Investigation of Neural ODE LSTM for RSSI Indoor Localization
| dc.contributor.author | Chang, Charles | |
| dc.contributor.supervisor | Dong, Xiaodai | |
| dc.date.accessioned | 2022-09-01T01:52:48Z | |
| dc.date.available | 2022-09-01T01:52:48Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-08-31 | |
| dc.degree.department | Department of Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Engineering M.Eng. | en_US |
| dc.description.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. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/14171 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Indoor localization | en_US |
| dc.subject | Neural Ordinary Differential Equations | en_US |
| dc.subject | Recurrent Neural Network | en_US |
| dc.title | Investigation of Neural ODE LSTM for RSSI Indoor Localization | en_US |
| dc.type | project | en_US |