Simulation Based Comparison of Geo-Location Methods in Wireless Networks

Simulation Based Comparison of Geo-Location Methods in Wireless Networks

Автор: E. Balarastaghi, MR. Amini, A. Mirzavandi

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 2 Vol. 7, 2015 года.

Бесплатный доступ

There are many Geo-Location techniques proposed in cellular networks. They are mainly classified based on the parameters used to extract location information. In this study it is tried to have a new look to these positioning methods and to classify them differently regardless of parameters type. We classified these techniques base on mathematical algorithms which is used to derive location information of users in the network. Such algorithms are divided into three main subclasses in here, estimation theory based (MUSIC, ESPIRIT), Meta-heuristic (Genetic, PSO...) and filtering approaches (Kalman, Particle, Grid, MH ). The proofs and details of how to apply techniques are presented and the simulation results are given.

Еще

Geo-Location, Cellular Network, Kalman Filter, Particle Filter, Metropolis Hastings, Estimation

Короткий адрес: https://sciup.org/15012232

IDR: 15012232

Список литературы Simulation Based Comparison of Geo-Location Methods in Wireless Networks

  • M. Aatiqu. Evaluation of TDOA Techniques for Position Estimation. PhD, Virginia Polytechnic Institute, Virginia, USA, 1997.
  • James Caffery Jr , Gordon L. St¨ube. Subscriber location in CDMA cellular networks. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 1998; 47: 406 - 416 .
  • Fuller Richard, Koutsoukos Xenofon D. Mobile Entity Localization and Tracking in GPS-less Environments. In : Springer 2009 Second International Workshop ; 30 September-2 October 2009; Orlando, FL, USA;3: pp.267-273.
  • Nayef Ali Alsindi. Performance of TOA Estimation Algorithms in Different Indoor Multipath Conditions. MSc, Worcester Polytechnic Institute, Worcester, Massachusetts, USA, 2004.
  • Geoffrey G.Messier. IS-95 Cellular Mobile Location Techniques. MSc, University of CALGARY, Calgary, Alberta, Canada, 1998.
  • João Figueiras, Simone Frattasi. Mobile Positioning and Tracking From Conventional to cooperative Techniques. 1st ed. New York, NY, USA: Wiley, 2010.
  • Yaakov Bar-Shalom, X.-Rong Li, Thiagalingam Kirubarajan. Estimation with Applications To Tracking and Navigation. 1st ed .New York, USA: Wiley, 2001 .
  • SHA TAO. Mobile Phone‐based Vehicle Positioning and Tracking and Its Application in Urban Traffic State Estimation. MSc, KTH Royal Institute of Technology ,Stockholm, Sweden 2012.
  • Taga, F. Smart MUSIC algorithm for DOA estimation. IEEE Electronics Letters 1997 ; 33: 190-201.
  • Jing, Xiong . An improved fast Root-MUSIC algorithm for DOA estimation. In : IEEE 2012 Image Analysis and Signal Processing Conference (IASP); 9-11 Nov. 2012; Hangzhou, China; IEEE. pp. 1 - 3 .
  • Zahernia, A. MUSIC algorithm for DOA estimation using MIMO arrays. In: IEEE, 2011 Telecommunication Systems, Services, and Applications Conference (TSSA); 20-21 Oct. 2011 ; Bali, Indonesia ; IEEE. pp. 149 - 153 .
  • Liyang Zhou. A modified ESPRIT algorithm based on a new SVD method for coherent signals. In : IEEE 2011 Information and Automation Conference (ICIA); 6-8 June 2011; Shenzhen ,China; IEEE. pp. 75 - 78 .
  • Badeau, R. Fast adaptive esprit algorithm. In: IEEE 2005 Statistical Signal Processing workshop; 17-20 July 2005; Novosibirsk, Russia ;IEEE. pp. 289 - 294.
  • Hui Jiang . A modified ESPRIT algorithm for signal DOA estimation. In :IET 2011 Communication Technology and Application Conference (ICCTA ); 14-16 Oct. 2011, Beijing , China; IEEE. pp. 140 - 144.
  • Kumar, A. Computational Intelligence based algorithm for node localization in Wireless Sensor Networks .In: IEEE 2012 Intelligent Systems Conference (IS); 3-5 Nov. 2012 , Sofia , Bulgaria ; IEEE. pp. 431 - 438 .
  • Kumar, A. Meta-heuristic range based node localization algorithm for Wireless Sensor Networks. In : IEEE 2012 Localization and GNSS Conference (ICL-GNSS); 25-27 June 2012; Starnberg , Germany; IEEE. pp. 1 - 7.
  • Chagas, S.H. Genetic Algorithms and Simulated Annealing optimization methods in wireless sensor networks localization using artificial neural networks. In :IEEE 2012 Circuits and Systems Midwest Symposium (MWSCAS); 5-8 Aug. 2012 ;Boise, Idaho, USA; IEE. pp. 928 - 931 .
  • Punviset, R. An optimum Markov random field-based localization algorithm wireless sensor networks. In: IEEE 2012 Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology Conference (ECTI-CON); 16-18 May 2012 ; Phetchaburi , Thailand; IEEE. pp. 1 - 4 .
  • Noradin Ghadimi, Aref Jalili, Rasoul Ghadimi, Optimum Allocation of Distributed Generation Based on Multi Objective Function with Genetic Algorithm . J. Basic Appl. Sci. Res. 2012 ;2: 185-191.
  • Fateme Masoudinia, A New Approach of Multi-Objective Optimization in Controlling Process Using Genetic Algorithm. J. Basic Appl. Sci. Res. 2012 ;2:777-787.
  • Kaipio J. , Somersalo E. Statistical and Computational Inverse Problems (Applied Mathematical Sciences).2005 ed. New York, NY, USA: Springer , 2004.
  • Zoran Salcic, Edwin Chan. Mobile Station Positioning Using GSM Cellular Phone and Artificial Neural Networks. Wireless Personal Communications 2000;14 : 235-254.
  • Dieter Fox , Jeffrey Hightower , Lin Liao , Dirk Schulz , Gaetano Borriello, Jeffrey Hightower. Bayesian Filters for Location Estimation. IEEE journal of Pervasive Computing 2003; 2 : 24 - 33.
  • Panlong WU, Jianshou KONG, Yuming BO. Modified iterated extended Kalman particle filter for single satellite passive tracking. Turk. J. Elec. Eng. & Comp. Sci 2013; 2: 120-130.
  • İlke TÜRKMEN, Kerim GÜNEY. Computation of Association Probabilities for Single Target Tracking with the Use of Adaptive Neuro-Fuzzy Inference System. Turk. J. Elec. Eng. & Comp. Sci 2005 ;13: 105-118.
  • Ruşen ÖKTEM, Elif AYDIN. An RFID based indoor tracking method for navigating visually impaired people. Turk. J. Elec. Eng. & Comp. Sci 2010; 18: 185-198.
  • MAHESH RAMAMURTHY. INDOOR GEO-LOCATION AND TRACKING OF MOBILE AUTONOMOUS ROBOT. MSc, B.E. University B.D.T. College of Engineering, Davangere, India ,2001.
  • Zainab R. Zaidi, Brian L. Mark. Mobility Tracking Based on Autoregressive Models. IEEE Trans. Mobile Computing 2011; 10:32-43.
  • L. Mihaylova, D. Angelova, S. Honary, D. R. Mobility Tracking in Cellular Networks Using Particle Filtering. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2007; 6: 3589 - 3599.
  • Bshara, M. Robust Tracking in Cellular Networks Using HMM Filters and Cell-ID Measurements. IEEE Transactions on Vehicular Technology 2011;60:1016 - 102
  • Lin, D.-B. 2005. Mobile Location Estimation Based on Differences of Signal Attenuations for GSM Systems. IEEE Transactions on Vehicular Technology, 2005; 54 :1447 - 1454 .
  • Allam Mousa, Yousef Dama,Mahmoud Najjar, Bashar Alsayeh. Optimizing Outdoor Propagation Model based on Measurements for Multiple RF Cell. International Journal of Computer Applications 2012;60 : 5-10 .
  • M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon, Tim ClappA. Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE TRANSACTIONS ON SIGNAL PROCESSING 2002;50: 174-188.
  • Henri Nurminen. Position estimation using RSS measurements with unknown measurement model parameters. MSc, TAMPERE UNIVERSITY OF TECHNOLOGY, Hervanta , Finland ,2012.
  • Yuehu Liu , Hao Yu ; Bin Chen ; Yubin Xu ; Zhihui Li ; Yu Fang. Improving Monte Carlo Localization algorithm using genetic algorithm in mobile WSNs. In : IEEE 2012 Geoinformatics Coference (GEOINFORMATICS); 15-17 June 2012 ;Hong Kong; IEEE. pp. 1 - 5 .
  • Christian P. Robert , George Casella . Introducing Monte Carlo Methods with R. 2010 ed. New York, NY.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp