Deep Neural Networks for wireless localization in indoor and outdoor environments
In this paper, we propose a wireless positioning method based on Deep Learning. To deal with the variant and unpredictable wireless signals, the positioning is casted in a four-layer Deep Neural Network (DNN) structure pre-trained by Stacked Denoising Autoencoder (SDA) that is capable of learning re...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 194; s. 279 - 287 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
19.06.2016
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | In this paper, we propose a wireless positioning method based on Deep Learning. To deal with the variant and unpredictable wireless signals, the positioning is casted in a four-layer Deep Neural Network (DNN) structure pre-trained by Stacked Denoising Autoencoder (SDA) that is capable of learning reliable features from a large set of noisy samples and avoids hand-engineering. Also, to maintain the temporal coherence, a Hidden Markov Model (HMM)-based fine localizer is introduced to smooth the initial positioning estimate obtained by the DNN-based coarse localizer. The data required for the experiments is collected from the real world in different periods to meet the actual environment. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy in coping with the turbulent wireless signals. |
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| AbstractList | In this paper, we propose a wireless positioning method based on Deep Learning. To deal with the variant and unpredictable wireless signals, the positioning is casted in a four-layer Deep Neural Network (DNN) structure pre-trained by Stacked Denoising Autoencoder (SDA) that is capable of learning reliable features from a large set of noisy samples and avoids hand-engineering. Also, to maintain the temporal coherence, a Hidden Markov Model (HMM)-based fine localizer is introduced to smooth the initial positioning estimate obtained by the DNN-based coarse localizer. The data required for the experiments is collected from the real world in different periods to meet the actual environment. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy in coping with the turbulent wireless signals. |
| Author | Gu, Jason Zhang, Weidong Zhang, Youmei Zhang, Wei Liu, Kan |
| Author_xml | – sequence: 1 givenname: Wei surname: Zhang fullname: Zhang, Wei organization: School of Control Science and Engineering, Shandong University, China – sequence: 2 givenname: Kan surname: Liu fullname: Liu, Kan email: sakuraxiafan@gmail.com organization: School of Control Science and Engineering, Shandong University, China – sequence: 3 givenname: Weidong surname: Zhang fullname: Zhang, Weidong organization: School of Control Science and Engineering, Shandong University, China – sequence: 4 givenname: Youmei surname: Zhang fullname: Zhang, Youmei organization: School of Control Science and Engineering, Shandong University, China – sequence: 5 givenname: Jason surname: Gu fullname: Gu, Jason organization: Department of Electrical and Computer Engineering, Dalhousie University, Canada |
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| Cites_doi | 10.1109/PLANS.2008.4569985 10.1109/MPRV.2003.1228524 10.1016/j.neucom.2015.03.099 10.1109/ICInfA.2014.6932657 10.1109/TASL.2011.2109382 10.1109/ACSSC.2010.5757507 10.1109/CAMSAP.2009.5413285 10.1109/TASL.2011.2134090 10.1109/ICIEA.2008.4582530 10.1145/2348543.2348578 10.1016/j.neucom.2012.10.011 10.1145/1164783.1164806 10.1109/ICNSC.2004.1297088 10.1109/TCE.2010.5681092 10.1109/WCNC.2013.6554922 10.1109/TCE.2007.381737 10.1162/neco.2006.18.7.1527 10.1007/978-3-540-30119-6_6 10.1109/INFCOM.2000.832252 10.1016/j.patcog.2015.04.012 10.1109/IROS.2008.4651203 10.1016/j.neucom.2013.07.032 10.1145/1390156.1390294 10.1109/ICInfA.2014.6932827 10.1126/science.290.5500.2323 10.1016/j.neucom.2015.05.120 10.1109/TCE.2011.6018861 10.1002/rob.20276 10.1109/TCE.2010.5606338 10.1109/TMC.2011.216 |
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| References | Cheng, Wu, Zhang (bib2) 2011; 57 Vincent, Larochelle, Lajoie, Bengio, Manzagol (bib36) 2010; 11 Z. Yang, C. Wu, Y. Liu, Locating in fingerprint space: wireless indoor localization with little human intervention, in: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, ACM, Istanbul, Turkey, 2012, pp. 269–280. W. Zhang, K. Liu, W. Zhang, Y. Zhang, J.-F. Gu, Wi-fi positioning based on deep learning, in: 2014 IEEE International Conference on Information and Automation (ICIA), IEEE, Hailar, China, 2014, pp. 1176–1179. Li, Li, Ge (bib12) 2013; 104 Dahl, Yu, Deng, Acero (bib21) 2012; 20 Feng, Au, Valaee, Tan (bib9) 2012; 11 Fox, Hightower, Liao, Schulz, Borriello (bib6) 2003; 3 Chen, Wang (bib24) 2014; 123 Lim, Wan, Ng, See (bib28) 2007; 53 Hinton, Osindero, Teh (bib34) 2006; 18 S. Nikitaki, P. Tsakalides, Localization in wireless networks via spatial sparsity, in: 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), IEEE, Pacific Grove, CA, 2010, pp. 236–239. M. Ciurana, F. Barceló, S. Cugno, Indoor tracking in wlan location with toa measurements, in: Proceedings of the 4th ACM International Workshop on Mobility Management and Wireless Access, ACM, Torremolinos, Spain, 2006, pp. 121–125. P. Sermanet, R. Hadsell, M. Scoffier, U. Muller, Y. LeCun, Mapping and planning under uncertainty in mobile robots with long-range perception, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, IROS 2008, IEEE, Nice, France, 2008, pp. 2525–2530. L. Deng, Three classes of deep learning architectures and their applications: a tutorial survey, APSIPA Trans. Signal Inf. Process 2012 H. Lee, P. Pham, Y. Largman, A.Y. Ng, Unsupervised feature learning for audio classification using convolutional deep belief networks, in: Advances in Neural Information Processing Systems, 2009, pp. 1096–1104. C.-L. Wu, L.-C. Fu, F.-L. Lian, Wlan location determination in e-home via support vector classification, in: 2004 IEEE International Conference on Networking, Sensing and Control, vol. 2, IEEE, 2004, pp. 1026–1031. De Sá, Nedjah, De Macedo Mourelle (bib13) 2016; 172 Zhang, Zhang, Ma, Guan, Gong (bib18) 2015; 48 Chang, Rashidzadeh, Ahmadi (bib26) 2010; 56 Zhang, Yu (bib27) 2010; 56 K. Derr, M. Manic, Wireless based object tracking based on neural networks, in: 3rd IEEE Conference on Industrial Electronics and Applications, 2008, ICIEA 2008, IEEE, Singapore, 2008, pp. 308–313. P. Bahl, V.N. Padmanabhan, Radar: An in-building rf-based user location and tracking system, in: INFOCOM 2000, Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, IEEE, vol. 2, IEEE, Tel Aviv, 2000, pp. 775–784. J. Hightower, G. Borriello, Particle filters for location estimation in ubiquitous computing: a case study, in: UbiComp 2004: Ubiquitous Computing, Springer, 2004, pp. 88–106. Q.V. Le, Building high-level features using large scale unsupervised learning, in: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Vancouver, BC, 2013, pp. 8595–8598. A.S. Paul, E.A. Wan, Wi-fi based indoor localization and tracking using sigma-point Kalman filtering methods, in: Position, Location and Navigation Symposium, 2008 IEEE/ION, IEEE, Monterey, CA, 2008, pp. 646–659. S.J. Pan, V.W. Zheng, Q. Yang, D.H. Hu, Transfer learning for wifi-based indoor localization, in: Proceedings of Workshop on Transfer Learning for Complex Task of the 23rd Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence, 2008. R. Battiti, A. Villani, T. Le Nhat, Neural network models for intelligent networks: deriving the location from signal patterns, Proc. AINS 2002, UCLA, 2002. H. Zhang, F. Zhou, W. Zhang, X. Yuan, Z. Chen, Real-time action recognition based on a modified deep belief network model, in: 2014 IEEE International Conference on Information and Automation (ICIA), IEEE, Hailar, China, 2014, pp. 225–228. Yang, Zhang, Chen (bib25) 2016; 174 Mohamed, Dahl, Hinton (bib19) 2012; 20 . Hadsell, Sermanet, Ben, Erkan, Scoffier, Kavukcuoglu, Muller, LeCun (bib23) 2009; 26 G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, arXiv preprint G. Ding, Z. Tan, J. Zhang, L. Zhang, Fingerprinting localization based on affinity propagation clustering and artificial neural networks, in: 2013 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Shanghai, China, 2013, pp. 2317–2322. Roweis, Saul (bib38) 2000; 290 I. Guvenc, C.T. Abdallah, R. Jordan, O. Dedeoglu, Enhancements to rss based indoor tracking systems using Kalman filters, in: GSPx & International Signal Processing Conference, 2003, pp. 91–102. C. Feng, W. S.A. Au, S. Valaee, Z. Tan, Orientation-aware indoor localization using affinity propagation and compressive sensing, in: 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), IEEE, Aruba, Dutch Antilles, 2009, pp. 261–264. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105. P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International Conference on Machine Learning, ACM, 2008, pp. 1096–1103. Chang (10.1016/j.neucom.2016.02.055_bib26) 2010; 56 De Sá (10.1016/j.neucom.2016.02.055_bib13) 2016; 172 Zhang (10.1016/j.neucom.2016.02.055_bib18) 2015; 48 10.1016/j.neucom.2016.02.055_bib20 Feng (10.1016/j.neucom.2016.02.055_bib9) 2012; 11 Zhang (10.1016/j.neucom.2016.02.055_bib27) 2010; 56 Roweis (10.1016/j.neucom.2016.02.055_bib38) 2000; 290 Li (10.1016/j.neucom.2016.02.055_bib12) 2013; 104 10.1016/j.neucom.2016.02.055_bib17 10.1016/j.neucom.2016.02.055_bib39 10.1016/j.neucom.2016.02.055_bib15 10.1016/j.neucom.2016.02.055_bib37 10.1016/j.neucom.2016.02.055_bib16 10.1016/j.neucom.2016.02.055_bib35 10.1016/j.neucom.2016.02.055_bib14 10.1016/j.neucom.2016.02.055_bib11 10.1016/j.neucom.2016.02.055_bib33 Hadsell (10.1016/j.neucom.2016.02.055_bib23) 2009; 26 10.1016/j.neucom.2016.02.055_bib7 10.1016/j.neucom.2016.02.055_bib4 10.1016/j.neucom.2016.02.055_bib5 Vincent (10.1016/j.neucom.2016.02.055_bib36) 2010; 11 10.1016/j.neucom.2016.02.055_bib3 Chen (10.1016/j.neucom.2016.02.055_bib24) 2014; 123 Lim (10.1016/j.neucom.2016.02.055_bib28) 2007; 53 10.1016/j.neucom.2016.02.055_bib1 10.1016/j.neucom.2016.02.055_bib31 10.1016/j.neucom.2016.02.055_bib10 10.1016/j.neucom.2016.02.055_bib32 10.1016/j.neucom.2016.02.055_bib30 10.1016/j.neucom.2016.02.055_bib8 Hinton (10.1016/j.neucom.2016.02.055_bib34) 2006; 18 10.1016/j.neucom.2016.02.055_bib29 Yang (10.1016/j.neucom.2016.02.055_bib25) 2016; 174 Fox (10.1016/j.neucom.2016.02.055_bib6) 2003; 3 Cheng (10.1016/j.neucom.2016.02.055_bib2) 2011; 57 10.1016/j.neucom.2016.02.055_bib22 Mohamed (10.1016/j.neucom.2016.02.055_bib19) 2012; 20 Dahl (10.1016/j.neucom.2016.02.055_bib21) 2012; 20 |
| References_xml | – reference: A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105. – reference: G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, arXiv preprint – volume: 57 start-page: 1099 year: 2011 end-page: 1104 ident: bib2 article-title: Indoor robot localization based on wireless sensor networks publication-title: IEEE Trans. Consum. Electron. – volume: 26 start-page: 120 year: 2009 end-page: 144 ident: bib23 article-title: Learning long-range vision for autonomous off-road driving publication-title: J. Field Robot. – reference: C.-L. Wu, L.-C. Fu, F.-L. Lian, Wlan location determination in e-home via support vector classification, in: 2004 IEEE International Conference on Networking, Sensing and Control, vol. 2, IEEE, 2004, pp. 1026–1031. – volume: 18 start-page: 1527 year: 2006 end-page: 1554 ident: bib34 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. – volume: 290 start-page: 2323 year: 2000 end-page: 2326 ident: bib38 article-title: Nonlinear dimensionality reduction by locally linear embedding publication-title: Science – volume: 53 start-page: 618 year: 2007 end-page: 622 ident: bib28 article-title: A real-time indoor wifi localization system utilizing smart antennas publication-title: IEEE Trans. Consum. Electron. – volume: 11 start-page: 1983 year: 2012 end-page: 1993 ident: bib9 article-title: Received-signal-strength-based indoor positioning using compressive sensing publication-title: IEEE Trans. Mob. Comput. – reference: S.J. Pan, V.W. Zheng, Q. Yang, D.H. Hu, Transfer learning for wifi-based indoor localization, in: Proceedings of Workshop on Transfer Learning for Complex Task of the 23rd Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence, 2008. – volume: 20 start-page: 14 year: 2012 end-page: 22 ident: bib19 article-title: Acoustic modeling using deep belief networks publication-title: IEEE Trans. Audio Speech Lang. Process. – volume: 11 start-page: 3371 year: 2010 end-page: 3408 ident: bib36 article-title: Stacked denoising autoencoders publication-title: J. Mach. Learn. Res. – volume: 48 start-page: 3191 year: 2015 end-page: 3202 ident: bib18 article-title: Multimodal learning for facial expression recognition publication-title: Pattern Recognit. – volume: 56 start-page: 2208 year: 2010 end-page: 2216 ident: bib27 article-title: Lswd publication-title: IEEE Trans. Consum. Electron. – volume: 56 start-page: 1860 year: 2010 end-page: 1867 ident: bib26 article-title: Robust indoor positioning using differential wi-fi access points publication-title: IEEE Trans. Consum. Electron. – volume: 3 start-page: 24 year: 2003 end-page: 33 ident: bib6 article-title: Bayesian filtering for location estimation publication-title: IEEE Pervasive Comput. – reference: Q.V. Le, Building high-level features using large scale unsupervised learning, in: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Vancouver, BC, 2013, pp. 8595–8598. – reference: S. Nikitaki, P. Tsakalides, Localization in wireless networks via spatial sparsity, in: 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), IEEE, Pacific Grove, CA, 2010, pp. 236–239. – reference: L. Deng, Three classes of deep learning architectures and their applications: a tutorial survey, APSIPA Trans. Signal Inf. Process 2012, – reference: R. Battiti, A. Villani, T. Le Nhat, Neural network models for intelligent networks: deriving the location from signal patterns, Proc. AINS 2002, UCLA, 2002. – reference: P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International Conference on Machine Learning, ACM, 2008, pp. 1096–1103. – volume: 172 start-page: 322 year: 2016 end-page: 336 ident: bib13 article-title: Distributed efficient localization in swarm robotic systems using swarm intelligence algorithms publication-title: Neurocomputing – volume: 174 start-page: 121 year: 2016 end-page: 133 ident: bib25 article-title: Rfid-enabled indoor positioning method for a real-time manufacturing execution system using os-elm publication-title: Neurocomputing – reference: G. Ding, Z. Tan, J. Zhang, L. Zhang, Fingerprinting localization based on affinity propagation clustering and artificial neural networks, in: 2013 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Shanghai, China, 2013, pp. 2317–2322. – reference: P. Bahl, V.N. Padmanabhan, Radar: An in-building rf-based user location and tracking system, in: INFOCOM 2000, Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, IEEE, vol. 2, IEEE, Tel Aviv, 2000, pp. 775–784. – reference: J. Hightower, G. Borriello, Particle filters for location estimation in ubiquitous computing: a case study, in: UbiComp 2004: Ubiquitous Computing, Springer, 2004, pp. 88–106. – reference: K. Derr, M. Manic, Wireless based object tracking based on neural networks, in: 3rd IEEE Conference on Industrial Electronics and Applications, 2008, ICIEA 2008, IEEE, Singapore, 2008, pp. 308–313. – reference: M. Ciurana, F. Barceló, S. Cugno, Indoor tracking in wlan location with toa measurements, in: Proceedings of the 4th ACM International Workshop on Mobility Management and Wireless Access, ACM, Torremolinos, Spain, 2006, pp. 121–125. – volume: 104 start-page: 170 year: 2013 end-page: 179 ident: bib12 article-title: A biologically inspired solution to simultaneous localization and consistent mapping in dynamic environments publication-title: Neurocomputing – volume: 20 start-page: 30 year: 2012 end-page: 42 ident: bib21 article-title: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition publication-title: IEEE Trans. Audio Speech Lang. Process. – reference: . – reference: H. Zhang, F. Zhou, W. Zhang, X. Yuan, Z. Chen, Real-time action recognition based on a modified deep belief network model, in: 2014 IEEE International Conference on Information and Automation (ICIA), IEEE, Hailar, China, 2014, pp. 225–228. – reference: Z. Yang, C. Wu, Y. Liu, Locating in fingerprint space: wireless indoor localization with little human intervention, in: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, ACM, Istanbul, Turkey, 2012, pp. 269–280. – reference: H. Lee, P. Pham, Y. Largman, A.Y. Ng, Unsupervised feature learning for audio classification using convolutional deep belief networks, in: Advances in Neural Information Processing Systems, 2009, pp. 1096–1104. – reference: C. Feng, W. S.A. Au, S. Valaee, Z. Tan, Orientation-aware indoor localization using affinity propagation and compressive sensing, in: 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), IEEE, Aruba, Dutch Antilles, 2009, pp. 261–264. – reference: A.S. Paul, E.A. Wan, Wi-fi based indoor localization and tracking using sigma-point Kalman filtering methods, in: Position, Location and Navigation Symposium, 2008 IEEE/ION, IEEE, Monterey, CA, 2008, pp. 646–659. – reference: W. Zhang, K. Liu, W. Zhang, Y. Zhang, J.-F. Gu, Wi-fi positioning based on deep learning, in: 2014 IEEE International Conference on Information and Automation (ICIA), IEEE, Hailar, China, 2014, pp. 1176–1179. – reference: P. Sermanet, R. Hadsell, M. Scoffier, U. Muller, Y. LeCun, Mapping and planning under uncertainty in mobile robots with long-range perception, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, IROS 2008, IEEE, Nice, France, 2008, pp. 2525–2530. – volume: 123 start-page: 354 year: 2014 end-page: 361 ident: bib24 article-title: Modeling rfid signal distribution based on neural network combined with continuous ant colony optimization publication-title: Neurocomputing – reference: I. Guvenc, C.T. Abdallah, R. Jordan, O. Dedeoglu, Enhancements to rss based indoor tracking systems using Kalman filters, in: GSPx & International Signal Processing Conference, 2003, pp. 91–102. – ident: 10.1016/j.neucom.2016.02.055_bib8 doi: 10.1109/PLANS.2008.4569985 – volume: 3 start-page: 24 year: 2003 ident: 10.1016/j.neucom.2016.02.055_bib6 article-title: Bayesian filtering for location estimation publication-title: IEEE Pervasive Comput. doi: 10.1109/MPRV.2003.1228524 – ident: 10.1016/j.neucom.2016.02.055_bib37 – volume: 172 start-page: 322 year: 2016 ident: 10.1016/j.neucom.2016.02.055_bib13 article-title: Distributed efficient localization in swarm robotic systems using swarm intelligence algorithms publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.03.099 – ident: 10.1016/j.neucom.2016.02.055_bib17 doi: 10.1109/ICInfA.2014.6932657 – ident: 10.1016/j.neucom.2016.02.055_bib16 – ident: 10.1016/j.neucom.2016.02.055_bib14 – ident: 10.1016/j.neucom.2016.02.055_bib5 – volume: 20 start-page: 14 issue: 1 year: 2012 ident: 10.1016/j.neucom.2016.02.055_bib19 article-title: Acoustic modeling using deep belief networks publication-title: IEEE Trans. Audio Speech Lang. Process. doi: 10.1109/TASL.2011.2109382 – ident: 10.1016/j.neucom.2016.02.055_bib10 doi: 10.1109/ACSSC.2010.5757507 – ident: 10.1016/j.neucom.2016.02.055_bib29 doi: 10.1109/CAMSAP.2009.5413285 – volume: 20 start-page: 30 issue: 1 year: 2012 ident: 10.1016/j.neucom.2016.02.055_bib21 article-title: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition publication-title: IEEE Trans. Audio Speech Lang. Process. doi: 10.1109/TASL.2011.2134090 – ident: 10.1016/j.neucom.2016.02.055_bib31 doi: 10.1109/ICIEA.2008.4582530 – ident: 10.1016/j.neucom.2016.02.055_bib20 – ident: 10.1016/j.neucom.2016.02.055_bib33 doi: 10.1145/2348543.2348578 – volume: 104 start-page: 170 year: 2013 ident: 10.1016/j.neucom.2016.02.055_bib12 article-title: A biologically inspired solution to simultaneous localization and consistent mapping in dynamic environments publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.10.011 – ident: 10.1016/j.neucom.2016.02.055_bib1 doi: 10.1145/1164783.1164806 – ident: 10.1016/j.neucom.2016.02.055_bib4 doi: 10.1109/ICNSC.2004.1297088 – volume: 56 start-page: 2208 issue: 4 year: 2010 ident: 10.1016/j.neucom.2016.02.055_bib27 article-title: Lswd publication-title: IEEE Trans. Consum. Electron. doi: 10.1109/TCE.2010.5681092 – ident: 10.1016/j.neucom.2016.02.055_bib32 doi: 10.1109/WCNC.2013.6554922 – volume: 53 start-page: 618 issue: 2 year: 2007 ident: 10.1016/j.neucom.2016.02.055_bib28 article-title: A real-time indoor wifi localization system utilizing smart antennas publication-title: IEEE Trans. Consum. Electron. doi: 10.1109/TCE.2007.381737 – volume: 18 start-page: 1527 issue: 7 year: 2006 ident: 10.1016/j.neucom.2016.02.055_bib34 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. doi: 10.1162/neco.2006.18.7.1527 – ident: 10.1016/j.neucom.2016.02.055_bib15 – ident: 10.1016/j.neucom.2016.02.055_bib7 doi: 10.1007/978-3-540-30119-6_6 – ident: 10.1016/j.neucom.2016.02.055_bib3 doi: 10.1109/INFCOM.2000.832252 – ident: 10.1016/j.neucom.2016.02.055_bib11 – volume: 48 start-page: 3191 issue: 10 year: 2015 ident: 10.1016/j.neucom.2016.02.055_bib18 article-title: Multimodal learning for facial expression recognition publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2015.04.012 – ident: 10.1016/j.neucom.2016.02.055_bib22 doi: 10.1109/IROS.2008.4651203 – volume: 123 start-page: 354 year: 2014 ident: 10.1016/j.neucom.2016.02.055_bib24 article-title: Modeling rfid signal distribution based on neural network combined with continuous ant colony optimization publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.07.032 – ident: 10.1016/j.neucom.2016.02.055_bib35 doi: 10.1145/1390156.1390294 – volume: 11 start-page: 3371 year: 2010 ident: 10.1016/j.neucom.2016.02.055_bib36 article-title: Stacked denoising autoencoders publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.neucom.2016.02.055_bib39 doi: 10.1109/ICInfA.2014.6932827 – volume: 290 start-page: 2323 issue: 5500 year: 2000 ident: 10.1016/j.neucom.2016.02.055_bib38 article-title: Nonlinear dimensionality reduction by locally linear embedding publication-title: Science doi: 10.1126/science.290.5500.2323 – volume: 174 start-page: 121 year: 2016 ident: 10.1016/j.neucom.2016.02.055_bib25 article-title: Rfid-enabled indoor positioning method for a real-time manufacturing execution system using os-elm publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.05.120 – volume: 57 start-page: 1099 issue: 3 year: 2011 ident: 10.1016/j.neucom.2016.02.055_bib2 article-title: Indoor robot localization based on wireless sensor networks publication-title: IEEE Trans. Consum. Electron. doi: 10.1109/TCE.2011.6018861 – ident: 10.1016/j.neucom.2016.02.055_bib30 – volume: 26 start-page: 120 issue: 2 year: 2009 ident: 10.1016/j.neucom.2016.02.055_bib23 article-title: Learning long-range vision for autonomous off-road driving publication-title: J. Field Robot. doi: 10.1002/rob.20276 – volume: 56 start-page: 1860 issue: 3 year: 2010 ident: 10.1016/j.neucom.2016.02.055_bib26 article-title: Robust indoor positioning using differential wi-fi access points publication-title: IEEE Trans. Consum. Electron. doi: 10.1109/TCE.2010.5606338 – volume: 11 start-page: 1983 issue: 12 year: 2012 ident: 10.1016/j.neucom.2016.02.055_bib9 article-title: Received-signal-strength-based indoor positioning using compressive sensing publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2011.216 |
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| Title | Deep Neural Networks for wireless localization in indoor and outdoor environments |
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