Abstract:
Localization and tracking of persons in industrial environment is critical in terms
of safety, privacy and security, particularly when there are hazardous zones. In
this research, RSSI of RF signals were used to localize, track and uniquely identify
a person in a cluttered environment with a case study into a doorway from a safe
zone to a hazardous zone in a cluttered warehouse. Vision based localization was
impractical both due to visual obstruction by moving large objects and privacy
issues. There were three approaches in RF based localization reviewed in this
work.This research uses the approach in which RF receivers are fixed and the
transmitter is worn by the target person. RSSI data in a doorway area of 420
cm × 450 cm was analysed both in simulation and in a real test bed and it
was proved that DNN and RNN based location prediction was feasible with an
accuracy of over 80% even though the environment had noise in the range of ±2
dB to ±15 dB and ±7 dB on average for RF signals. The experiments carried
out with a test bed consisting of Raspberry Pi-3 as receivers and Kontakt-io
Tough Beacon TB15-1 module as transmitter connected over POE module to a
centralized server. The results show that a bounded type RF receiver arrangement
to cover the whole area with at least few receivers mounted at a high elevation to
capture line of sight signals was effective in accurately localizing the person. The
density of positions at which the RSSI data is collected to train the DNN also
considerably affected the localization accuracy. The body attenuation was found
to be another critical factor affecting the localization accuracy. When the DNN
was trained with data captured at one orientation of the person, this DNN was
successful in localizing a person with the same orientation but not in localizing
a person in completely different orientations. This behaviour was used to detect
the body orientation of a person using multiple neural network. A straight path
traversed by a walking person at an average speed of 25 𝑐𝑚/𝑠 was successfully
tracked at a point-wise accuracy over 80% using time series RSSI data with a
threshold of 25 cm. The threshold was reduced to half by averaging the data
over three consecutive predicted positions in the form a centroid. Lastly, Timedomain
based RSSI data were used to train RNNs. Deep-LSTM model showed
around 95% path-wise localization accuracy for constructed walking paths. Also,
RNNs were able to detect the walking direction in single RNN network compared
to multiple DNN approach. Finally, this research was able to uniquely identify,
localize, detect body orientation and track the walking path of a person and since
the person is uniquely identified and RSSI data is MAC addressed this work can
be extended to localization of multiple persons.
Citation:
Aravinda, S.P.P. (2021). Optimization of RSSI based indoor localization and tracking using machine learning techniques [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22275