Volume 9 Issue 5
Oct.  2020
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Article Navigation >  Journal of Radars  > 2020  >  9(5): 878-885
CUI Lei, QIU Xiaolan, GUO Jiayi, et al. Multi-channel phase error estimation method based on an error backpropagation algorithm for a multichannel SAR[J]. Journal of Radars, 2020, 9(5): 878–885. doi: 10.12000/JR20096
Citation: CUI Lei, QIU Xiaolan, GUO Jiayi, et al. Multi-channel phase error estimation method based on an error backpropagation algorithm for a multichannel SAR[J]. Journal of Radars, 2020, 9(5): 878–885. doi: 10.12000/JR20096
shu

Multi-channel Phase Error Estimation Method Based on an Error Backpropagation Algorithm for a Multichannel SAR

DOI: 10.12000/JR20096
  • 1.

    Institute of Electronics, Chinese Academy of Sciences, Suzhou Research Center, Suzhou 215123, China

  • 2.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    National Key Lab of Microwave Imaging Technology, Beijing 100190, China

Funds:  The National Natural Science Foundation of China (61991420, 61991421)
More Information
  • Corresponding author: CUI lei, cuilei1167@163.com; QIU Xiaolan, xlqiu@mail.ie.ac.cn
  • Received Date: 2020-07-07
  • Rev Recd Date: 2020-09-04
    • Available Online: 2020-09-28
  • Publish Date: 2020-10-28
    Abstract
  • An azimuth multi-channel Synthetic Aperture Radar (SAR) can be used to obtain high-resolution wide-swath SAR images. Accurate estimation of the phase error between channels is the key to ensuring image quality. In this study, we present a channel phase error estimation method based on the error backpropagation algorithm. During the physical process of a multi-channel SAR echo generation, this method constructs an observation matrix with the parameters to be estimated including the phase error between channels. The initial SAR echo is generated using the initial channel error matrix and initial target scattering coefficient matrix, and the error between the echo and measured multi-channel SAR echo is calculated. Using the backpropagation algorithm commonly used in deep learning, the abovementioned parameters are continuously trained and optimized. Finally, the estimation of the phase error between channels is obtained along with the target scattering coefficient. This method combines the error backpropagation method with the principle of multi-channel SAR channel error. Phase estimation and imaging are realized based on the sparsity assumption, which provides a new approach for estimating an error in a multi-channel SAR. The effectiveness of the presented method is validated using multi-channel SAR simulation data.

     

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