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LIU Qi, GUO Rui, WANG Jiajia, et al. A high-accuracy beamspace DOA estimation method for low-elevation angle targets[J]. Journal of Radars, in press. doi: 10.12000/JR25173
Citation: LIU Qi, GUO Rui, WANG Jiajia, et al. A high-accuracy beamspace DOA estimation method for low-elevation angle targets[J]. Journal of Radars, in press. doi: 10.12000/JR25173
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A High-accuracy Beamspace DOA Estimation Method for Low-elevation Angle Targets

DOI: 10.12000/JR25173 CSTR: 32380.14.JR25173

  • School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds:  The National Natural Science Foundation of China (U2133216), Shenzhen Science and Technology Program (KQTD20190929172704911), Guangdong Provincial Science and Technology Program (2019ZT08X751), Science and Technology Planning Project of Key Laboratory of Advanced Intellisense Technology, Guangdong Science and Technology Department (2023B1212060024)
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  • Corresponding author: GUO Rui, guor29@mail.sysu.edu.cn
  • Received Date: 2025-09-10
  • Rev Recd Date: 2025-12-13
    • Available Online: 2025-12-24
    Abstract
  • Direction of Arrival (DOA) estimation for low-elevation angle targets is a critical challenge in meter-wave and holographic staring radar systems, as its accuracy directly affects target height measurement performance. Traditional beamspace methods reduce computational complexity by projecting high-dimensional element-space data onto a low-dimensional beamspace using a beamformer. However, this lossy mapping leads to partial information loss, resulting in degraded elevation-angle estimation accuracy compared to that of element-space methods. To address this issue, this study proposes a high-accuracy beamspace DOA estimation method for low-elevation angle targets. First, the Cramér-Rao Bound (CRB) for both element-space and beamspace DOA estimation is derived, and the conditions under which these bounds are equal are analyzed. Since these conditions are difficult to satisfy in practical scenarios, an approximate-condition-based beamformer design strategy is developed to reduce data dimensionality while preserving effective target information. Finally, precise elevation-angle estimation is achieved using the maximum likelihood criterion. Simulation and experimental results show that the proposed method significantly reduces data dimensionality while maintaining estimation accuracy comparable to that of element-space methods at low-elevation angles, clearly outperforming existing beamspace algorithms.

     

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