Name
Abstract | ||
|---|---|---|
Direction of arrival (DOA) estimation plays an important role in Multi-Input and Multi-Output (MIMO) radar signal processing. Traditional methods, such as discrete Fourier transform (DFT) and multiple signal classification (MUSIC), were proposed for DOA estimation in array radar system. However, the disadvantage of traditional methods are low resolution and limitation of radar freedom under many targets situation. In this paper, a deep neural network (DNN) is proposed applying to a real mono-static millimeter wave MIMO radar system. The model performance are evaluated on test dataset after the model converged. Simulation result confirms that our DNN based DOA estimation algorithm is effective under various criteria including signal success detected ratio, root mean square error (RMSE) and area under the curve (AUC). Different signal resolution results verifies the effectiveness of our algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00117 | SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geyu Tang | 1 | 0 | 0.34 |
| Xingyu Gao | 2 | 17 | 2.29 |
| Zhenyu Chen | 3 | 1 | 1.37 |
| Yu Zhang | 4 | 0 | 0.34 |
| Huicai Zhong | 5 | 2 | 1.91 |
| Menggang Li | 6 | 0 | 0.34 |