Indoor position tracking using received signal strength-based fingerprint context aware partitioning

• Published in: IET radar, sonar & navigation Vol. 10; no. 8; pp. 1347 - 1355
• Main Authors: D'Souza, Matthew, Schoots, Brendan, Ros, Montserrat
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.10.2016
• Subjects: Accuracy; FCAP tracking model; Fingerprints; Indoor environments; indoor navigation; indoor position tracking; Interference; Localization; mobile indoor localisation; Partitioning; Position (location); radio tracking; radiofrequency interference; received RF signal strength fingerprinting; received signal strength‐based fingerprint context aware partitioning model; reference nodes; Research Article; RSSI; Signal strength; spatial density; user indoor environments; wireless access points; wireless channel propagation characteristics; wireless channels; wireless interference; wireless localisation network; wireless localisation systems; indoor navigation; FCAP tracking model; spatial density; radio tracking; wireless localisation systems; user indoor environments; wireless channel propagation characteristics; mobile indoor localisation; wireless localisation network; wireless interference; wireless access points; RSSI; radiofrequency interference; wireless channels; received RF signal strength fingerprinting; received signal strength-based fingerprint context aware partitioning model; reference nodes; indoor position tracking
• ISSN: 1751-8784; 1751-8792
• DOI: 10.1049/iet-rsn.2015.0396

Mobile indoor localisation has numerous uses for logistics and health applications. Current wireless localisation systems experience reliability difficulties in indoor environments due to interference and also require a large number of wireless access points to ensure position accuracy and resolution. Localisation using wireless channel propagation characteristics, such as radio-frequency (RF) receives signal strength are subject to wireless interference. The Fingerprint Context Aware Partitioning (FCAP) tracking model used received RF signal strength fingerprinting, combined with context aware information about the user's indoor environment. The authors show the use of context aware information in the FCAP model, reduces the effect of wireless interference and lowers the spatial density of access points required. The wireless localisation network consisted of reference nodes placed at locations in a building. Reference nodes are used by mobile nodes, to localise a user's position. The authors tested the FCAP model in a typical indoor environment and compared the performance and accuracy to other received signal strength indicator fingerprint localisation methods. They found the FCAP model had improved performance and was able to achieve a similar accuracy to other protocols, with fewer reference nodes.