SenSys-RIPS.pdfIntroductionInterferometric positioningSources of errorImplementationCC1000 characteristicsTime synchronizationTuningFrequency and phase estimationSchedulingRange calculationLocalizationEvaluationEffective rangeExperimental setupFrequency and phase accuracyRanging accuracyLocalization accuracyLatencyConclusions and Future WorkAcknowledgmentREFERENCES -9ptInstitute for Software Integrated Systems Vanderbilt University Nashville Tennessee 37235 TECHNICAL REPORT TR #: ISIS-05-602 Title: Radio Interferometric Positioning Authors:, Miklos Maroti, Branislav Kusy, Gyorgy Balogh, Peter Volgyesi, Karoly Molnar, Andras Nadas, Sebestyen Dora, Akos LedecziRadio Interferometric GeolocationMikl´os Mar´oti∗P´eter V¨olgyesi†Sebesty´en D´ora†Branislav Kus´y†Andr´as N´adas†´Akos L´edeczi†Gy¨orgy Balogh†K´aroly Moln´ar†{miklos.maroti, branislav.kusy, akos.ledeczi}@vanderbilt.eduABSTRACTWe present a novel radio interference based sensor local-ization metho d for wireless sensor networks. The techniquerelies on a pair of nodes emitting radio waves simultaneouslyat slightly different frequencies. The carrier frequency of thecomp osite signal is between the two frequencies, but has avery low frequency envelope. Neighboring nodes can mea-sure the energy of the envelope signal as the signal strength.The relative phase offset of this s ignal measured at two re-ceivers is a function of the distances between the four nodesinvolved and the carrier frequency. By making multiple mea-surements in an at least 8-node network, it is possible toreconstruct the relative location of the nodes in 3D. Ourprototype implementation on the MICA2 platform yieldsan average localization error as small as 3 cm and a range ofup to 160 meters. In addition to this high precision and longrange, the other main advantage of the Radio Interferomet-ric Positioning System (RIPS) is the fact that it does notrequire any sensors other than the radio used for wirelesscommunication.Categories and Subject DescriptorsC.2.4 [Computer-Communications Networks]: Distrib-uted Systems; C.3 [Special-Purpose and Application-Based Systems]: Real-time and Embedded Systems; J.2[Physical Sciences and Engineering]: EngineeringGeneral TermsAlgorithms, Design, Experimentation, Measurement, The-ory∗Department of Mathematics, Vanderbilt University, 1326Stevenson Center, Nashville, TN 37240, USA†Institute for Software Integrated Systems, Vanderbilt Uni-versity, 2015 Terrace Place, Nashville, TN 37203, USAPermission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.SenSys’05, November 2–4, 2005, San Diego, California, USA.Copyright 2005 ACM 1-59593-054-X/05/0011 ...$5.00.KeywordsSensor Networks, Localization, Location-Awareness, RadioInterferometry, Ranging1. INTRODUCTIONMany applications of wireless sensor networks (WSN) re-quire the knowledge of where the individual nodes are lo-cated [1, 2, 3]. Yet robust sensor localization is still an openproblem today. While there are many approaches in exis-tence, they all have significant weaknesses that limit theirapplicability to real world problems. Techniques based onaccurate—typically acoustic—ranging have limited range [4,5, 6]. They need an actuator/detector pair that adds to thecost and size of the platform. Furthermore, a considerablenumber of applications require stealthy operation makingultrasound the only acoustic option. However, ultrasonicmetho ds have even more limited range and directionalityconstraints [7, 8]. Methods utilizing the radio usually relyon the received signal strength that is relatively accuratein short ranges with extensive calibration, but imprecise be-yond a few meters [8, 9, 10]. The simplest of methods deducerough location information from the message hop count [11].In effect, they also use the radio signal strength, but theyquantize it to a single bit. Finally, many of the proposedmetho ds work in 2D only. For a recent summary of local-ization methods and their performance refer to [8].In summary, existing WSN localization methods todayhave either adequate accuracy or acceptable range, but notboth at the same time. Furthermore, the very physical phe-nomenon they use—acoustics and radio signal strength—donot show any promise of achieving the significant improve-ment that is necessary to move beyond the current stateof the art. Our novel method, on the other hand, uses ra-dio interferometry and attains high accuracy and long rangesimultaneously.Traditional radio interferometry has many applications inphysics, geodesy and astronomy. The method is based ontwo directional antennas measuring the radio signal from asingle source and performing cross correlation. The res ul-tant interference signal can be further analyzed to createradio images of distant celestial objects, determine the rel-ative location of two receivers very precisely, or conversely,determine the location of a radio source when the locationof the two receivers are known. A radio interferometer isan expensive device requiring tunable directional antennas,very high sampling rates and high-precision time synchro-nization. Hence, it is not directly applicable to WSNs.The novel idea behind the prop osed Radio InterferometricPositioning System (RIPS) is to utilize two transmitters tocreate the interference signal directly. If the frequencies ofthe two emitters are almost the same then the composite sig-nal will have a low frequency envelope that can be measuredby cheap and simple hardware readily available on a WSNno de . Trying to use this signal to deduce information on thepositions of the two transmitters and the receiver directlywould require tight synchronization of the nodes involvedmandating hardware support. Instead, we use the relativephase offset of the signal at two receivers which is a func-tion of the relative positions of the four nodes involved andthe carrier frequency. By making multiple measurements inan at least 8-node network, it is possible to reconstruct therelative location of the nodes in 3D.The key attribute of this method is that the phase offsetof a low frequency signal is measured, yet it