Localization through Compressive Sensing: A Survey
International Journal of Wireless Communications and Mobile Computing
Volume 3, Issue 2-1, March 2015, Pages: 1-5
Received: Nov. 4, 2014; Accepted: Nov. 7, 2014; Published: Nov. 29, 2014
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Author
A. Ali, TRENDS Lab, ITU University, Lahore, Pakistan
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Abstract
User mobile device or for wireless node detection localization is a primary concern not only in normal days but especially during emergency situations. There is variety of useful and necessary applications related to localization and it is an important technology playing critical role in wireless communication. The conceptual point of view is to sense the localization (coordinates of the user) from a specific region of interest (ROI). For reducing the complexity and increasing efficiency, the data samples for location sensing is limited in a term of taking sparsity of the detected signal in known transformed domain by taking fewer data samples. This whole phenomenon is called compressive sensing. This paper introduces this technology especially in location-sensing and discusses the present techniques.
Keywords
Cognitive Radio, Localization, Mobile Networks, Wireless Networks, Sparsity, Compressive Sensing, Signal Detection
To cite this article
A. Ali, Localization through Compressive Sensing: A Survey, International Journal of Wireless Communications and Mobile Computing. Special Issue: Localization by Compressive Sensing. Vol. 3, No. 2-1, 2015, pp. 1-5. doi: 10.11648/j.wcmc.s.2015030201.11
References
[1]
J. F. Jiang, G. J. Han, C. Zhu, Y. H. Dong, N. Zhang, “Secure localization in wireless sensor networks: A survey”, Journal of Communications, vol.6, no.6, pp.460-470, 2011.
[2]
Reza Zekavat, R. Michael Buehrer, “Handbook of Position Location: Theory, Practice and Advances,” ISBN: 978-0-470-94342-7
[3]
Olga V.Holtz, “Compressive sensing: a paradigm shift in signal processing”, Dec, 2008
[4]
Richard G. Baraniuk, "More Is Less: Signal Processing and the Data Deluge",DOI: 10.1126/science.1197448 , 717 (2011); 331 Science
[5]
Chen Feng, Shahrokh Valaee1, Zhenhui Tan Department of Electrical and Computer Engineering, University of Toronto, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, "Localization of wireless sensors using compressive sensing for manifold learning," IEEE The 20th Personal, Indoor and Mobile Radio Communications Symposium, PIMRC, 2009.
[6]
C. Feng, S. Valaee, and Z. Tan, "Multiple target localization using compressive sensing," in GLOBECOM'09: Proceedings of the 28th IEEE conference on Global telecommunications, 2009, pp. 4356-4361.
[7]
J. geun Park, E. D. Demaine, and S. Teller, "Moving-baseline localization," in Proceedings of Information Processing in Sensor Networks (IPSN), 2008, pp. 15-26.
[8]
S. Nikitaki and P. Tsakalides, ldquo, “Localization in Wireless Networks via Spatial Sparsity,” Proc. Conf. Record of the 44th Asilomar Conf. Signals, Systems and Computers (ASILOMAR ',10), pp. 236-239, Nov. 2010.
[9]
C. R. Berger , Z. Wang , J. Huang and S. Zhou "Application of compressive sensing to sparse channel estimation", IEEE Commun. Mag., vol. 48, no. 11, pp.164 -174 2010
[10]
V. Cevher , M. F. Duarte and R. G. Baraniuk "Distributed target localization via spatial sparsity", 16th Eur. Signal Process. Conf., 2008.
[11]
S. Nikitaki and P. Tsakalides, "Localization in wireless networks based on jointly compressed sensing," Proc. of European Signal Proc. Conf. (EUSIPCO), pp. 1809 - 1813, Aug.-Sept. 2011.
[12]
B. Zhang, X. Cheng, N. Zhang, Y. Cui, Y. Li, and Q. Liang, "Sparse target counting and localization in sensor networks based on compressive sensing," in Proc. IEEE INFOCOM, pp. 2255-2263, 2011.
[13]
Wael Guibène and Dirk Slock, "Cooperative Spectrum Sensing and Localization in Cognitive Radio Systems Using Compressed Sensing" Hindawi Publishing Corporation, Journal of Sensors, Volume 2013, Article ID 606413, 9 pages, http://dx.doi.org/10.1155/2013/606413
[14]
W. Guibene and D. Slock, “A combined spectrum sensing and terminals localization technique for cognitive radio networks,” in Proceedings of the IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Comm’s (WiMob '12), 2012.
[15]
Sofia Nikitaki University of Crete & FORTH, Heraklion, Greece, Panagiotis Tsakalides University of Crete & FORTH, Heraklion, Greece, "Decentralized indoor wireless localization using compressed sensing of signal-strength fingerprints", PM2HW2N '12, Pages 37-44, ACM New York, NY, USA ©2012, ISBN: 978-1-4503-1626-2 doi>10.1145/2387191.2387198
[16]
Gan, Ming; Guo, Dongning; Dai, Xuchu, "Distributed Ranging and Localization for Wireless Networks via Compressed Sensing", eprint arXiv:1308.3548, Publication Date: 08/2013
[17]
Lanchao Liu, Zhu Han, Zhiqiang Wu, Lijun Qian, "Spectrum Sensing and Primary User Localization in Cognitive Radio Networks via Sparsity" , EAI Endorsed Transactions on Wireless Spectrum, Copyright © 2014, doi:10.4108/ws.1.1.e2
[18]
R. M. Vaghefi and R. M. Buehrer, "Improving positioning in LTE through collaboration," in Proc. IEEE WPNC, 2014.
[19]
Raja Jurdak, X. Rosalind Wang, Oliver Obst, and Philip Valencia, CSIRO ICT Centre, Australia, "Wireless Sensor Network Anomalies: Diagnosis, and Detection Strategies", A. Tolk and L.C. Jain (Eds.): Intelligence-Based Systems Engineering, ISRL 10, pp. 309–325.
[20]
Sheenam, Navdeep Kaur, SBSTC, Ferozepur, India, "Improvement of Energy Efficiency of Compressive Sensing in Wireless Sensor Networks", ISSN 2348-5426 International Journal of Advances in Science and Technology (IJAST) Vol 2 Issue 2 (June 2014)
[21]
W. Guibene and D. Slock, “Cooperative spectrum sensing and localization in cognitive radio systems using compressed sensing,” Journal of Sensors, vol. 2013, Article ID 606413, 9 pages, 2013.
[22]
K. Hayashi, M. Nagahara, and T. Tanaka, "A user's guide to compressed sensing for communications systems, " IEICE Trans. on Communications, vol. E96-B, no. 3, pp. 685-712, Mar. 2013.
[23]
Joseph Lardies, Hua MA, Marc Berthillier. Source localization using a sparse representation of sensor measurements. Soci´et´e Fran¸caise d’Acoustique. Acoustics 2012, Apr 2012, Nantes, France.
[24]
D.L Donoho and B. Logan, “Signal recovery and the large sieve,” SIAM J. Appl. Math., vol.52, no.2, pp.577-591, April 1992
[25]
S.G. Mallat, “A Wavelet Tour of Signal Processing”, Third ed. The Sparse Way, Academic Press, 2008.
[26]
P.Buhlmann and S. van de Geer, Statistics for High-Dimensional Data: Methods, Theory and Applications, Springer, 2011.
[27]
S.G. Mallat and Z.Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process., vol.41, no.12, pp.3397-3415, Dec. 1993
[28]
R. Tibshirani, “Regression shrinkage and selection via the lasso,” J.R. Statist. Soc. B, vol.58, no.1, pp.267-288, 1996
[29]
J.L. Starck, F.Murtagh, and J.M. Fadili, Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity, Cambridge University Press, 2010.
[30]
J. Yoo, C. Turnes, E. Nakamura, C. Le, S. Becker, E. Sovero, M. Wakin, M. Grant, J. Romberg, A. Emami-Neyestanak, and E. Cand`es, “A compressed sensing parameter extraction platform for radar pulse signal acquisition,” Submitted to IEEE J. Emerg. Sel. Topics Circuits Syst., February 2012.
[31]
W. Dai, O. Milenkovic, Subspace pursuit for compressive sensing: Closing the gap between performance and complexity, available at: http://www.dsp.ece.rice.edu/cs/SubspacePursuit.pdf (preprint)
[32]
I. F. Gorodnitsky and B. D. Rao, “Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm,” IEEE Transactions on Signal Processing, vol. 45, no. 3, pp. 600–616, 1997.
[33]
V. Cevher, A. C. Gurbuz, J. H. McClellan, and R. Chellappa, “Compressive wireless arrays for bearing estimation,” in IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, NV, Apr. 2008.
[34]
D. Malioutov, M. Cetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Transactions on Signal Processing, vol. 53, no. 8, pp. 3010–3022, 2005.
[35]
D.Model and M. Zibulevsky, “Signal reconstruction in sensor arrays using sparse representations,” Signal Processing, vol. 86, no. 3, pp. 624–638, 2006.
[36]
A. C. Gurbuz, V. Cevher, and J. H.McClellan, “A compressive beamformer,” in IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, NV, 2008.
[37]
Isaac Amundson and Xenofon D. Koutsoukos, "A Survey on Localization for Mobile Wireless Sensor Networks", R. Fuller and X.D. Koutsoukos (Eds.): MELT 2009, LNCS 5801, 2009, Pages: 235-254
[38]
Guevara, J.; Jiménez, A.R.; Prieto, J.C.; Seco, F. Error Estimation for the Linearized Auto-Localization Algorithm. Sensors 2012, 12, 2561–2581.
[39]
S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing, Applied and Numerical Harmonic Analysis, DOI 10.1007/978-0-8176-4948-7 2, © Springer Science+Business Media New York 2013
[40]
M. Ding, F. Liu, A. Thaeler, D. Chen, and X. Cheng, “Fault-tolerant target localization in sensor networks,” in EURASIP J. Wirel. Commun. Netw., vol. 2007, no. 1, 2007, pp. 19–28.
[41]
T. Clouqueur, K. K. Saluja, and P. Ramanathan, “Fault tolerance in collaborative sensor networks for target detection,” in IEEE Transactions on Computer, vol. 53, no. 3, 2004, pp. 320–333.
[42]
Marco F. Duarte, “Localization and Bearing Estimation via Structured Sparsity Models,” IEEE Statistical Signal Processing Workshop (SSP), 2012, Ann Arbor, MI, pp. 333-336.
[43]
Emamnuel J. Candès, "Compressive sampling", Applied and Computational Mathematics, California Institute of Technology, Pasadena, CA 91125, U.S.A
[44]
Lei Liu Jin-Song Chong, Xiao-Qing Wang, and Wen Hong, "Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks" International Journal of Distributed Sensor Networks, Volume 2012 (2012), Article ID 592471, 15 pages, http://dx.doi.org/10.1155/2012/592471
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