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Volume 12 Issue 5
Oct.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(5): 971-985
SONG Jiaqi and TAO Haihong. A fast parameter estimation algorithm for near-field non-circular signals[J]. Journal of Radars, 2020, 9(4): 632–639. doi: 10.12000/JR20053
Citation: LI Yi, XIA Weijie, ZHOU Jianjiang, et al. A range-angle joint imaging algorithm for automotive radar systems based on Doppler domain compensation[J]. Journal of Radars, 2023, 12(5): 971–985. doi: 10.12000/JR23097
shu

A Range-angle Joint Imaging Algorithm for Automotive Radar Systems Based on Doppler Domain Compensation

DOI: 10.12000/JR23097
  • 1.

    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China

  • 2.

    Nanjing Chuhang Technology Co., Ltd., Nanjing 211100, China

Funds:  Special Funds for Transformation of Scientific and Technological Achievements in Jiangsu Province, China (BA2021079)
More Information
  • Corresponding author: XIA Weijie, nuaaxwj@nuaa.edu.cn
  • Received Date: 2023-05-29
  • Rev Recd Date: 2023-07-30
    • Available Online: 2023-08-04
  • Publish Date: 2023-09-04
    Abstract
  • Single snapshot forward-looking imaging technology with high performance and resolution is crucial for enabling the development of automotive radars. However, range migration issues can limit the implementation of coherent integration methods, and improving system resolution is generally difficult due to hardware parameter limitations. Based on the Time-Division Multiplexing Multiple-Input-Multiple-Output (TDM-MIMO) forward-looking imaging systems of automotive millimeter wave radar, this paper proposes Doppler domain compensation and point-to-point echo correction measures for achieving multidomain signal decoupling. However, the accuracy of traditional single-dimension range and angle imaging is limited by the number of finite array elements and significant noise interference. Therefore, this paper proposes a multidomain joint estimation algorithm based on the Improved Bayesian Matching Pursuit (IBMP) method. The Bayesian method is based on the Bernoulli-Gaussian (BG) model, and the estimated parameters and support domain are iteratively updated in this method while adhering to the Maximum a Posteriori (MAP) criterion constraint to achieve the high-precision reconstruction of multidimensional joint signals. The final set of simulation and actual measurement results demonstrate that the proposed method can effectively solve the problem of range migration and improve the angle resolution of radar forward-looking imaging while exhibiting excellent noise robustness.

     

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