Loading...
Please wait, while we are loading the content...
Omega-KA-Net: A SAR Ground Moving Target Imaging Network Based on Trainable Omega-K Algorithm and Sparse Optimization
| Content Provider | MDPI |
|---|---|
| Author | Zhang, Hongwei Ni, Jiacheng Xiong, Shichao Luo, Ying Zhang, Qun |
| Copyright Year | 2022 |
| Description | The ground moving target (GMT) is defocused due to unknown motion parameters in synthetic aperture radar (SAR) imaging. Although the conventional Omega-K algorithm (Omega-KA) has been proven to be applicable for GMT imaging, its disadvantages are slow imaging speed, obvious sidelobe interference, and high computational complexity. To solve the above problems, a SAR-GMT imaging network is proposed based on trainable Omega-KA and sparse optimization. Specifically, we propose a two-dimensional (2-D) sparse imaging model deducted from the Omega-KA focusing process. Then, a recurrent neural network (RNN) based on an iterative optimization algorithm is built to learn the trainable parameters of Omega-KA by an off-line supervised training method, and the solving process of the sparse imaging model is mapped to each layer of the RNN. The proposed trainable Omega-KA network (Omega-KA-net) forms a new GMT imaging method that can be applied to high-quality imaging under down-sampling and a low signal to noise ratio (SNR) while saving the imaging time substantially. The experiments of simulation data and measured data demonstrate that the Omega-KA-net is superior to the conventional algorithms in terms of GMT imaging quality and time. |
| Starting Page | 1664 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14071664 |
| Journal | Remote Sensing |
| Issue Number | 7 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-30 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Synthetic Aperture Radar (sar) Ground Moving Target (gmt) Sparse Imaging Recurrent Neural Networks (rnn) Omega-k |
| Content Type | Text |
| Resource Type | Article |
