Unsupervised Deep Feature Learning to Reduce the Collection of Fingerprints for Indoor Localization Using Deep Belief Networks

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    Abstract

    One of the most practical localization techniques is WLAN-based fingerprinting for location-based services because of the availability of WLAN Access Points (APs). This technique measures the Received Signal Strength (RSS) from APs at each indicated location to construct fingerprints. However, the collection of fingerprints is notoriously laborious and needs to be repeatedly updated due to the changes of environments. To reduce the workload of fingerprinting, we apply Deep Belief Networks to unlabeled RSS measurements to extract hidden features of the fingerprints, and thereby minimize the collection of fingerprints. These features are used as inputs for conventional regression techniques such as Support Vector Machine and K-Nearest Neighbors. The experiment results show that our feature representations learned from unlabeled fingerprints provide better performance for indoor localization than baseline approaches with a small fraction of labeled fingerprints traditionally used. In the experiment, our approach already improves the localization accuracy by 1.9 m when using only 10% of labeled fingerprints, compared to the closest baseline approach which used 100% of labeled fingerprints.

    Original languageEnglish
    Title of host publicationIPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation
    PublisherIEEE
    Pages1-7
    Number of pages7
    ISBN (Electronic)978-1-5386-5635-8
    DOIs
    Publication statusPublished - 24 Sep 2018
    Event9th International Conference on Indoor Positioning and Indoor Navigation 2018 - Nantes, France
    Duration: 24 Sep 201827 Sep 2018
    Conference number: 9
    http://ipin2018.ifsttar.fr/conference/about/

    Conference

    Conference9th International Conference on Indoor Positioning and Indoor Navigation 2018
    Abbreviated titleIPIN 2018
    CountryFrance
    CityNantes
    Period24/09/1827/09/18
    Internet address

    Keywords

    • deep belief network
    • fingerprint reduction
    • indoor localization
    • unsupervised deep feature learning
    • WLAN-fingerprint based localization

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