3 research outputs found
A WiFi RSSI Ranking Fingerprint Positioning System and Its Application to Indoor Activities of Daily Living Recognition
WiFi RSSI (Received Signal Strength Indicators) seem to be the basis of the most widely
used method for Indoor Positioning Systems (IPS) driven by the growth of deployed
WiFi Access Points (AP), especially within urban areas. However, there are still several
challenges to be tackled: its accuracy is often 2-3m, it’s prone to interference and
attenuation effects, and the diversity of Radio Frequency (RF) receivers, e.g.,
smartphones, affects its accuracy. RSSI fingerprinting can be used to mitigate against
interference and attenuation effects. In this paper, we present a novel, more accurate,
RSSI ranking-based method that consists of three parts. First, an AP selection based on a
Genetic Algorithm (GA) is applied to reduce the positioning computational cost and
increase the positioning accuracy. Second, Kendall Tau Correlation Coefficient (KTCC)
and a Convolutional Neural Network (CNN) are applied to extract the ranking features
for estimating locations. Third, an Extended Kalman filter (EKF) is then used to smooth
the estimated sequential locations before Multi-Dimensional Dynamic Time Warping
(MD-DTW) is used to match similar trajectories or paths representing ADLs from
different or the same users that vary in time and space In order to leverage and evaluate
our IPS system, we also used it to recognise Activities of Daily Living (ADL) in an office
like environment. It was able to achieve an average positioning accuracy of 1.42m and a
79.5% recognition accuracy for 9 location-driven activities