Phase-modulated continuous-wave (PMCW) multiple-input multiple-output (MIMO) radar systems are known to possess excellent mutual interference mitigation capabilities, but require costly and power-hungry high sampling rate and high-precision analog-to-digital converters (ADC's). To reduce cost and power consumption, we consider a mixed-ADC architecture, in which most receive antenna outputs are sampled by one-bit ADC's, and only one or a few outputs by high-precision ADC's. We first derive the Cram{é}r-Rao bound (CRB) for the mixed-ADC based PMCW MIMO radar to characterize the best achievable performance of an unbiased target parameter estimator. The CRB analysis demonstrates that the mixed-ADC architecture with a relatively small number of high-precision ADC's and a large number of one-bit ADC's allows us to drastically reduce the hardware cost and power consumption while still maintain a high dynamic range needed for autonomous driving applications. We also introduce a two-step estimator to realize the computationally efficient maximum likelihood (ML) estimation of the target parameters. We formulate the angle-Doppler imaging problem as a sparse parameter estimation problem, and a computationally efficient majorization-minimization (MM) based estimator of sparse parameters, referred to as mLIKES, is devised for accurate angle-Doppler imaging. This is followed by using a relaxation-based approach to cyclically refine the results of mLIKES for accurate off-grid target parameter estimation. Numerical examples are provided to demonstrate the effectiveness of the proposed algorithms for angle-Doppler imaging using mixed-ADC based PMCW MIMO radar.
One-Bit ADCs/DACs based MIMO Radar: Performance Analysis and Joint Design
极低分辨率（例如，一位）模数转换器（ADC）和数模转换器（DAC）可以显著降低MIMO雷达的硬件成本和功耗，特别是具有大规模天线的MIMO雷达。本文主要研究了采用1位ADC和DAC的MIMO雷达的检测性能分析和联合设计。在低信噪比（SNR）和低干扰噪声比（INR）的假设下，推导了1比特MIMO雷达的检测概率（$\mathcal{P}_d$）和虚警概率（$\mathcal{P}_f$）的表达式，并给出了在仅有噪声情况下与无限比特MIMO雷达的理论性能差距。我们还发现，对于固定的$\mathcal{P}_f$，$\mathcal{P}_d$取决于所定义的量化信号与干扰加噪声比（QSINR），其是发送波形和接收滤波器的函数。因此，通过联合设计波形和滤波器来最大化QSINR的优化问题自然出现。对于公式化的问题，我们提出了一个替代的\emph{g}波形式\emph{r} m和过滤\emph{e} r d \emph{e}符号的QSINR最大化\emph{t}离子（GREET）。在GREET的每一次迭代中，接收滤波器通过最小方差无失真响应（MVDR）方法被更新，并且基于乘法器的交替方向方法（ADMM）算法优化1比特波形，其中针对主变量和松弛变量获得闭合形式的解。数值仿真结果与理论分析一致，验证了所提设计算法的有效性。
OTFS-ISAC System with Sub-Nyquist ADC Sampling Rate
Elmore Family School of Electrical and Computer Engineering, Purdue University（普渡大学埃尔莫电气与计算机工程学院）；School of Aeronautics and Astronautics, Purdue University（普渡大学航空航天学院）；Indian Institute of Science（印度科学学院）
