A Probabilistic Approach to WLAN User Location Estimation

By Teemu Roos, Petri Myllymäki, H. Tirri, P. Misikangas, Juha Sievänen. Int. J. Wirel. Inf. Networks, 2002

985 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a29a3e03a1f634943fe7b74055d4891433cc1efa

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Robotics-Based Location Sensing Using Wireless Ethernet (2002, result): “Roos et al. [ 42 ] implemented a similar system and got similar localization results.”
Survey of Wireless Indoor Positioning Techniques and Systems (2007, background): “[39] developed a grid-based Bayesian location-sensing system over a small region of their office building, achieving localization and tracking to within 1.”
Modeling of indoor positioning systems based on location fingerprinting (2004, methodology): “Probabilistic Methods Instead of representing the location fingerprints with prototypes or vectors of mean RSS as in the nearest neighbor approach, the probabilistic approach models the location fingerprint with conditional probability and utilizes the Bayesian inference concept to estimate location”
Indoor Positioning Techniques Based on Wireless LAN (2007, background): “This led researchers to consider a Bayesian approach to WLAN localization [8] [9].”
Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning (2009, methodology): “…for this, including k-nearest neighbor estimation, support vector regression, smallest M-vertex polygon, Bayesian modeling, neural networks and kernel-ized distance estimation (see Roos et al. 2002, Youssef et al. 2003, Kaemarungsi and Krishnamurthy 2004, Kolodziej and Hjelm 2006, Yim 2008).”
Adaptive Temporal Radio Maps for Indoor Location Estimation (2005, background): “In this section, we describe our experimental setup and the noisy characteristics of wireless channel which makes the problem of location determination a challenging task.”
Kernel-Based Positioning in Wireless Local Area Networks (2007, methodology): “These densities are estimated through either parametric [31] or nonparametric methods [28], [32], [33].”
Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning (2006, methodology): “Applying the Bayes rule leads to the following posterior distribution of the location [16]:”
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].”
A Statistical Modeling Approach to Location Estimation (2002, background): “Our preliminary empirical results [ 23 ] suggest that such solutions produce very accurate location estimation at the cost of onsite calibration, whereas the method presented in this paper is based on a more general-purpose model that is less accurate, but can be calibrated offsite with very little ”
RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers (2009, methodology): “These methods estimate probability density for the training RSSI and then compute likelihood/a posteriori estimates during the tracking phase using the observed RSSI and the estimated densities [8], [15], [16].”
Hyperbolic Location Fingerprinting: A Calibration-Free Solution for Handling Differences in Signal Strength (concise contribution) (2008, methodology): “Several LF systems use Bayesian inference [4, 10], which represents a probabilistic method.”
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, neutral): “known as Maximum Likelihood (ML) estimation [96].”
A comparative survey of WLAN location fingerprinting methods (2009, methodology): “Examples of possible norms are given in Table I.”