Building optimal radio-frequency signal maps

Piotr Mirowski, Tin Kam Ho, Philip Whiting

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

7 Citations (Scopus)


A popular way for using radio-frequency (RF) signals (e.g. WiFi) to position people or device indoors is by matching received radio signal strength (RSS) to fingerprints that are spatial signatures of such measures. Traditionally such signal maps are built by manual collection of repeated measurements at predefined locations following a spatial sampling scheme. Recently, such labor intensive processes are being replaced by robot-based automation or crowd-sourced simultaneous localization and mapping (SLAM). These new approaches produce time-stamped trajectories along with time-stamped RSS as the human or robot moves freely about the building. However, they require an additional procedure to segment the continuous RF samples into fingerprint cells to produce a robust signal map. In this paper, we explore several strategies for building optimal signal maps from RSS collected along robotic or pedestrian trajectories. We compare two clustering algorithms with a baseline strategy that divides the trajectories into a hierarchy of fixed-size grids. We study the trade-off between the spatial extent of the fingerprint cells and the differentiability of the RSS distribution in each cell, as well as their impact on localization accuracy and on fingerprint storage. We experimented with traces collected by an autonomous robot exploring a large multi-floor office building.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781479952083
Publication statusPublished - 4 Dec 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameInternational Conference on Pattern Recognition
PublisherIEEE Computer Society
ISSN (Print)1051-4651


Other22nd International Conference on Pattern Recognition, ICPR 2014

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