WLAN location determination via clustering and probability distributions

By Moustafa Youssef, Ashok K. Agrawala, A. Shankar. Annual IEEE International Conference on Pervasive Computing and Communications, 2003

1,059 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/c872af08fcf473d5275f2733a9c5e5938fe87754

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Citations in the corpus

Modeling of indoor positioning systems based on location fingerprinting (2004, methodology): “Comments from the authors of [18] and [53] have suggested that location fingerprints with different makes of wireless cards can be different, perhaps substantially.”
Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments (2009, background): “This is usually constructed only one time during an offline phase.”
Adaptive Temporal Radio Maps for Indoor Location Estimation (2005, methodology): “In the offline phase, aradio mapis built by tabulating the signalstrength values received from the access points at selected locations in the area of interest.”
Recent Advances in Wireless Indoor Localization Techniques and System (2013, methodology): “The location of an object is then determined by matching online measurement with the closed location against the database [35] (Figure 8).”
A Survey of Selected Indoor Positioning Methods for Smartphones (2017, background): “[26] proposed to reduce computational cost by choosing only a subset of all "heard" APs with the strongest signal.”
Power-efficient access-point selection for indoor location estimation (2006, methodology): “Another branch of research is the probabilistic techniques [10], [3], [11], [1], [6] which construct a conditional probability distribution over locations in the environment of interest.”
Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing (2012, methodology): “• Strongest APs [26] Same as what we used in the coarse localization, the set of APs with the highest RSS readings is selected, arguing that the strongest APs provide the highest probability of the coverage over time.”
The limits of localization using signal strength: a comparative study (2004, methodology): “Up to this step ABP is very similar to the traditional Bayesian approaches [14], [17], with the exception of the Gaussian and variance assumptions.”
Practical robust localization over large-scale 802.11 wireless networks (2004, methodology): “Clustering techniques have also been applied to the problem of location determination [58].”
Survey of Wireless Indoor Positioning Techniques and Systems (2007, methodology): “Horus system [37], [38] offered a joint clustering technique for location estimation, which uses the probabilistic method described previously.”
Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking (2009, methodology): “The algorithms selected here span broad techniques that proved practical and useful from surveying the references (Elnahrawy et al., 2004; Bahl et al., 2000; Youssef et al., 2003; Youssef et al., 2004; Battiti et al., 2002; Moore et al., 2004; Fang et al., 2005; Savvides et al., 2001; Lorincz et al.”
Kernel-Based Positioning in Wireless Local Area Networks (2007, methodology): “The most commonly used selection methodology is to choose a subset of APs with the highest observation RSS as the strongest APs provide the highest probability of coverage over time [26].”
The Cricket indoor location system (2005, methodology): “Several indoor location systems use this technique to determine a mobile node’s position based on the RF signal strength of access points [6, 15, 56, 41, 79, 109].”
Accurate GSM Indoor Localization (2005, neutral): “There have been improvements to RADAR’s fingerprint matching algorithm that have improved accuracy [2,11,17] and differentiated floors of a building with a high degree of precision [6].”
Indoor localization in multi-floor environments with reduced effort (2010, background): “Typical pattern descriptions include histogram [9], mixture of gaussian[10], or simply the mean value of signal strength at different locations [1].”