A Statistical Modeling Approach to Location Estimation

By Teemu Roos, Petri Myllymäki, H. Tirri. IEEE Transactions on Mobile Computing, 2002

375 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/63e85a8b81cec954fe2f830db3b7f89576211b37

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In Aerospace Engineering

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Statistical learning theory for location fingerprinting in wireless LANs (2005, methodology): “The authors wish to thank Andrea Delai for his work on collecting the test samples and for developing software for the maximum likelihood approach in his thesis for the laurea degree [23].”
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.”
Bayesian indoor positioning systems (2005, background): “There exists a substantial literature on using RSS for location estimation in wireless networks - see, for example, [1], [13], [20], [29], [12].”
The limits of localization using signal strength: a comparative study (2004, methodology): “11 and signal strength are closely related to ours [10], [11], [12], [13], [14], [15], [16], [17].”
Location sensing and privacy in a context-aware computing environment (2002, methodology): “We evaluate this approach in comparison to our previous client-centric algorithm based on pattern matching (CMU-PM), Microsoft’s RADAR system [2], a server-centric approach (CMU-SC), and statistical approaches (e.g., in [3, 4 ]. We also relate these WaveLAN-dependent systems to proprietary infrastr”
Indoor Localization System using RSSI Measurement of Wireless Sensor Network based on ZigBee Standard (2006, methodology): “An inexpensive RF-based approach with low configuration requirements has been studied [3-6].”
Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning (2006, methodology): “A probabilistic approach has been proposed in [16, 17].”
Defense against Primary User Emulation Attacks in Cognitive Radio Networks (2008, methodology): “3As mentioned before, the two values can be estimated using the offsite calibration technique presented in [23].”
A system for LEASE: location estimation assisted by stationary emitters for indoor RF wireless networks (2004, background): “The collected information is the model where each point is mapped to either a signal strength vector [4, 14, 15, 19] or a signal strength probability distribution [6, 18, 22, 25].”
Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010, methodology): “In addition, the use of mobile positioning data in geographical studies has different approaches and algorithms (Roos et al. , 2002).”
Kernel-Based Positioning in Wireless Local Area Networks (2007, methodology): “SINCE the advent of the Global Positioning System (GPS) [1] and the introduction of the E-911 mandate by the US Federal Communications Commission, positioning systems have been used to deliver Location-Based Services (LBSs) [2], [3] in outdoor environments.”
Wireless sensor network localization techniques (2007, methodology): “, the RSS profiling-based localization techniques [42–46], work by constructing a form of map of the signal strength behavior in the coverage area.”