, "Indoor position tracking using received signal strength-based fingerprint context aware partitioning," IET Radar, Sonar and Navigation, vol. 10, (8) pp. 1347-1355, 2016 Indoor position tracking using received signal strength-based fingerprint context aware partitioning 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. We tested the FCAP model in a typical indoor environment and compared the performance and accuracy to other RSSI Fingerprint localisation methods. We found the FCAP model had improved performance was able to achieve a similar accuracy to other protocols, with fewer reference nodes.
D'Souza et al. REF extended the fingerprint localizations by using context-aware information on mobile users, and they built a floor plan with the information and reduced the required number of signal sources and the effect of wireless interference.
Indoor position tracking using received signal strength-based fingerprint context aware partitioning
Iet Radar Sonar and Navigation
Mathematics