Improved CRB for Millimeter-Wave Radar With 1-Bit ADCs
Improved CRB for Millimeter-Wave Radar With 1-Bit ADCs
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DOI：
10.1109/ojsp.2021.3080213
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发表时间：
2021
影响因子：
2.8
通讯作者：
K. Mazher;A. Mezghani;R. Heath
中科院分区：
文献类型：
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作者：
K. Mazher;A. Mezghani;R. Heath
毫米波广泛用于消费类雷达应用，如自动驾驶汽车中的驾驶员辅助系统和非触摸界面中的手势识别。为了科普毫米波频率下宽带系统硬件复杂度的增加、成本和功耗的提高，我们提出了一种在每个射频链上都具有低分辨率模数转换器（ADC）的全数字架构。低分辨率ADC对雷达参数估计的影响由提出的硬件约束下的Cramér-Rao界（CRB）表征。先前的工作表明，在低信噪比下，与具有理想无限分辨率ADC的系统相比，具有1位ADC的雷达系统在参数估计方面遭受2 dB的性能损失。在本文中，我们设计了一个模拟的预处理单元，波束形成在一个特定的方向，并提高了系统的性能，在可实现的CRB。我们优化了所提出的预处理架构，并表明优化后的网络是通过低成本低分辨率移相器实现的。与采用理想ADC的系统相比，采用优化的预处理器网络后，我们将差距降至1.16 dB。我们证明了所提出的架构的潜力，以满足高分辨率传感的要求，通过分析推导和数值计算的改进CRB，并通过相关性为基础的估计显示其可扩展性。
Millimeter-wave is widely used for consumer radar applications like driver assistance systems in automated vehicles and gesture recognition in touch-free interfaces. To cope with the increased hardware complexity, higher costs and power consumption of wideband systems at millimeter-wave frequencies, we propose a fully digital architecture with low-resolution analog-to-digital converters (ADCs) on each radio-frequency chain. The effect of the low-resolution ADCs on radar parameter estimation is characterized by the Cramér-Rao bound (CRB) under the proposed hardware constraints. Prior work has shown that at low signal-to-noise ratio, a radar system with 1-bit ADCs suffers a performance loss of 2 dB in parameter estimation compared to a system with ideal infinite resolution ADCs. In this paper, we design an analog preprocessing unit that beamforms in a particular direction and improves the system performance in terms of the achievable CRB. We optimize the proposed preprocessing architecture and show that the optimized network is realizable through low-cost low-resolution phase-shifters. With the optimized preprocessor network in the system, we reduce the gap to 1.16 dB compared to a system with ideal ADCs. We demonstrate the potential of the proposed architecture to meet the requirements of high-resolution sensing through analytical derivation and numerical computation of an improved CRB and show its achievability through a correlation-based estimator.