Chang, Charles2022-09-012022-09-0120222022-08-31http://hdl.handle.net/1828/14171Traditionally, 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-ODELSTM model should be taken into account for the extra model complexity in practical applications.enAvailable to the World Wide WebIndoor localizationNeural Ordinary Differential EquationsRecurrent Neural Networkproject