| Proj No. | A3187-251 |
| Title | Development of deep learning algorithm for radar pulse deinterleaving |
| Summary | It is well known that radar emitter pulse streams exhibit flexible features and intricate patterns. In complex electromagnetic environments, pulse signals from different emitters often overlap in time, spatial and frequency domains, which makes pulse deinterleaving become a pivotal focus in the pulse stream processing field. Nowadays, as the electromagnetic environment is more complex, Deep Learning (DL) based methods have gained prominence in radar signal analysis, where convolutional neural network (CNN) and recurrent neural network (RNN) based deinterleaving methods are becoming increasingly popular. In this project, the student is expected to explore a novel DL-based deinterleaving algorithm for multi-pulse deinterleaving using pulse features, such as pulse repetition interval (PRI) and time of arrival (TOA). Matlab or Python programming will be used to generate numerical results for this project. |
| Supervisor | A/P Teh Kah Chan (Loc:S2 > S2 B2A > S2 B2A 03, Ext: +65 67905365) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Centre for Information Sciences & System (CISS) (Loc: S2-B4b-05) |
| Single/Group: | Single |
| Area: | Wireless and Communications Engineering |
| ISP/RI/SMP/SCP?: |