Localization using Neural Networks and Push-Pull Estimation based on RSS from AP to Mobile Device
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Although the development of Global Positioning System (GPS) are more and more mature, its accuracy is just acceptable for outdoor positioning, not positioning for the indoor of building and the underpass. For the positioning application area for the indoor of building and the underpass, GPS even cannot achieve that accuracy because of the construction materials while the requirement for accurate positioning in the indoor of building and the underpass, because a space, a person is necessary, may be very small space with several square meters in the indoor of building and the underpass. The Received Signal Strength (RSS) based localization is becoming a good choice especially for the indoor of building and the underpass scenarios where the WiFi signals of IEEE 802.11, Wireless LAN, are available in almost every indoor of building and the underpass. The fundamental requirement of such localization system is to estimate location from Access Point (AP) to mobile device using RSS at a specific location. The Multi-path fading effects in this process make RSS to fluctuate unpredictably, causing uncertainty in localization. To deal with this problem, the combination for the method of Neural Networks and Push-Pull Estimation is applied so that the carried along the devices can learn and make the decision of position using mobile device where it is in the indoor of building and the underpass.
Abstract
Although the development of Global Positioning System (GPS) are more and more mature, its accuracy is just acceptable for outdoor positioning, not positioning for the indoor of building and the underpass. For the positioning application area for the indoor of building and the underpass, GPS even cannot achieve that accuracy because of the construction materials while the requirement for accurate positioning in the indoor of building and the underpass, because a space, a person is necessary, may be very small space with several square meters in the indoor of building and the underpass. The Received Signal Strength (RSS) based localization is becoming a good choice especially for the indoor of building and the underpass scenarios where the WiFi signals of IEEE 802.11, Wireless LAN, are available in almost every indoor of building and the underpass. The fundamental requirement of such localization system is to estimate location from Access Point (AP) to mobile device using RSS at a specific location. The Multi-path fading effects in this process make RSS to fluctuate unpredictably, causing uncertainty in localization. To deal with this problem, the combination for the method of Neural Networks and Push-Pull Estimation is applied so that the carried along the devices can learn and make the decision of position using mobile device where it is in the indoor of building and the underpass.
참고문헌 (25)
- Cynthia, "Challenges in Location-Aware Computing", pp.1268~1536 (2003), IEEE ComSoc
- G. Sun, "Signal Processing Techniques in Network-Aided Positioning: A Survey of State-of-the-art Positioning Designs", IEEE Signal Processing Magazine, 22(4), pp.12~23 (2005)
- R. Mautz, "The challenges of indoor environments and specification on some alternative positioning systems", Positioning, Navigation and Communication, 2009. WPNC 2009, 6th Workshop on March 2009, pp.29~36 (2009)
- V. Honkavirta, "A comparative survey of WLAN location fingerprinting methods", Positioning, Navigation and Commu- nication, 2009. WPNC 2009. 6th Workshop on, pp.243~251 (2009)
- Steve Pope, "Issues Related to RSSI Measurement", IEEE802.11- 02
- L. Jing, "Super-resolution time of arrival estimation for indoor geolocation based on IEEE 802.11 a", g, 2008, 6612~6615
- Dieter, "Bayesian Filters for Location Estimation", IEEE CS and IEEE ComSoc, pp.1268~1536
- Roy, "The active badge location system", ACM Transactions on Information Systems (TOIS), 10(1), pp.91~102
- A. Bensky, "Wireless Positioning Technologies and Applications" (2008), Artech House
- R. Singh, "A statistical modeling based location determination method using fusion in WLAN", In Proceedings of the International Workshop on Wireless Ad-hoc Networks (2005)
- N. K. Sharma, "A weighted center of mass based trilateration approach for locating wireless devices in indoor environment", In Proceedings of the 4th ACM international workshop on Mobility management and wireless access, pp.112~115 (2006)
- A. Kushki, "Kernel-Based Positioning in Wireless Local AreaNetworks", IEEE Transactions, 6(6), pp.689~705 (2007)
- "i-Phone WiFi Scanner Apps Banned By Apple" (2010)[URL]
- K. Kaemarungsi, "Modeling of indoor positioning systems based on location fingerprinting", In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, 2, pp.1012~1022 (2004)
- A. Goldsmith, "Wireless Communications,1st ed" (2005), Cambridge University Press
- B. Li, "Method for yielding a database of location fingerprints in WLAN", IEEE Proceedings, 152(5), pp.580~586 (2005)
- Uzair, "In Building Localization using Neural Networks", IEEE International Conference on Engineering of Intelligent Systems (2006)
- K. Kaemarungsi, "MOBIQUITOUS 2004", in Mobile and Ubiquitous Systems: Net- working and Services, 2004. MOBIQUITOUS 2004. The First Annual International Conference on, pp.14~23 (2004)
- Anthea Wain Sy Au, "RSS-based WLAN Indoor Positioning and Tracking System Using Compressive Sensing and Its Implementation on Mobile Devices", ~
- Statistical Learning Theory for Location Fingerprinting in Wireless LANs, "Roberto Battiti", University of Trento Italy, ~, pp.Technical Report # DIT-02-0086 (2002)
- Jie Wang, "Differential Radio Map Based Robust Indoor Localization", Eurasip Journal on Wireless Communications and Networking, pp.1~20
- Seong Jin Cho, "Location Estimation based Personalization using Support Vector Machine and Signal Strength of Mobile Phone", ~
- Viet-Hung Dang, "Distributed Push-Pull Estimation for Node Localization in Wireless Sensor Networks", Journal of Parallel and Distributed Computing, 71(3), pp.471~484 (2011)
- Viet-Hung Dang, "Acoustic multiple object positioning system", In International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, PE-WASUN-2010 (2010), ACM
- Uzair, "Modular Multilayer Perceptron For WLAN Based Localization", Neural Networks, 2006. IJCNN '06, International Joint Conference (2006)