Event Details
Investigation of Neural ODE LSTM for RSSI Indoor Localization
Presenter: Charles Chang
Supervisor:
Date: Thu, August 25, 2022
Time: 10:00:00 - 11:00:00
Place: via Zoom - please see link below
ABSTRACT
Link:
https://uvic.zoom.us/j/84837513597?pwd=dS8zN0w2TnRRbnBDWndWTDBUYVBoZz09
Meeting ID: 848 3751 3597
Password: 118376
Note: Please log in to Zoom via SSO and your UVic Netlink ID
Abstract:
Traditionally, indoor localization using received signal strength indicator (RSSI) measurements ignores the elapsed time between positions. This project investigates the use of elapsed time between positions as a feature with RSSI measurements for indoor localization. We use the recently proposed neural ordinary differential equations long short-term memory (ODE-LSTM) to handle the varying time gaps between each position. Our experiment compares the performance of the bidirectional LSTM (BiLSTM) model and the bidirectional ODE-LSTM (Bi-ODE-LSTM) model. The result shows that using time as a training feature for RSSI indoor localization can improve the accuracy. However, the benefit of the ODE-based model decreases as the number of RSSI features increases. Finally, the benefit brought by the Bi-ODE-LSTM model should be taken into account for the extra model complexity in practical applications.