Bayesian indoor positioning systems

By David Madigan, E. Einahrawy, Richard P. Martin, Wen-Hua Ju, Parameshwaran Krishnan, Anjur Sundaresan Krishnakumar. Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., 2005

427 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c5c7dbfb644d8999f37250fae8c254c67adfb888

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Citations in the corpus

Zero-Configuration, Robust Indoor Localization: Theory and Experimentation (2006, background): “[22] build a Bayesian hierarchical model to make a tradeoff between the training dataset sizes and the levels of the range estimation precision.”
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, methodology): “ministic [2] and probabilistic methods [52], [53].”
Indoor localization without the pain (2010, methodology): “[18] use a Bayesian hierarchical approach for indoor localization, which avoids the need to know the locations of the training points.”
WILL: Wireless indoor localization without site survey (2012, methodology): “[18] uses a Bayesian hierarchical model to avoid the need of locations of the training points.”
A survey on sensor localization (2010, methodology): “There is also a reference to the use of Bayesian models for the noisy distance measurements [ 43 ].”
Indoor localization in multi-floor environments with reduced effort (2010, background): “For example, [15] and [16] only asked for unlabeled data to do localization; however, both of them required the access points’ locations to be known in advance, which is not always available in practice due to security control.”
Smartphones Based Crowdsourcing for Indoor Localization (2015, background): “Global reference points include the last reportedGPS location [6], AP’s location [19], similar surrounding sound signature [1], feature-distinct public area, etc.”
ArrayTrack: A Fine-Grained Indoor Location System (2011, neutral): “Other model-based proposals augment RF propagation models with Bayesian probabilistic models to capture the relationships between different nodes in the network [16], and develop conditions for a set of nodes to be localizable [42].”
Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking (2009, methodology): “Although the tiles are concentrated around the most likely location, the scatter is substantial and can interfere with higher-level functions (Madigan et al., 2005; Elnahrawy et al., 2004).”
Wireless sensor network localization techniques (2007, methodology): “Recently, there has been proposed a method of online profiling, which would reduce or possibly eliminate the amount of profiling required before deployment, but at the expense of deploying a large number of additional devices (termed ‘‘sniffers’’) at known locations [36,44].”
Indoor Tracking: Theory, Methods, and Technologies (2015, background): “Factor graphs are efﬁcient tools for representing the conditional dependence among the random variables of a model as happens in cooperative tracking settings [54]–[56].”
Growing an organic indoor location system (2010, neutral): “[24] and Lim et al.”
Locating in fingerprint space: wireless indoor localization with little human intervention (2012, background): “Global reference points include the last reported GPS location [6], AP’s location [18], similar surrounding sound signature [1], feature-distinct public area, etc.”
A Survey on Localization for Mobile Wireless Sensor Networks (2009, methodology): “Such methods are used in [55], [53], and [70].”
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “However, it is shown that the distribution is not typically log-normal but left skewed, stationary only over small time frames, and the user’s presence makes it multi-modal [65], [85], [101].”