Properties of indoor received signal strength for WLAN location fingerprinting

By Kamol Kaemarungsi, Prashant V. Krishnamurthy. The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004., 2004

590 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/133710302e7454f3e283e19c92bbfa0865ddadb5

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In Electrical and Electronic Engineering

In Signal Processing

Citations in the corpus

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “So, the propagated signal is affected by severe frequency selective multipath fluctuations and hence cannot be considered wide sense stationary (non-WSS) [65].”
Kernel-Based Positioning in Wireless Local Area Networks (2007, methodology): “In particular, Silverman [44] recommends the following for- mula as a quick estimate of the parameter for a Gaussian kernel but cautions against overfitting: ¼ 4 2dþ 1 1=ðdþ4Þ ̂n 1=ð4þdÞ: ð21Þ Here, ̂2 ¼ 1d Pd l¼1 2 rli is the average of marginal variances.”
UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems (2014, background): “It is worth saying that, according to our experiments, the main factors that affect to the number of WAPs reported by a WiFi scan are the location, the phone model (Android version and hardware) and how the device is held, although we are aware that there exist other factors as is demonstrated in [6”
A survey of indoor positioning systems for wireless personal networks (2009, methodology): “Fingerprinting includes two phases: offline training phase and online position determination phase [50].”