Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
A Statistical Modeling Approach to Location Estimation
IEEE Transactions on Mobile Computing
WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
The Horus location determination system
Wireless Networks
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Exploring estimator bias-variance tradeoffs using the uniform CRbound
IEEE Transactions on Signal Processing
Location sensing and privacy in a context-aware computing environment
IEEE Wireless Communications
An empirically based path loss model for wireless channels in suburban environments
IEEE Journal on Selected Areas in Communications
A Polygonal Method for Ranging-Based Localization in an Indoor Wireless Sensor Network
Wireless Personal Communications: An International Journal
Design and measurement results of an UHF RFID localization system
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Push the limit of WiFi based localization for smartphones
Proceedings of the 18th annual international conference on Mobile computing and networking
A Multisensor Architecture Providing Location-based Services for Smartphones
Mobile Networks and Applications
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This work investigates the lower bounds of wireless localization accuracy using signal strength on commodity hardware. Our work relies on trace-driven analysis using an extensive indoor experimental infrastructure. First, we report the best experimental accuracy, twice the best prior reported accuracy for any localization system. We experimentally show that adding more and more resources (e.g., training points or landmarks) beyond a certain limit, can degrade the localization performance for lateration-based algorithms, and that it could only be improved further by "cleaning" the data. However, matching algorithms are more robust to poor quality RSS measurements. We next compare with a theoretical lower bound using standard Cramér Rao Bound (CRB) analysis for unbiased estimators, which is frequently used to provide bounds on localization precision. Because many localization algorithms are based on different mathematical foundations, we apply a diverse set of existing algorithms to our packet traces and found that the variance of the localization errors from these algorithms are smaller than the variance bound established by the CRB. Finally, we found that there exists a wide discrepancy from what freespace models predict in the signal to distance function even in an environment with limited shadowing and multipath, thereby imposing a fundamental limit on the achievable localization accuracy indoors.