Volume 12 Issue 5
Oct.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(5): 986-999
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: SHU Yue, FU Dongning, CHEN Zhanye, et al. Super-resolution DOA estimation method for a moving target equipped with a millimeter-wave radar based on RD-ANM[J]. Journal of Radars, 2023, 12(5): 986–999. doi: 10.12000/JR23040
shu

Super-resolution DOA Estimation Method for a Moving Target Equipped with a Millimeter-wave Radar Based on RD-ANM

DOI: 10.12000/JR23040
  • 1.

    School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

  • 2.

    Hwa Create Technology Cop., Ltd., Beijing 100193, China

  • 3.

    HWA-NCUT Radar RF Simulation Laboratory, Beijing 100144, China

  • 4.

    State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China

  • 5.

    Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, School of Information Science and Engineering, Southeast University, Nanjing 210096, China

Funds:  The National Natural Science Foundation of China (62001062, 62271142, 61901112)
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    Abstract
  • Super-resolution Direction of Arrival (DOA) estimation is a critical problem related to vehicle-borne Millimeter-wave radars that needs to be solved to realize accurate target positioning and tracking. Based on the common conditions of limited array aperture, low snapshot, low signal-to-noise ratio, and coherent sources with respect to vehicle-borne scenarios, a super-resolution DOA estimation method for a moving target with an MMW radar based on Range-Doppler Atom Norm Minimize (RD-ANM) is proposed herein. First, an array for receiving signals in the range-Doppler domain is constructed based on the radar echo of the moving target. Then, the compensation vector for the Doppler coupling phase of the moving target is designed to reduce the influence of target motion on DOA estimation. Finally, a multitarget super-resolution DOA estimation method based on the atomic norm framework is proposed herein. Compared to the existing DOA estimation algorithm, the proposed algorithm can achieve higher angular resolution and estimation accuracy owing to low signal-to-noise ratio and single snapshot processing conditions, as well as robust performance in processing coherent sources without sacrificing array aperture. The effectiveness of the proposed algorithm is proven via theoretical analyses, numerical simulations, and experiments.

     

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  • [1]
    ENGELS F, HEIDENREICH P, ZOUBIR A M, et al. Advances in automotive radar: A framework on computationally efficient high-resolution frequency estimation[J]. IEEE Signal Processing Magazine, 2017, 34(2): 36–46. doi: 10.1109/MSP.2016.2637700
    [2]
    GAMBA J. Fundamentals of Radar Systems[M]. GAMBA J. Radar Signal Processing for Autonomous Driving. Singapore: Springer, 2020: 1–14.
    [3]
    LI Xinrong, WANG Xiaodong, YANG Qing, et al. Signal processing for TDM MIMO FMCW millimeter-wave radar sensors[J]. IEEE Access, 2021, 9: 167959–167971. doi: 10.1109/ACCESS.2021.3137387
    [4]
    王永良, 丁前军, 李荣锋. 自适应阵列处理[M]. 北京: 清华大学出版社, 2009.

    WANG Yongliang, DING Qianjun, and LI Rongfeng. Adaptive Array Processing[M]. Beijing: Tsinghua University Press, 2009.
    [5]
    YARDIBI T, LI Jian, STOICA P, et al. Source localization and sensing: A nonparametric iterative adaptive approach based on weighted least squares[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(1): 425–443. doi: 10.1109/TAES.2010.5417172
    [6]
    王永良, 陈辉, 彭应宁, 等. 空间谱估计理论与算法[M]. 北京: 清华大学出版社, 2004.

    WANG Yongliang, CHEN Hui, PENG Yingning, et al. Spatial Spectrum Estimation Theory and Algorithm[M]. Beijing: Tsinghua University Press, 2004.
    [7]
    YANG Zai, XIE Lihua, and ZHANG Cishen. A discretization-free sparse and parametric approach for linear array signal processing[J]. IEEE Transactions on Signal Processing, 2014, 62(19): 4959–4973. doi: 10.1109/TSP.2014.2339792
    [8]
    WU Xiaohuan, ZHU Weiping, and YAN Jun. A toeplitz covariance matrix reconstruction approach for direction-of-arrival estimation[J]. IEEE Transactions on Vehicular Technology, 2017, 66(9): 8223–8237. doi: 10.1109/TVT.2017.2695226
    [9]
    ZHANG Zhe, WANG Yue, and TIAN Zhi. Efficient two-dimensional line spectrum estimation based on decoupled atomic norm minimization[J]. Signal Processing, 2019, 163: 95–106. doi: 10.1016/j.sigpro.2019.04.024
    [10]
    吕明久, 马建朝, 韦旭, 等. 基于矩阵化原子范数的高频雷达距离-多普勒二维估计方法[J]. 电子学报, 2022, 50(5): 1150–1158. doi: 10.12263/DZXB.20210479

    LÜ Mingjiu, MA Jianchao, WEI Xu, et al. Two-dimensional Range-Doppler estimation method for high frequency radar based on matrix atomic norm[J]. Acta Electronica Sinica, 2022, 50(5): 1150–1158. doi: 10.12263/DZXB.20210479
    [11]
    CHANDRASEKARAN V, RECHT B, PARRILO P A, et al. The convex geometry of linear inverse problems[J]. Foundations of Computational Mathematics, 2012, 12(6): 805–849. doi: 10.1007/s10208-012-9135-7
    [12]
    陈栩杉, 张雄伟, 杨吉斌, 等. 如何解决基不匹配问题: 从原子范数到无网格压缩感知[J]. 自动化学报, 2016, 42(3): 335–346. doi: 10.16383/j.aas.2016.c150539

    CHEN Xushan, ZHANG Xiongwei, YANG Jibin, et al. How to overcome basis mismatch: From atomic norm to gridless compressive sensing[J]. Acta Automatica Sinica, 2016, 42(3): 335–346. doi: 10.16383/j.aas.2016.c150539
    [13]
    TANG Gongguo, BHASKAR N B, SHAH P, et al. Compressed sensing off the grid[J]. IEEE Transactions on Information Theory, 2013, 59(11): 7465–7490. doi: 10.1109/TIT.2013.2277451
    [14]
    BHASKAR B N, TANG Gongguo, and RECHT B. Atomic norm denoising with applications to line spectral estimation[J]. IEEE Transactions on Signal Processing, 2013, 61(23): 5987–5999. doi: 10.1109/TSP.2013.2273443
    [15]
    YANG Zai and XIE Lihua. On gridless sparse methods for line spectral estimation from complete and incomplete data[J]. IEEE Transactions on Signal Processing, 2015, 63(12): 3139–3153. doi: 10.1109/TSP.2015.2420541
    [16]
    YANG Zai, LI Jian, STOICA P, et al. Sparse Methods for Direction-of-arrival Estimation[M]. CHELLAPPA R and THEODORIDIS S. Academic Press Library in Signal Processing, Volume 7: Array, Radar and Communications Engineering. Amsterdam: Elsevier, 2018: 509–581. doi: 10.1016/B978-0-12-811887-0.00011-0.
    [17]
    HUANG Yan, ZHANG Hui, GUO Kunpeng, et al. Density-based vehicle detection approach for automotive millimeter-wave radar[C]. The IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT), Shenzhen, China, 2020: 534–537.
    [18]
    BARAL A B and TORLAK M. Joint Doppler frequency and direction of arrival estimation for TDM MIMO automotive radars[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(4): 980–995. doi: 10.1109/JSTSP.2021.3073572
    [19]
    HALPERN D, WILSON H B, and TURCOTTE L H. Chapter 6: Fourier Series and the Fast Fourier Transform[M]. WILSON H B, TURCOTTE L H, and HALPERN D. Advanced Mathematics and Mechanics Applications Using MATLAB. 3rd ed. New York: Chapman and Hall/CRC, 2002.
    [20]
    XU Zhihuo, BAKER C J, and POONI S. Range and Doppler cell migration in wideband automotive radar[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 5527–5536. doi: 10.1109/TVT.2019.2912852
    [21]
    赵春雷, 王亚梁, 毛兴鹏, 等. 基于压缩感知的高频地波雷达二维DOA估计[J]. 系统工程与电子技术, 2017, 39(4): 733–741. doi: 10.3969/j.issn.1001-506X.2017.04.07

    ZHAO Chunlei, WANG Yaliang, MAO Xingpeng, et al. Compressive sensing based two-dimensional DOA estimation for high frequency surface wave radar[J]. Systems Engineering and Electronics, 2017, 39(4): 733–741. doi: 10.3969/j.issn.1001-506X.2017.04.07
    [22]
    YANG Zai, XIE Lihua, and STOICA P. Vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super-resolution[J]. IEEE Transactions on Information Theory, 2016, 62(6): 3685–3701. doi: 10.1109/TIT.2016.2553041
    [23]
    RODRÍGUEZ A F, DE SANTIAGO RODRIGO L, GUILLÉN E L, et al. Coding Prony’s method in MATLAB and applying it to biomedical signal filtering[J]. BMC Bioinformatics, 2018, 19(1): 451. doi: 10.1186/s12859-018-2473-y
    [24]
    吕明久, 陈文峰, 徐芳, 等. 基于原子范数最小化的步进频率ISAR一维高分辨距离成像方法[J]. 电子与信息学报, 2021, 43(8): 2267–2275. doi: 10.11999/JEIT200501

    LÜ Mingjiu, CHEN Wenfeng, XU Fang, et al. One dimensional high resolution range imaging method of stepped frequency ISAR based on atomic norm minimization[J]. Journal of Electronics &Information Technology, 2021, 43(8): 2267–2275. doi: 10.11999/JEIT200501
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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