Name
Abstract | ||
|---|---|---|
A fast and excellent unimodular waveform with good autocorrelation and cross-correlation design method for the multiple-input multiple-output radar is devised. Unlike the existing methods that only optimize partial metrics or only optimize the short sequence in acceptable time, we propose a comprehensive waveform design method to minimize the weighted sum of almost entirely metrics under the constant modulus constraint. Then, a deep learning framework, named as the comprehensive optimization network, is derived to handle the problem. Numerical results show that the proposed method has superior performance and acceptable optimization time compared with the existing methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TAES.2020.3037406 | IEEE Transactions on Aerospace and Electronic Systems |
Keywords | DocType | Volume |
Deep learning,multiple-input multiple-output (MIMO) radar,waveform design | Journal | 57 |
Issue | ISSN | Citations |
2 | 0018-9251 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinfeng Hu | 1 | 13 | 5.48 |
| Zhiyong Wei | 2 | 0 | 0.34 |
| Yuzhi Li | 3 | 0 | 0.34 |
| Huiyong Li | 4 | 5 | 6.15 |
| jie wu | 5 | 327 | 47.55 |