Statistical learning theory for location fingerprinting in wireless LANs

By Mauro Brunato, Roberto Battiti. Comput. Networks, 2005

644 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/346f08405785740e4440fdbff6ef9b1b646b203f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

In Signal Processing

Citations in the corpus

Machine learning in manufacturing: advantages, challenges, and applications (2016, methodology): “SLT allows to reduce the number of needed samples in certain cases (Koltchinskii, Abdallah, Ariola, & Dorato, 2001).”
Kernel-Based Positioning in Wireless Local Area Networks (2007, methodology): “The dimensions of the experimentation site were 36 42 m, which are comparable to those reported in [23], [25], and [43].”
CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach (2016, methodology): “purposes, we implemented three existing methods, including FIFS [12], Horus [6], and ML [17].”
Wireless sensor network localization techniques (2007, methodology): “Distance-based one-hop localization techniques The most well-known distance-based localization technique is based on use of GPS.”
Survey of Wireless Indoor Positioning Techniques and Systems (2007, methodology): “Support vector classification (SVC) of multiple classes and support vector regression (SVR) have been used successfully in location fingerprinting [19], [20].”
CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach (2016, methodology): “For comparison purpose, we implement three existing methods, including FIFS [10], Horus [6], and ML [17].”
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “ponent is added to compensate for the RSS variations [54].”
DeepFi: Deep learning for indoor fingerprinting using channel state information (2015, methodology): “3) Benchmarks: For comparison purpose, we implemented three existing methods, including FIFS [9], Horus [6], and Maximum Likelihood (ML) [14].”
Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs (2005, methodology): “Other fingerprinting algorithms, including the K-nearest neighbor (KNN), the Kalman filter, ANN, and the support vector machine, are discussed in [34].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “Their toolbox yields much higher accuracy as compared to traditional methods, including including FIFS [487], Horus [488], and Maximum Likelihood [489].”
The limits of localization using signal strength: a comparative study (2004, background): “The work most closely related to ours is [11].”
Indoor Tracking: Theory, Methods, and Technologies (2015, methodology): “Many of the proposed methods that use position measurements come from the theory of pattern recognition [59], [60].”
A system for LEASE: location estimation assisted by stationary emitters for indoor RF wireless networks (2004, methodology): “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].”
Fusing Similarity-Based Sequence and Dead Reckoning for Indoor Positioning Without Training (2017, neutral): “, WKNN (weighted k-nearest neighbors) [28] and Horus [29].”
Bayesian indoor positioning systems (2005, background): “Each point is mapped to either a signal strength vector ([1], [13], [17], [29], [21] or a signal strength probability distribution ([ 3 ], [20], [26], [29], [30]).”
Recurrent Neural Networks for Accurate RSSI Indoor Localization (2019, methodology): “Despite their low complexity, the accuracy of these methods is unstable due to the wide fluctuation of WiFi RSSI [13]–[15].”
Modeling of indoor positioning systems based on location fingerprinting (2004, methodology): “The tuple of real coordinates can vary from one dimension to five dimensions which includes the three dimension space and two orientation variables expressed in spherical coordinates [22].”