Research into a RFID Neural Network Localization Algorithm

Transcription

1 , pp Research nto a RFID Neural Network Localzaton Algorthm Jangang Jn Software Technology Vocatonal College, North Chna Unversty of Water Resources and Electrc Power, Zhengzhou , Chna henanjngjg@sna.com Abstract The accuracy of ndoor postonng algorthm has been the focus of research. In ths paper, a partcle swarm optmzaton algorthm based on partcle swarm optmzaton algorthm and K means algorthm s proposed. In ths paper, frstly, the ndoor postonng RFID model s constructed, and the postonng equaton s constructed, then reduce the clusterng algorthm to avod human nterference, through the K means algorthm to form a partcle swarm algorthm to ntalze the partcle swarm algorthm, fnally, the partcle swarm optmzaton algorthm s used to tran all the parameters of RBF neural network, and then the optmal output model s obtaned. Smulaton results show that the algorthm can effectvely mprove the postonng accuracy, reduce energy consumpton, and mprove the postonng accuracy of 10%. Keywords: RFID, Indoor Postonng, Subtractve Clusterng Algorthm, K means RBF 1. Introducton Rado frequency technology s one of the key research technques n the Internet of thngs. It s wdely used n the dentfcaton and markng of object dentty, and has a wde range of applcatons n ntellgent home, logstcs management and so on [1]. Especally n recent years, wdely used n ndoor postonng, has made a lot of research results []. Domestc and foregn scholars on the research, the lterature [3] proposed usng RFID topology network postonng algorthm to mprove the level of automaton of the refuelng system. Ths algorthm not only can montor the object n real tme, but also can detect the workng state of the reader. When the reader fals, t can stll track the object, whch has good robustness; an ndoor localzaton algorthm based on neural network and RFID s proposed n the paper, whch s a fuson of the lterature [4] and neural network. Frst calculated the receved sgnal strength of the room, then the receved sgnal strength as the nput vector of the neural network, path loss coeffcent as the target output of the neural network, through the tranng of neural network set up ndoor feld strength sgnal propagaton model, and genetc algorthm s used to optmze the parameters of the neural network; Fnally, the ndoor postonng performance s tested and analyzed wth concrete examples. Smulaton results show that the proposed algorthm can mprove the ndoor postonng accuracy, and can effectvely meet the requrements of ndoor wreless locaton; A RFID ndoor localzaton algorthm based on BP neural network s proposed n the paper. The algorthm s ntroduced nto the lterature [5] neural network to establsh the model of the feld strength sgnal transformaton by usng the BP neural network. In the model, the nput receved sgnal strength value, output path loss coeffcent, network model to mprove the accuracy of the path loss coeffcent, and then use the dstance - loss model to acheve accurate postonng, thereby reducng the postonng error; The lterature [6] proposes an mproved nearest neghbor algorthm, whch s the best choce to acheve the nearest neghbor label by the selecton of adjacent reference tags; The lterature [7] proposed to use the label poston ISSN: IJFGCN Copyrght c 016 SERSC

2 dstrbuton characterstc as pror nformaton to mprove the postonng accuracy, based on tme of arrval (TOA) two-step weghted least squares method from maxmum lkelhood estmaton s extended to mnmum mean square error estmaton s deduced, and based on the mnmum mean square error estmaton of TOA locaton method of the Cramer Rao lower bound (CRLB) and varance theory smulaton experment proves that the algorthm has good postonng effect; The lterature [8] proposed the use of fractonal Fourer transform-based peak search ESPRIT algorthm localzaton algorthm for sgnal detecton and estmaton of azmuth mult-target non-unform antenna array low. Smulaton results show that the proposed algorthm can dentfy multple frequency and space targets wth hgh accuracy. TDOA hyperbolc postonng method s proposed n the lterature [9]. The method uses rado frequency sgnal TDOA to measure the dstance between the rado frequency (RF) tag and the target wth the precse tme dfference, and then uses the hyperbolc locaton algorthm to locate the target. Based on the above research, ths paper proposes a combnaton of partcle swarm optmzaton algorthm and K means algorthm based on partcle swarm optmzaton algorthm. Frstly, the ndoor postonng RFID model s constructed, and the postonng equaton s constructed, then the algorthm s used to avod human dsturbance, and the ntal soluton of the partcle swarm algorthm s generated by the K means algorthm. Fnally, the all parameters of partcle swarm algorthm are used to tran the RBF neural network, so as to obtan the output optmzaton model to determne the locaton of RFID tags, smulaton experments show that ths algorthm effectvely mproves the postonng accuracy.. Indoor Postonng RFID Model In the ndoor three-dmensonal space, ths paper uses the locaton probablty dstrbuton densty to descrbe the poston of the RFID Reader: N T (0) X X 1 p ( X) K ( X X ) (1) In the formula, NT s the target number; X ndcates the locaton of the reader n the regon; K X s the normalzed coeffcent; s the relable factor for the poston X ; () s the Drac functon. In RFID reader sgnal the sgnal collectng process, due to the nose n the sgnal, deformaton and nonlnear, so n order to facltate the research, hypothess RFID reader to collect sgnal postonng data Z, where the RFID reader poston functon can descrbe as: NT ˆ ( s) f X ( X / Z) K X g X / ( ) Z X X 1 () zm hm ( X e ) vm ( ) ( ) In the formula, ˆ s ˆ s ( ) X X ( Z), 1,, NT s the estmated result of X ; ˆ s gx / ( ) Z X X s the maxmum ( ) value of X at X X ˆ s ; In formula (), Z can be descrbed as a combnaton of multple ndependent poston vectors: Z z T 1, z,, z M (3) Because the sgnal wll nevtably contan other factors such as nose, the RFID vector can be descrbed as: zm hm ( Xe) v m (4) Among them, hm( Xe) s a determnstc functon vector of the RFID electronc label poston e vm s the postonng error vector of the vector v m. X ; 96 Copyrght c 016 SERSC

3 3. Constructng the Postonng Equaton Located n the observaton poston: X [0,0,0] T R, RFID tag and RFID reader locaton are: X [ x, y, z] T and X k [ k, k, k ] T Tr x y z, postonng system as shown n fgure 1. Z Reader Electronc label Electronc label Electronc label X Fgure 1. RFID Indoor Locaton The observaton equaton s establshed accordng to the nformaton of the target angle: arctan( y / x) xsn( ) ycos( ) (5) Because there are some errors n the postonng process, there are: m= + v (6) can get: xsn( m) ycos( m) = xsn( )+ xcos( ) v y cos( ) ysn( ) v (7) xsn( ) y cos( ) xcos( ) ysn( ) v From fgure 1: R x y xcos ycos (8) Constructng the observaton equaton of ptch angle: On xcos ycos at Taylor launched avalable: m m arctan z x y (9) xcos( ) ysn( ) m = xcos( ) xsn( ) v ysn( ) y cos( ) v R y cos( ) xsn( ) v R m Then the formula (9) s: xcos m snm ysnm snm z cosm R snm z cosm (11) The observaton equaton of ptch angle becomes: xcos( m)sn( m) ysn( m)sn( m) zcos( m) Rv (1) Accordng to the relaton of tme dfference, the equaton s constructed: k ( R r k d k ) / c (13) k k k R ( r ) ( R r )( R r ) k k k k k k x x y y z z ( x ) ( y ) ( z ) (14) The formula (13) nto the formula (14), and formula (13) can be obtaned: k k k k x x y y z z ( d ) 1 ( k k R c d k k k k ) c d ( c d ) (15) Fnally, establsh the postonng equaton: k k (16) m v (10) Copyrght c 016 SERSC 97

4 The localzaton of RFID s a typcal nonlnear estmaton problem, and t s dffcult to establsh a precse target locaton model based on the tradtonal method. In order to mprove the postonng accuracy, the partcle swarm optmzaton algorthm s used to optmze the parameters of RBF neural network n ths paper. 4. PSO-RBF Neural Network Model RBF neural network has the advantages of smple structure, fast convergence, easy mplementaton and strong robustness. The RBF neural network usually conssts of 3 layers: nput layer, hdden layer and output layer. PSO algorthm has not only the ablty of global optmzaton, but also local optmzaton. In ths paper, we use the subtractve clusterng algorthm to determne the number of RBF center ponts, determne the ntal soluton of the partcle swarm by K means method, and use the PSO algorthm to tran the Gauss's functon n the RBF neural network, and the hdden layer and output layer weghts Subtractve Clusterng Algorthm In ths method, each data sample s used as the center of the class, and the cluster centers can be determned accordng to the densty of the sample data, and the dstrbuton of the effectve feedback data can be effectvely. When there are SS data samples n the DDD dmensonal space of the data samples for each data pont, the densty formula s M x xj D exp( ) (17) ( / ) j1 In formula (17),, j, respectvely, sad two data samples, densty ndex. Select the hghest densty pont as the frst cluster center, the densty s marked as SS, update the densty ndex of each data pont: M x xcs Dc 1 D exp( ) (18) ( / ) After the update of the densty ndex, select the next cluster center x c to meet the end of the Dmax Dc 1 clusterng. j1, contnuous teraton, 4. K means Algorthm From a data object where a collecton of randomly selected data objects, each object sad a clusterng center and by other data object and the data objects, where the clusterng center dstance contnuous the remanng data center gves to the center of the nearest class, and update new data objects are added to the exstng data center, we n the process teratvely, untl meet the class center does not change so far Partcle Swarm Optmzaton Algorthm Partcle swarm optmzaton (PSO) algorthm s an ntellgent algorthm, manly used to group flght smulaton of brds foragng behavor, n d-dmensonal search space, the poston and velocty of the partcle ( 1,,, m Ζ z, z,, z, V v 1, v,, vd, d gd ) respectvely, 1 D P, ndcated optmal postons n the hstory of the partcle "flght", p sad populaton hstory best poston, the partcle update the velocty and poston accordng to the followng formula: v t 1 v t c rand () p t z t d d 1 d d c rand () p t z t gd d (19) 98 Copyrght c 016 SERSC

5 1 1 z t z t v t d d d (0) Among them, T represents the number of teratons; C1, C for learnng factors; rand () s a random number between [0, 1]; ω s the nerta weght. In ths paper, the nerta weght s updated by lnear change: tmax Among them, max s the ntal weght; mn number of teratons; t s the current teraton number PSO-RBF Algorthm Model max mn t (1) s the fnal weght; tmax s the maxmum (1) Select a sample sze, as the sample pont, the sample accordng to the formula () for normalzaton, the sample sze of the data between [0, 1] x x 1 () xmax xmn () Accordng to the formula (17) calculated the densty of data ponts, whle searchng for the max{ D1, D, DM } data ponts as the frst clusterng center and the densty ndex s obtaned accordng to the formula (18), fnd the D max, and conversely f not found, contnue to look for, untl he found so far. Determne the number of cluster centers (3) The number of cluster centers wll be generated as the number of K means clusterng, and then perform the K means algorthm to generate a set of cluster centers, the results generated n the frst k clusterng for Ck1, Ck, C kn, and constantly teraton, untl the K tmes. At the end of the cluster, the ntal populaton sze of the partcle swarm algorthm s K means, and the ntal populaton sze of the partcle swarm algorthm s K, whch needs to be K clusterng, whch can produce K ntal partcles. (4) Wll have the K group bass functon centers, and randomly generated K a RBF neural network weghts w, Gaussan radus functon r combnaton of C, C, C, w, w, w, r ), produced a total of a K the structure of partcle swarm ( k1 k kn p1 p pk p algorthm as ntal soluton, through the contnuous adjustment of the partcle velocty and qualty, when meet teraton termnaton condton tme, the end of the teraton to determne all the parameters of RBF functon. 5. Target Locaton Algorthm based on PSO-RBF In the actual scene, for nonlnear relatonshps exst n RFID electronc tag target poston and the poston nformaton feld and RBF s nonlnear approxmaton ablty and can therefore PSO-RBF s adopted to establsh a poston nformaton feld to the target locaton mappng model, the steps are as follows Step1: The nformaton feld data of the target poston of the RFID electronc tag s collected and used as an nput, the actual poston of the target s used as the output, and the sample s constructed through the nput and output. Step: The tranng samples are nput to the RBF for learnng, and the partcle swarm optmzaton algorthm s used to optmze the RFB parameters, and the nonlnear estmaton model of the target poston and poston nformaton feld of FRID s obtaned. Step3: The sgnal nput to the RFID electronc tag to be postoned to establsh a nonlnear estmaton model, the estmated poston of the output model. The Flow Chart of the Algorthm s shown n Fgure. Copyrght c 016 SERSC 99

6 RFID tag target locaton Informaton normalzaton processng Buld sample and tranng sample RBF neural network constructon PSO optmzaton parameters Searchng for optmal parameters N Y Set up locaton model Output postonng result 6. Smulaton Experment Fgure. Algorthm Procedure In order to further test the superorty of ths algorthm, we set up 3 sets of experments, each set of 5 electronc tags, and select the 30*30m n the three-dmensonal space for the experment. The expermental group No. Experment 1 Experment Table1. Expermental Data Tag No. coordnates coordnates coordnates Copyrght c 016 SERSC

7 Experment Because n the actual envronment due to the occurrence of nterference, so obtaned a large data exsts certan nose, the algorthm n the lterature [10] n the course of the study consdered these factors, wll compare the algorthm wth reference [10] algorthm. Therefore, comparng the results wth the practce. The comparson results are shown n fgure 3-4. Fgure 5-7 descrbes the error contrast of the 5 tab ponts n the three groups. Fgure 3. Comparson of Two Localzaton Algorthms' Duraton Tme Fgure 4. Comparson of Two Localzaton Algorthms' Energy Consumpton Copyrght c 016 SERSC 301

8 Fgure 5. Comparson of Label Postonng n the Frst Experment Fgure 6. Comparson of Label Postonng n the Second Experment Fgure 7. Comparson of Label Postonng n the thrd Experment It can be found n Fgure 3-4, compared to algorthm proposed n ths paper and the authors algorthm both n postonng tme consumpton and energy consumpton are reduced, manly due to the locaton equaton s constructed usng the PSO-RBF algorthm, so that the postonng s more accurate. Compared wth the three sets of data n Fgure 5-7, the algorthm n ths paper s reduced by 10% to 6% compared wth the lterature algorthm, whch shows that the algorthm can effectvely mprove the RFID postonng accuracy. 7. Concluson How to mprove the postonng accuracy of RFID has been the focus of the study, ths paper frstly constructs ndoor postonng RFID model, and then constructs the postonng equaton. In ths paper, the partcle swarm optmzaton algorthm s used to optmze the parameters of RBF neural network, whch can mprove the accuracy of postonng by reducng the clusterng algorthm and SS algorthm. Smulaton results show that the algorthm can effectvely mprove the postonng accuracy of 10%. 30 Copyrght c 016 SERSC

9 References [1] Q. Z. Hong, Internet of Thngs-orented wreless sensor networks revew, Journal of Electroncs and Infornaton technology, vol. 35, no. 1, (013), pp [] L. L. Na, Summary of study on RFID Indoor localzaton technology, Internatonal Journal of Computer Applcatons and Software, vol. 3, no. 9, (015), pp.1-4. [3] X. Xang, Applcaton of node localzaton algorthm for RFID topology network, Journal of Laonng Techncal Unversty (Natural Scence Edton, vol. 34, no., (015), pp [4] Z. Ka, Indoor Localzaton Algorthm Based on BP Neural Network and RFID, Journal of Laser Applcatons, vol. 36, no. 8, (015), pp [5] W. Chao, Research on RFID Indoor Localzaton Algorthm Based on BP Neural Network, Journal of Computer Smulaton, vol. 3, no. 7, (015), pp [6] S. Y. Bo, Research of RFID Indoor Locaton Algorthm Based on Reference Tags, Journal of Vdeo Engneerng, vol. 39, no. 1, (015), pp [7] L. Y, Mnmum mean square error estmator for Indoor Moble Locaton Usng RFID Technque, Journal of Nanjng Unversty of Posts and Telecommuncatons(Natural Scence), vol. 33, no. 6, (013), pp [8] P. Yong, A Locaton Method for Mult-tags of RFID by a Movable Reader Based on FRFT, Acta Scentarum Naturalum Unvsestats Nankaenss, vol. 46, no. 5, (013), pp [9] Y. G. Hua, RFID hyperbolc postonng usng TDOA method for ndoor movng target, Journal of Computer Applcatons, vol. 34, no.s, (013), pp [10] C. Z. Qang, An RFID Indoor locaton algorthm based on fuzzy neural network model, Journal of Systems Scence and Mathematcal Scences, vol. 34, no. 1, (014), pp Author Jangang Jn, born on November 1971, a Lecturer, Master, and Research Orentaton: Computng Network. Copyrght c 016 SERSC 303

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