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<p>As a core challenge in array signal processing, Direction of Arrival (DOA) estimation is vital for wireless communications, radar, navigation, and autonomous driving. The Hybrid Analog-Digital Structure (HADS), an emerging millimeter-wave technology, cuts power consumption and hardware costs significantly. This paper proposes a novel DOA estimation framework that realizes systematic collaborative design of one-bit Analog-to-Digital Converters (ADCs) and sparse antenna arrays under HADS. It addresses two key challenges simultaneously: hardware efficiency and spatial resolution. To mitigate severe nonlinear distortion from one-bit quantization, we reconstruct the Autocorrelation Function (ACF) from quantized data. The DOA estimation is then formulated as convex optimization, ensuring computational efficiency and accuracy. Numerical simulations show the method compensates for quantization-induced performance loss via sparse arrays’ high degrees of freedom. It enables accurate estimation in underdetermined scenarios where sources exceed sensors. It also outperforms traditional one-bit Multiple Signal Classification (MUSIC), offering an efficient solution for low-power, high-resolution DOA estimation.</p>

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Direction of Arrival Estimation in Hybrid Analog-Digital Systems with One-Bit ADCs and Sparse Arrays

  • 期刊论文

  • 发表日期：

    2026年3月22日

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  • Cheng Liu,
  • Anqi Yan,
  • Yan Zhou,
  • YaXin Xiao,
  • Siyu Yang,
  • Chuangrui Meng

摘要

As a core challenge in array signal processing, Direction of Arrival (DOA) estimation is vital for wireless communications, radar, navigation, and autonomous driving. The Hybrid Analog-Digital Structure (HADS), an emerging millimeter-wave technology, cuts power consumption and hardware costs significantly. This paper proposes a novel DOA estimation framework that realizes systematic collaborative design of one-bit Analog-to-Digital Converters (ADCs) and sparse antenna arrays under HADS. It addresses two key challenges simultaneously: hardware efficiency and spatial resolution. To mitigate severe nonlinear distortion from one-bit quantization, we reconstruct the Autocorrelation Function (ACF) from quantized data. The DOA estimation is then formulated as convex optimization, ensuring computational efficiency and accuracy. Numerical simulations show the method compensates for quantization-induced performance loss via sparse arrays’ high degrees of freedom. It enables accurate estimation in underdetermined scenarios where sources exceed sensors. It also outperforms traditional one-bit Multiple Signal Classification (MUSIC), offering an efficient solution for low-power, high-resolution DOA estimation.