Volume 11 Issue 1
Feb.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(1): 95-106
QI Meng, HUANG Lijia, QIU Xiaolan, et al. Method of range ambiguity suppression combining sparse reconstruction and matched filtering[J]. Journal of Radars, 2022, 11(1): 95–106. doi: 10.12000/JR21181
Citation: QI Meng, HUANG Lijia, QIU Xiaolan, et al. Method of range ambiguity suppression combining sparse reconstruction and matched filtering[J]. Journal of Radars, 2022, 11(1): 95–106. doi: 10.12000/JR21181
shu

Method of Range Ambiguity Suppression Combining Sparse Reconstruction and Matched Filtering

doi: 10.12000/JR21181
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Science, Beijing 100094, China

  • 2.

    Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  National Key R&D Program of China (2018YFC1407200)
More Information
  • Corresponding author: HUANG Lijia, iecas8huanglijia@163.com
  • Received Date: 2021-11-17
  • Accepted Date: 2021-12-30
  • Rev Recd Date: 2021-12-29
    • Available Online: 2022-01-10
  • Publish Date: 2022-01-28
    Abstract
  • To a certain extent, SAR images are affected by range ambiguity due to antenna sidelobe characteristics and pulse operating system. The work of range ambiguity suppression focuses on SAR system design and signal processing. One type of idea tries to modify the way of transmitting and receiving to block the receiving of the ambiguous energy, such as multiple elevation beams and azimuth phase coding. The other ideas are algorithms that use signal processing technology to reduce the distance ambiguity energy in echo and image domains. This paper proposes a range ambiguity suppression method that combines sparse reconstruction and matched filtering. The method performs the sparse reconstruction of the ambiguity area, estimates the ambiguity area signal using the ambiguity area image and reconstruction matrix, separates it from the echo signal to obtain the primary image signal after range ambiguity suppression, and uses matched filtering to obtain the main area image. In this method, sparse reconstruction ensures the accuracy of fuzzy signal estimation, and matched filtering ensures the efficiency of imaging processing. Simulation results show that the proposed method can effectively suppress range ambiguity, with a suppression effect of 10 dB or higher, and it has a good ability to maintain the weak targets and the details of the main image.

     

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