The Sectored Antenna Array Indoor Positioning System with Neural Networks

Chih-Yung Chen; Yu-Ju Chen; Ya-Chen Weng; Shen-Whan Chen; Rey-Chue Hwang

doi:doi:10.11648/j.acis.20160402.13

| Peer-Reviewed

The Sectored Antenna Array Indoor Positioning System with Neural Networks

Chih-Yung Chen, Yu-Ju Chen, Ya-Chen Weng, Shen-Whan Chen, Rey-Chue Hwang

Published in Automation, Control and Intelligent Systems (Volume 4, Issue 2)

Received: 24 March 2016 Published: 25 March 2016

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

This paper presents a sectored antenna array indoor positioning system (IPS) with neural network (NN) technique. The hexagonal positioning station is composed of six printed-circuit board Yagi-Uda antennas and Zigbee modules. The values of received signal strength (RSS) sensed by wireless sensors were used to be the information for object’s position estimation. Two NN models, including NN with back-propagation (BP) learning algorithm and probabilistic NN (PNN), were applied to perform the positioning work for a comparison. In the experiments, an 8x8 square meters indoor scene was performed and 288 points and 440 points were experimented in this area. The positioning results show that both NN models have the average error less than 0.7 meter. In other words, the proposed positioning system not only has the high positioning accuracy, but also has the potential in real application.

Published in	Automation, Control and Intelligent Systems (Volume 4, Issue 2)
DOI	10.11648/j.acis.20160402.13
Page(s)	21-27
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Sectored Antenna, Indoor Positioning System (IPS), Neural Network (NN), Received Signal Strength (RSS)

References

[1]	Y. Y. Gu, A. Lo, I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 13-32, 2009.
[2]	H. Liu, H. Darabi, P. Banerjee, J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 37, no. 6, pp. 1067-1077, 2007.
[3]	G. W. Shi, Y. Ming, “Survey of indoor positioning systems based on ultra-wideband (UWB) technology,” Lecture Notes in Electrical Engineering, Wireless Communications, Networking and Applications, Proceedings of WCNA 2014, Vol. 348, pp. 1269-1278, 2016.
[4]	B. Kim, W. Bong, Y. C. Kim, “Indoor localization for Wi-Fi devices by cross-monitoring AP and weighted triangulation,” In the Proceedings of IEEE Consumer Communications and Networking Conference (CCNC), NV, U.S.A., pp. 933-936, 2011.
[5]	Y. Mo, Z. Z. Zhang, Y. Lu, G. Agha, “A novel technique for human traffic based radio map updating in Wi-Fi indoor positioning systems,” KSII Transactions on Internet and Information Systems, vol. 9, no. 5, pp. 1881-1903, 2015.
[6]	X. F. Jiang, C. J. Mike Liang, K. F. Chen, B. Zhang, J. Hsu, J. Liu, B. Cao, F. Zhao, “Design and evaluation of a wireless magnetic-based proximity detection platform for indoor applications,” In the Proceedings of 11th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN/SPOTS), Beijing, China, pp. 221-231, 2012.
[7]	J. Hightower, G. Borriello, “Location sensing techniques,” Technical Report UW CSE 2001-07-30, Department of Computer Science and Engineering, University of Washington, 2001.
[8]	K. Kaemarungsi, P. Krishnamurthy, “Properties of indoor received signal strength for WLAN location fingerprinting,” In the Proceedings of 1st Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous ’04), MA, USA, pp. 14-23, 2004.
[9]	D. Focken, R. Stiefelhagen, “Towards vision-based 3-D people tracking in a smart room,” In the Proceedings of 4th IEEE Intl Conference on Multimodal Interfaces, PA, USA, pp. 400-405, 2002.
[10]	A. Kotanen, M. Hannikainen, H. Leppakoski, T. D. Hamalainen, “Positioning with IEEE 802.11b wireless LAN,” In the Proceedings of 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, Beijing, China, pp. 2218–2222, 2003.
[11]	Y. B. Xu, M. Zhou, L. Ma, “Hybrid FCM/ANN indoor location method in WLAN environment,” In the Proceedings of IEEE Youth Conference on Information, Computing and Telecommunications, Beijing, China, pp. 475–478, 2009.
[12]	V. Honkavirta, T. Perala, S. Ali-Loytty, R. Piche, “A comparative survey of WLAN location fingerprinting methods,” In the Proceedings of 6th Workshop on Positioning, Navigation and Communication (WPNC’09), Hannover, Germany, pp. 243–251, 2009.
[13]	M. Y. Umair, K. V. Ramana, D. K. Yang, “An enhanced K-Nearest Neighbor algorithm for indoor positioning systems in a WLAN,” 2014 IEEE Computers, Communications and Its Applications, pp. 19-23, January 20, 2014.
[14]	K. F. S. Wong, I. W. Tsang, V. Cheung, S. H. G. Chan, J. T. Kwok, “Position estimation for wireless sensor networks,” In the Proceedings of IEEE Global Telecommunications Conference, MO, USA, pp. 2772–2776, 2005.
[15]	S. Aomumpai, K. Kondee, C. Prommak, K. Kaemarungsi, “Optimal placement of reference nodes for wireless indoor positioning systems,” 11th International Conference on Electrical Engineering, Electronics, Computer, Telecommunications and Information Technology. Paper no. 6839894, 2014.
[16]	P. Bahl V. N. Padmanabhan, “RADAR: An in-building RF-based user location and tracking system,” In the Proceedings of INFOCOM 2000, Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, Tel Aviv, Israel, pp. 775-784, 2000.
[17]	H. D. Chon, S. Jun, H. Jung, S. W. An, “Using RFID for accurate positioning,” Journal of Global Positioning Systems, vol. 3, pp. 32–39. 2004.
[18]	H. L. Ding, W. W. Y. Ng, P. P. K. Chan, D. L. Wu, X. L. Chen, D. S. Yeung, “RFID indoor positioning using RBFNN with L-GEM,” In the Proceedings of IEEE 2010 International Conference on Machine Learning and Cybernetics, Qingdao, China, pp. 1147–1152, 2010.
[19]	A. K. M. M. Hossain, W. S. Soh, “A comprehensive study of Bluetooth signal parameters for localization,” In the Proceedings of 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’07), Athens, Greece, pp. 1-5, 2007.
[20]	F. Subhan, H. Hasbullah, A. Rozyyev, S. T. Bakhsh, “Indoor positioning in Bluetooth networks using fingerprinting and lateration approach,” In the Proceedings of 2011 International Conference on Information Science and Applications (ICISA), Jeju Island, Korea, pp. 1-9, 2001.
[21]	W. P. Chen, X. F. Meng, “A cooperative localization scheme for Zigbee-based wireless sensor networks,” In the Proceedings of 14th IEEE International Conference on Networks, Singapore, pp. 1-5, 2006.
[22]	G. Goncalo, S. Helena, “Indoor location system using ZigBee technology,” In the Proceedings of Third International Conference on Sensor Technologies and Applications, Athens/Glyfada, Greece, pp. 152-157, 2009.
[23]	S. Merat, W. Almuhtadi, “Wireless network channel quality estimation inside reactor building using RSSI measurement of wireless sensor network,” In the Proceedings of Canadian Conference on Electrical and Computer Engineering, Calgary, AB, Canada, pp. 339-341, 2009.
[24]	H. C. Chen, Y. J. Chen, C. Y. Chen, S. M. T. Wang, J. P. Yang, R. C. Hwang, “A new indoor positioning technique based on neural network,” Advanced Science Letters, vol. 19, no. 7, pp. 2029-2033, 2013.
[25]	A. Zaknich, “Introduction to the modified probabilistic neural network for general signal processing applications,” IEEE Transactions on Signal Processing, vol. 46, no. 7, pp. 1980-1990, 1998.
[26]	R. C. Chen, Y. H. Lin, “Using ZigBee sensor network with artificial neural network for indoor location,” In the Proceedings of Eighth International Conference on Natural Computation, Chongqing, China, pp. 290-294, 2012.
[27]	M. Altini, D. Brunelli, E. Farella, L. Benini, “Bluetooth indoor localization with multiple neural networks,” In the Proceedings of 5th IEEE International Symposium on Wireless Pervasive Computing (ISWPC), Modena, Italy, pp. 295-300, 2010.
[28]	Y. S. Lin, R. C. Chen, Y. C. Lin, “An indoor location identification system based on neural network and genetic algorithm,” In the Proceedings of 3rd International Conference on Awareness Science and Technology (iCAST), Dalian, China, pp. 193-198, 2011.
[29]	H. Mohammad, A. F. Ozan, A. N. Ali, P. Aveh, “Neural network assisted identification of the absence of direct path in indoor localization,” In the Proceedings of IEEE Global Telecommunications Conference, Washington, DC, USA, pp. 387–392, 2007.
[30]	S. H. Fang, T. N. Lin, “Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments,” IEEE Transactions on Neural Networks, vol. 19, no. 11, pp. 1973–1978, 2008.
[31]	C. Laoudias, D. G. Eliades, P. Kemppi, C. G. Panayiotou, M. M. Polycarpou, “Indoor localization using neural networks with location fingerprints,” Lecture Notes in Computer Science - 19th International Conference on Artificial Neural Networks, Limassol, Cyprus, vol. 5769, no. 2, pp. 954–963, 2009.
[32]	A. Cidronali, S. Maddio, G. Giorgetti, G. Manes, “Analysis and performance of a smart antenna for 2.45-GHz single-anchor indoor positioning,” IEEE Trans. Microw. Theory Tech., vol. 58, no. 1, pp. 21-31, 2010.
[33]	C. H. Lim, Y. Wan, B. P. Ng, C. M. S. See, “A real-time indoor WiFi localization system utilizing smart antennas,” IEEE Transactions on Consumer Electronics, vol. 53, no. 2, pp. 618-622, 2007.
[34]	X. M. Qing, Z. N. Chen, T. S. P. See, “Sectored antenna array for indoor mono-station UWB positioning applications,” In the Proceedings of 3rd European Conference on Antennas and Propagation, Berlin, Germany, pp. 822-825, 2009.
[35]	Antennas and Propagation. http://www.radio-electronics.com/info/antennas/yagi/yagi.php
[36]	A. Khotanzad, R. C. Hwang, A. Abaye, D. Maratukulam, “An adaptive modular artificial neural network: Hourly load forecaster and its implementation at electric utilities,” IEEE Transactions on Power Systems, vol. 10, pp. 1716–1722, 1995.
[37]	R. C. Hwang, P. T. Hsu, J. Cheng, C. Y. Chen, C. Y. Chang, H. C. Huang, “The indoor positioning technique based on neural networks,” In the Proceedings of IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC 2011), Xi'an, China, pp. 225-228, 2011.

Cite This Article

Plain Text BibTeX RIS

APA Style

Chih-Yung Chen, Yu-Ju Chen, Ya-Chen Weng, Shen-Whan Chen, Rey-Chue Hwang. (2016). The Sectored Antenna Array Indoor Positioning System with Neural Networks. Automation, Control and Intelligent Systems, 4(2), 21-27. https://doi.org/10.11648/j.acis.20160402.13

Copy | Download

ACS Style

Chih-Yung Chen; Yu-Ju Chen; Ya-Chen Weng; Shen-Whan Chen; Rey-Chue Hwang. The Sectored Antenna Array Indoor Positioning System with Neural Networks. Autom. Control Intell. Syst. 2016, 4(2), 21-27. doi: 10.11648/j.acis.20160402.13

Copy | Download

AMA Style

Chih-Yung Chen, Yu-Ju Chen, Ya-Chen Weng, Shen-Whan Chen, Rey-Chue Hwang. The Sectored Antenna Array Indoor Positioning System with Neural Networks. Autom Control Intell Syst. 2016;4(2):21-27. doi: 10.11648/j.acis.20160402.13

Copy | Download

@article{10.11648/j.acis.20160402.13,
  author = {Chih-Yung Chen and Yu-Ju Chen and Ya-Chen Weng and Shen-Whan Chen and Rey-Chue Hwang},
  title = {The Sectored Antenna Array Indoor Positioning System with Neural Networks},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {2},
  pages = {21-27},
  doi = {10.11648/j.acis.20160402.13},
  url = {https://doi.org/10.11648/j.acis.20160402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160402.13},
  abstract = {This paper presents a sectored antenna array indoor positioning system (IPS) with neural network (NN) technique. The hexagonal positioning station is composed of six printed-circuit board Yagi-Uda antennas and Zigbee modules. The values of received signal strength (RSS) sensed by wireless sensors were used to be the information for object’s position estimation. Two NN models, including NN with back-propagation (BP) learning algorithm and probabilistic NN (PNN), were applied to perform the positioning work for a comparison. In the experiments, an 8x8 square meters indoor scene was performed and 288 points and 440 points were experimented in this area. The positioning results show that both NN models have the average error less than 0.7 meter. In other words, the proposed positioning system not only has the high positioning accuracy, but also has the potential in real application.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - The Sectored Antenna Array Indoor Positioning System with Neural Networks
AU  - Chih-Yung Chen
AU  - Yu-Ju Chen
AU  - Ya-Chen Weng
AU  - Shen-Whan Chen
AU  - Rey-Chue Hwang
Y1  - 2016/03/25
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160402.13
DO  - 10.11648/j.acis.20160402.13
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 21
EP  - 27
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160402.13
AB  - This paper presents a sectored antenna array indoor positioning system (IPS) with neural network (NN) technique. The hexagonal positioning station is composed of six printed-circuit board Yagi-Uda antennas and Zigbee modules. The values of received signal strength (RSS) sensed by wireless sensors were used to be the information for object’s position estimation. Two NN models, including NN with back-propagation (BP) learning algorithm and probabilistic NN (PNN), were applied to perform the positioning work for a comparison. In the experiments, an 8x8 square meters indoor scene was performed and 288 points and 440 points were experimented in this area. The positioning results show that both NN models have the average error less than 0.7 meter. In other words, the proposed positioning system not only has the high positioning accuracy, but also has the potential in real application.
VL  - 4
IS  - 2
ER  -

Copy | Download

Author Information

Chih-Yung Chen

Department of Computer and Communication, Shu-Te University, Kaohsiung City, Taiwan
Yu-Ju Chen

Department of Information Management, Cheng Shiu University, Kaohsiung City, Taiwan
Ya-Chen Weng

Department of Computer and Communication, Shu-Te University, Kaohsiung City, Taiwan
Shen-Whan Chen

Department of Communication Engineering, I-Shou University, Kaohsiung City, Taiwan
Rey-Chue Hwang

Department of Electrical Engineering, I-Shou University, Kaohsiung City, Taiwan

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Chih-Yung Chen, Yu-Ju Chen, Ya-Chen Weng, Shen-Whan Chen, Rey-Chue Hwang. (2016). The Sectored Antenna Array Indoor Positioning System with Neural Networks. Automation, Control and Intelligent Systems, 4(2), 21-27. https://doi.org/10.11648/j.acis.20160402.13

Copy | Download

ACS Style

Chih-Yung Chen; Yu-Ju Chen; Ya-Chen Weng; Shen-Whan Chen; Rey-Chue Hwang. The Sectored Antenna Array Indoor Positioning System with Neural Networks. Autom. Control Intell. Syst. 2016, 4(2), 21-27. doi: 10.11648/j.acis.20160402.13

Copy | Download

AMA Style

Chih-Yung Chen, Yu-Ju Chen, Ya-Chen Weng, Shen-Whan Chen, Rey-Chue Hwang. The Sectored Antenna Array Indoor Positioning System with Neural Networks. Autom Control Intell Syst. 2016;4(2):21-27. doi: 10.11648/j.acis.20160402.13

Copy | Download

@article{10.11648/j.acis.20160402.13,
  author = {Chih-Yung Chen and Yu-Ju Chen and Ya-Chen Weng and Shen-Whan Chen and Rey-Chue Hwang},
  title = {The Sectored Antenna Array Indoor Positioning System with Neural Networks},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {2},
  pages = {21-27},
  doi = {10.11648/j.acis.20160402.13},
  url = {https://doi.org/10.11648/j.acis.20160402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160402.13},
  abstract = {This paper presents a sectored antenna array indoor positioning system (IPS) with neural network (NN) technique. The hexagonal positioning station is composed of six printed-circuit board Yagi-Uda antennas and Zigbee modules. The values of received signal strength (RSS) sensed by wireless sensors were used to be the information for object’s position estimation. Two NN models, including NN with back-propagation (BP) learning algorithm and probabilistic NN (PNN), were applied to perform the positioning work for a comparison. In the experiments, an 8x8 square meters indoor scene was performed and 288 points and 440 points were experimented in this area. The positioning results show that both NN models have the average error less than 0.7 meter. In other words, the proposed positioning system not only has the high positioning accuracy, but also has the potential in real application.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - The Sectored Antenna Array Indoor Positioning System with Neural Networks
AU  - Chih-Yung Chen
AU  - Yu-Ju Chen
AU  - Ya-Chen Weng
AU  - Shen-Whan Chen
AU  - Rey-Chue Hwang
Y1  - 2016/03/25
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160402.13
DO  - 10.11648/j.acis.20160402.13
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 21
EP  - 27
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160402.13
AB  - This paper presents a sectored antenna array indoor positioning system (IPS) with neural network (NN) technique. The hexagonal positioning station is composed of six printed-circuit board Yagi-Uda antennas and Zigbee modules. The values of received signal strength (RSS) sensed by wireless sensors were used to be the information for object’s position estimation. Two NN models, including NN with back-propagation (BP) learning algorithm and probabilistic NN (PNN), were applied to perform the positioning work for a comparison. In the experiments, an 8x8 square meters indoor scene was performed and 288 points and 440 points were experimented in this area. The positioning results show that both NN models have the average error less than 0.7 meter. In other words, the proposed positioning system not only has the high positioning accuracy, but also has the potential in real application.
VL  - 4
IS  - 2
ER  -

Copy | Download