Name
Abstract | ||
|---|---|---|
The detection of a radar emittting signal and determining the associated radar function are among the most important duties of electronic warfare (EW) systems. In this study, the classification of radar function in accordance with Electronic Countermeasure (ECM) usage concept is aimed by using the radar parameters measured by EW systems. Multitask learning and single task learning neural networks are applied to this problem. Oversampling prior to classifier, quantization for interval values and grouping of class values are done in the pre-processing step. It is shown by the experimental results that, multitask learning technique outperforms single task learning technique. It is clearly observed that utilizing one or more of (1) oversampling algorithm, (2) preprocessed data set and (3) the grouped classes increases the performance of both methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SIU.2019.8806336 | 2019 27th Signal Processing and Communications Applications Conference (SIU) |
Keywords | Field | DocType |
Radar Function Classification,Multitask Learning(MTL),Machine Learning,Electronic Warfare | Radar,Multi-task learning,Oversampling,Pattern recognition,Computer science,Electronic countermeasure,Artificial intelligence,Electronic warfare,Quantization (signal processing),Artificial neural network,Classifier (linguistics) | Conference |
ISSN | ISBN | Citations |
2165-0608 | 978-1-7281-1905-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatih Altiparmak | 1 | 39 | 5.56 |
| Fatih Çagatay Akyön | 2 | 0 | 0.68 |
| Emirhan Özmen | 3 | 0 | 0.68 |
| Fuat Çogun | 4 | 0 | 0.68 |
| Aydin Bayri | 5 | 3 | 2.60 |