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Using context-aware sub sorting of received signal strength fingerprints for indoor localisation

Conference Paper


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Abstract


  • Mobile indoor localisation has numerous uses for

    logistics, health, sport and social networking applications. Current

    wireless localisation systems experience reliability difficulties

    while operating within indoor environments due to interference

    caused by the presence of metallic infrastructure. Current position

    localisation use wireless channel propagation characteristics,

    such as RF receive signal strength to localise a user’s position,

    which is subject to interference. To overcome this, we developed

    a Fingerprint Context Aware Partitioning tracking model for

    tracking people within a building. The Fingerprint Context Aware

    Partitioning tracking model used received RF signal strength fingerprinting,

    combined with localised context aware information

    about the user’s immediate indoor environment surroundings.

    We also present an inexpensive and robust wireless localisation

    network that can track the location of users in an indoor

    environment, using the Zigbee/802.15.4 wireless communications

    protocol. The wireless localisation network used reference nodes

    placed at known positions in a building. The reference nodes are

    used by mobile nodes, carried by users to localise their position.

    We found that the Fingerprint Context Aware Partitioning model

    had improved performance than using only multilateration, in

    locations that were not in range of multiple reference nodes.

    Further work includes investigating how multiple mobile nodes

    can be used by Fingerprint Context Aware Partition model to

    improve position accuracy.

UOW Authors


  •   Ros, Montserrat
  •   Schoots, Brendan (external author)
  •   D'Souza, Matthew (external author)

Publication Date


  • 2012

Citation


  • M. Ros, B. Schoots & M. D'Souza, "Using context-aware sub sorting of received signal strength fingerprints for indoor localisation," in 2012 6th International Conference on Signal Processing and Communication Systems (ICSPCS), 2012, pp. 1-7.

Scopus Eid


  • 2-s2.0-84880305674

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1529&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/523

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 7

Place Of Publication


  • USA

Abstract


  • Mobile indoor localisation has numerous uses for

    logistics, health, sport and social networking applications. Current

    wireless localisation systems experience reliability difficulties

    while operating within indoor environments due to interference

    caused by the presence of metallic infrastructure. Current position

    localisation use wireless channel propagation characteristics,

    such as RF receive signal strength to localise a user’s position,

    which is subject to interference. To overcome this, we developed

    a Fingerprint Context Aware Partitioning tracking model for

    tracking people within a building. The Fingerprint Context Aware

    Partitioning tracking model used received RF signal strength fingerprinting,

    combined with localised context aware information

    about the user’s immediate indoor environment surroundings.

    We also present an inexpensive and robust wireless localisation

    network that can track the location of users in an indoor

    environment, using the Zigbee/802.15.4 wireless communications

    protocol. The wireless localisation network used reference nodes

    placed at known positions in a building. The reference nodes are

    used by mobile nodes, carried by users to localise their position.

    We found that the Fingerprint Context Aware Partitioning model

    had improved performance than using only multilateration, in

    locations that were not in range of multiple reference nodes.

    Further work includes investigating how multiple mobile nodes

    can be used by Fingerprint Context Aware Partition model to

    improve position accuracy.

UOW Authors


  •   Ros, Montserrat
  •   Schoots, Brendan (external author)
  •   D'Souza, Matthew (external author)

Publication Date


  • 2012

Citation


  • M. Ros, B. Schoots & M. D'Souza, "Using context-aware sub sorting of received signal strength fingerprints for indoor localisation," in 2012 6th International Conference on Signal Processing and Communication Systems (ICSPCS), 2012, pp. 1-7.

Scopus Eid


  • 2-s2.0-84880305674

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1529&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/523

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 7

Place Of Publication


  • USA