A Novel Wireless Localization Fusion Algorithm: BP-LS-RSSI

Transcription

1 Sensors & ransucers Vol. 60 Issue December 0 pp. 7- Sensors & ransucers 0 by IFSA A Novel Wreless Localzaton Fuson Algorthm: BP-LS-RSSI Yuanqang WANG Shang Fu HAO Any LAN Je YANG School of Informaton Scence an Engneerng Hebe North Unversty He Be Chna E-mal: hbnuwyq@6.com 878@qq.com papernetwor@6.com Receve: 6 October 0 /Accepte: November 0 /Publshe: 0 December 0 Abstract: Wth the ncreasng eman for locaton-aware servces hgh-precson noor postonng play more mportant role for some applcatons. People also put forwar hgher requrements on postonng accuracy. BP neural networ as a n of typcal forwar neural networ has the very strong self learnng ablty an can approxmate any scontnuty of ratonal functon. hs paper proposes BP-LS-RSSI localzaton moel then use the moel to fx receve sgnal strength ncaton (RSSI values for postonng by the LS algorthm. Snce the postonng accuracy o not satsfy the nees by the tratonal LS algorthm we transfer the RSSI values nto confence weghts accorng to the topology of networ then use the weghte least squares (LS metho to further optmze the postonng system. Smulaton results show that the propose algorthm has obvous ncrease to the postonng accuracy s a feasble localzaton algorthm. Copyrght 0 IFSA. Keywors: BP neural networ Least square algorthm RSSI Relablty weght.. Introucton Wth the avancement of communcatons technology embee computng technology mcroprocessng technology an sensor technology the wreless sensor networs (WSNs have began to receve wely attenton as ts percepton computng an communcaton capabltes [ ]. For most applcatons only the combnaton of locaton nformaton the sensor ata obtane have practcal sgnfcance. he rap evelopment of wreless sensor networs mae wreless postonng technology has become one of the man ways n the mltary proucton all areas of lfe whch s wely use. Artfcal Neural Networ (ANN s a manmae neural networ structure can mplement some functons base on the unerstanng of the human bran. ANN has been wely use n varous fels n the process of wreless localzaton mult-sensor fuson postonng whch s got the people's attenton. By tranng the neural networ system t s possble to fn out the new an effectve way to solve non-lne-of-sght (NLOS problems for wreless postonng [ ]. Accorng to whether measure the stance n the process of localzaton wreless localzaton algorthms can be ve nto range-free an rangebase algorthm. he algorthm base on range-free whch o not measure the stance an angle but calculate the locaton by networ connectvty nformaton. hs type of algorthms o not requre atonal harware support the communcaton overhea s smaller. he range-base algorthms measure the between base staton an moble noe by electromagnetc wave. here are some nstances as followng: angle of arrval (AOA tme of arrval (OA tme fferent of arrval (DOA receve sgnal strength ncaton (RSSI [-8]. As wreless communcaton envronment becomes ncreasngly complex a sngle localzaton algorthm can not meet the nee of precse postonng collaboratve multstaton mult- algorthm fuson s the man trens for postonng. We try to use BP neural networ to amen the RSSI value an then use the value Artcle number P_9 7

2 Sensors & ransucers Vol. 60 Issue December 0 pp. 7- amene to calculate locaton of moble noe by LS algorthm. Snce the postonng accuracy oes not satsfy the nee by the LS algorthm we transfer the RSSI values nto confence weghts accorng to the topology of networ then use the weghte least squares metho to further optmze the postonng system. Smulaton results show that the algorthm sgnfcantly mprove postonng accuracy s a vable localzaton algorthm. he remaner of ths paper s organze as follows: We scuss the RSSI moel n Secton. In Secton we overvew the bacgroun of postonng algorthms. Secton emphaszes the fuson algorthm for postonng Secton outlnes smulaton result an Secton 6 conclues ths paper. n Fg.. Input layer has 6 sgnal strengths (RSSI values supporte by 6 base staton noes.. RSSI Moel he relatonshp between the launchng power an recevng power of the wreless sgnal can be presente by formula ( P P / r Bs Ms n ( where P Bs P Ms r n ncates the recevng power launchng power stance an transmtte vsor respectvely. Among them the magntue of the transmtte vsor epens on the envronment of the sgnal transmsson. Once we obtan the logarthm of formula ( then we can get 0n lgr 0lgPMs/ PBs 0lgPBs A0n lgr ( Among them A s the recevng power of the sgnal when the sgnal s transmtte n free space for m. 0 nlg r s the manfestaton of the recevng power of sgnal converte nto Bm an t can be recore as P Bs ( Bm A 0nlg r ( In real envronment especally noor envronment wreless sgnal wll occur obstacle whch result n ffracton an multpath effects urng the pero of transmsson. here s certan error between the actual an measure RSSI values. If there are more obstacles n room the problem s more obvous. herefore the measure RSSI values t s necessary to approprately mofe to mae t more lely close to the actual values.. Overvews.. BP Neural Networ BP neural networ moel s compose of nput layer hen layer an output layer whch as shown Fg.. BP neural networ amenment moel for RSSI measurements. Input vector: P [ RSSI RSSI RSSI RSSI RSSI RSSI 6] ( he number of neurons at hen layer can be fgure out wth emprcal equaton.e. N log wheren N s the number of neurons at hen layer whle represents the menson of tranng sample. Increase of the number of neurons at hen layer helps to mprove localzaton accuracy yet brng about heaver calculaton loa. In vew of the fact that ths Paper uses a relatvely small number of samples an attaches more mportance to accuracy 8 neurons are selecte at hen layer. he transfer functon at hen layer s sgmo functon f( x tanh( x of whch the nput value can be arbtrary value whle the output value can be between - an +. he output layer s compose of 6 neurons an employs lnear transfer functon Pureln.e. f ( x x whose output s the nterpolaton of fngerprnt atabase. he output vector s gven below. O [ r r r r r6 r7] ( Learnng algorthm for BP networ s as follows: assumng that the nput layer s P the number of nput neurons s r there are s neurons at hen layer the corresponng actvaton functon s f there are s neurons at output layer the corresponng actvaton functon s f the output s A an the target vector s. w j represents the connecton weght between nput layer an hen layer whle w j represents the connecton weght between hen layer an output layer. 8

3 Sensors & ransucers Vol. 60 Issue December 0 pp. 7- he output of the th neuron at hen layer: a r f( w p b... s j j j Output of the K th neuron at output layer: a s f( wa b... s Defnton error functon: (6 (7 estmaton methos. In general the LS estmaton can be escrbe as: ( x x ( y y ( where =... K. K means the number of BS noes. Square both ses of Formula ( we are able to get Rxx yy ( x y xx yy R x y 0. ( ( s E( W B ( t a (8 Determne the weght varaton an the bac propagaton of error usng graent-escent algorthm; the change n the weght of output layer s proportonal to the negatve graent of error functon aganst the weght of output layer: a s f( wa b... s (9 he weght of output layer s upate accorng to formula (0: w ( t w ( t w ( t (0 Change n the weght of hen layer s proportonal to the negatve graent of error functon aganst the weght of hen layer: E w( t wj E a. a w ' ( t a. fa ( he weght of hen layer s upate accorng to Formula (: wj ( t wj ( t wj ( t ( where R x y then formula ( can be presente n the followng matrx form where H X ( x y 0. x H y x y 0. R x y X x y Constructon estmator performance ncators J( ( X H ( X H we can mae the error reach to the mnmum only when we can get J ( to become the mnmum. hus J( [( X H ( X H] H ( X H 0 ( H H H X (6 he we get from ths calculaton s the estmate coornate pont of LS of MS. However as the localzaton accuracy s not hgh enough an ts functon cannot be evaluate n the LS localzaton algorthm therefore ts accuracy nees to be mprove further... LS Algorthm Least squares estmaton s an ancent an effectve estmaton metho because t oes not requre any pror nowlege you only nee to be estmate on the amount of the observe sgnal moel we can acheve sgnal parameter estmaton an easy to mplement an square error can be mnmze so t s a very broa applcaton of. Localzaton Algorthm.. RSSI Value Amen by BP Neural Networ Fg. shows fve base statons (BS an a moble noe (MS the RSSI value can be obtane fve groups ( RSSI RSSI RSSI RSSI RSSI. he real envronment especally n noor 9

4 Sensors & ransucers Vol. 60 Issue December 0 pp. 7- envronment the wreless sgnal transmsson encounter obstacles whch wll occur ffracton an result n multpath effect. Some external factors mpact the measure RSSI value; there s error between measure an actual RSSI values. We obtane BP neural networ RSSI value s correcte so that the measure RSSI value s more lely closer to the actual value. Locaton estmaton of least-square metho although able to mnmze the stance sum of square resuals but there are stll some resual error can also cause the error s ffcult to avo weghtng approach can be use to reuce ths n of error. We wll bass ponts (BS n the receve sgnal strength (RSSI nto the weghts of creblty to the weghte least square metho (r. he optmal estmate fnal output noe (X Y. x r x r x r x r XY yr yr yr yr ( ( (0 he LS - RSSI base on BP neural networ ata fuson localzaton algorthm can be represente as shown n Fg.. Fg.. Postonng noe topology. ( RSSI RSSI RSSI RSSI RSSI ransfer to relablty weght ( R R R R R (7.. he Weghte LS Algorthm Base on RSSI Relablty Weght We can obtan fve groups stance value ( by the RSSI value ( RSSI RSSI RSSI RSSI RSSI transfer ( to a matrx as shown n formula (8. (8 we use LS estmaton algorthm to calculate the locaton M tmes then calculate the mean value( x y ( x y ( x y ( x y ( x y. he formula s shown n (9 ( x y ( x y ( H H H X ( x y ( x y ( x y (9 Fg.. LS - RSSI localzaton flow optmze by BP neural networ.. Smulaton hs paper uses the MALAB smulaton tools to valate the effectveness of the algorthm an mae a comparson wth the non-optmzaton algorthm. In smulaton experence MS emsson power s Bm sprea envronment s represente by logarthm-normal strbuton moe. Manly for the postonng of the moble noe selecte MS ( as be estmate noe BS (0 0 BS ( BS ( BS ( BS ( as the fve base staton noe. In the D coornates the use of square error AVG of postonng results as a general postonng accuracy as shown n formula (. AVG ( X x ( Y y ( 0

5 Sensors & ransucers Vol. 60 Issue December 0 pp. 7- Fg. shows comparson of the precson for LS an BP-LS-RSSI. staton postonng an the relablty weghte effectvely elmnates the error of LS localzaton algorthm sgnfcantly mprove the postonng accuracy. Smulaton results show that ths postonng algorthm oes not requre harware extenson a small amount of calculaton; the ornary LS algorthm s effectve to stealy mprove the postonng accuracy. Acnowlegements (a he authors woul le to than the research of Fxe Assets Management & Evaluaton system Base on the technology of Internet of thngs whch supporte by the Scence an echnology Bureau of Zhang Jaou Hebe provnce. for ther support n ths research. References (b Fg.. he comparson of accuracy between LS an BP-LS-RSSI. We can be seen from the Fg. BP-LS-RSSI localzaton algorthm of the weghte fuson accuracy compare wth the sngle LS algorthm has sgnfcantly mprove; expermental smulaton show that LS-RSSI base on BP neural networ ata fuson locaton algorthm s a feasble an effectve metho. 6. Conclusons Base on the stuy of LS RSSI localzaton algorthm an BP neural networ algorthm propose a BP-LS-RSSI fuson algorthm. he metho usng the learnng feature of neural networ s faster an the ablty to approxmate any nonlnear mappng mae t sutable for complcate multpath envronment. By BP neural networ correcton for RSSI value effectvely restran the sgnal n the process of NLOS transmsson error an through the optmzaton of base staton selecton rotate the base []. J. Lu Q. Wang J. Wan J. Xong B. Zeng owars Key Issues of Dsaster A base on Wreless Boy Area Networs IIS Vol. 7 0 pp []. H. Suo J. Wan L. Huang C. Zou Issues an Challenges of Wreless Sensor Networs Localzaton n Emergng Applcatons n Proceengs of the Internatonal Conference on Computer Scence an Electroncs Engneerng Hongzhou 0 pp. 7-. []. J. Lu Q. Wang J. Wan J. Xong owars Real- me Inoor Localzaton n Wreless Sensor Networs n Proceengs of the th Internatonal Conference on Computer an Informaton echnology (CI 0 pp []. M. Chen J. Wan F. L Machne-to-Machne Communcatons: archtectures stanars an applcatons KSII ransactons on Internet an Informaton Systems Vol. 6 0 pp []. Y. Weng W. Xao L. Xe otal Least Squares Metho for Robust Source Localzaton n Sensor Networs Usng DOA Measurements Internatonal Journal of Dstrbute Sensor Networs Vol [6]. A. De Angels J. Nlsson I. Sog P. Hänel P. Carbone Inoor Postonng by Ultrawe Ban Rao Ae Inertal Navgaton Metrology an Measurement Systems Vol. XII 00 pp [7]. F. Zhu Z. We B. Hu J. Chen Z. Guo Analyss of noor postonng approaches base on actve RFID n Proceengs of the th Internatonal Conference on Wreless Communcatons Networng an Moble Computng Bejng Chna 009 pp [8]. J. Lu Q. Wang X. Chen W. Huang A Novel Wreless D Localzaton Metho Supporte by WSN Internatonal Journal of Onlne Engneerng (JOE Vol. 9 0 pp Copyrght Internatonal Frequency Sensor Assocaton (IFSA. All rghts reserve. (