fang, T.-N. Lin, and K.-C. Lee, “A novel algorithm for multipath fingerprinting in indoor wlan environments,” Wireless Communications, IEEE Transactions on, 2008. [4] L. Aalto, N. G ¨othlin, J. Korhonen, and T. Ojala, “Bluetooth and wap push based location-aware mobile advertising system,” in Proceedings of the 2nd international conference on Mobile systems, applications, and services, 2004. [5] J. Blumenthal, R. Grossmann, F. Golatowski, and D. Timmermann, “Weighted centroid localization in zigbee-based sensor networks,” in Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on, 2007. [6] L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, “Landmarc: indoor location sensing using active rfid,” Wireless networks, 2004. [7] J. Wang and D. Katabi, “Dude, where’s my card?: Rfid positioning that works with multipath and non-line of sight,” in Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM, 2013. [8] P. V. Nikitin, R. Martinez, S. Ramamurthy, H. Leland, G. Spiess, and K. Rao, “Phase based spatial identification of uhf rfid tags,” in RFID, 2010 IEEE International Conference on, 2010. [9] S. Azzouzi, M. Cremer, U. Dettmar, R. Kronberger, and T. Knie, “New measurement results for the localization of uhf rfid transponders using an angle of arrival (aoa) approach,” in RFID (RFID), 2011 IEEE International Conference on, 2011. [10] C. Hekimian-Williams, B. Grant, X. Liu, Z. Zhang, and P. Kumar, “Accurate localization of rfid tags using phase difference,” in RFID, 2010 IEEE International Conference on, 2010. [11] J. Hightower, R. Want, and G. Borriello, “Spoton: An indoor 3d location sensing technology based on rf signal strength,” UW CSE 00- 02-02, University of Washington, Department of Computer Science and Engineering, Seattle, WA. [12] D. Hahnel, W. Burgard, D. Fox, K. Fishkin, and M. Philipose, “Mapping and localization with rfid technology,” in Robotics and Automation. Proceedings. ICRA’04. 2004 IEEE International Conference on, 2004. [13] R. Miesen, F. Kirsch, and M. Vossiek, “Holographic localization of passive uhf rfid transponders,” in RFID (RFID), 2011 IEEE International Conference on, 2011. [14] T. Deyle, H. Nguyen, M. Reynolds, and C. C. Kemp, “Rf vision: Rfid receive signal strength indicator (rssi) images for sensor fusion and mobile manipulation,” in Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, 2009. [15] A. Nemmaluri, M. D. Corner, and P. Shenoy, “Sherlock: automatically locating objects for humans,” in Proceedings of the 6th international conference on Mobile systems, applications, and services, 2008