Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics
MSWiM '05 Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
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Personal and Ubiquitous Computing
Information Sciences: an International Journal
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IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
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Proceedings of the 4th ACM international workshop on Experimental evaluation and characterization
Unsupervised context detection using wireless signals
Pervasive and Mobile Computing
A priority-based pattern matching location algorithm for wireless sensor networks
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 1
Modeling RFID signal strength and tag detection for localization and mapping
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization
Mobile Networks and Applications
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LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
WASP: an enhanced indoor locationing algorithm for a congested Wi-Fi environment
MELT'09 Proceedings of the 2nd international conference on Mobile entity localization and tracking in GPS-less environments
A low-complexity location estimation scheme for indoor wireless local area networks
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
WLAN location-aware application based on accumulated orientation strength algorithm
EuroSSC'06 Proceedings of the First European conference on Smart Sensing and Context
Cryptography and Security
Terrain/clutter based location prediction by using multi-condition Bayesian decision theory
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ACM Transactions on Sensor Networks (TOSN)
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This paper describes a probabilistic approach to global localization within an in-door environment with minimum infrastructure requirements. Global localization is a flavor of localization in which the device is unaware of its initial position and has to determine the same from scratch. Localization is performed based on the Received Signal Strength Indication (RSSI) as the only sensor reading, which is provided by most off-the-shelf wireless network interface cards,. Location and orientation estimates are computed using Bayesian filtering on a sample set derived using Monte-Carlo sampling. Research leading to the proposed method is outlined along with results and conclusions from simulations and real life experiments.