Investigation of Neural ODE LSTM for RSSI Indoor Localization
Date
2022-08-31
Authors
Chang, Charles
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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-ODELSTM model should be taken into account for the extra model complexity in practical applications.
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Keywords
Indoor localization, Neural Ordinary Differential Equations, Recurrent Neural Network