Abstract
<p class="0abstract">In this report, we perform the digital filter computation using Matlab for Wi-Fi tracking application. This work motivates to improve the accuracy of filter algorithm in the RSSI-based distance localization system. There are several aspects that we can improve, e.g., in the Filter part and Path-loss model. But, in this work, we focus on filter part; Unscented Kalman Filter (UKF) is implemented to replace linear Kalman Filter (KF), which is used in previous work. Based on the performance comparison, UKF has 90% hit ratio while linear KF has only 81.15 % hit ratio. We found that UKF can handle the noise in RSSI. Further work, the UKF algorithm is then embedded on the server system.</p>
Highlights
In [1], we have performed some experiment to observe whether the RSSI method is suitable for Wi-Fi tracker system or not
Based on literature review, including Ref. [3,4,5,6,7], we found that the Non-Linier Least Square (NLS) + Unscented Kalman Filter (UKF) technique for RSSI-based Wi-Fi tracking system applications has not been done by many researchers
UKF is used to reduce the high variance on RSSI value
Summary
In [1], we have performed some experiment to observe whether the RSSI method is suitable for Wi-Fi tracker system or not. We found out that we can collect the required information from a smartphone, that are Wi-Fi signal strength and MAC address. In [1], there are several points that become consideration for further evaluation: 1) unstable value of RSSI; 2) interval-time which the packet data request is not constant broadcasted, it depends on the smartphone state; and 3) each smartphone emits different initial power. Those things become main concern whenever use RSSI method in Wi-Fi iJIM ‒ Vol 14, No 16, 2020
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Interactive Mobile Technologies (iJIM)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
