We investigate the one-bit MIMO (1b-MIMO) radar that performs one-bit sampling with a time-varying threshold in the temporal domain and employs compressive sensing in the spatial and Doppler domains. The goals are to significantly reduce the hardware cost, energy consumption, and amount of stored data. The joint angle and Doppler frequency estimations from noisy onebit data are studied. By showing that the effect of noise on one-bit sampling is equivalent to that of sparse impulsive perturbations, we formulate the one-bit ℓ 1 -regularized atomic-norm minimization (1b-ANM-L1) problem to achieve gridless parameter estimation with high accuracy. We also develop an iterative method for solving the 1b-ANM-L1 problem via the alternating direction method of multipliers. The Cramér-Rao bound (CRB) of the 1b-MIMO radar is analyzed, and the analytical performance of one-bit sampling with two different threshold strategies is discussed. Numerical experiments are presented to show that the 1b-MIMO radar can achieve high-resolution parameter estimation with a largely reduced amount of data.

中文翻译：

---

具有时变阈值的一位 MIMO 雷达的无网格参数估计


我们研究了一位 MIMO (1b-MIMO) 雷达，该雷达在时域中以时变阈值执行一位采样，并在空间域和多普勒域中采用压缩感知。目标是显着降低硬件成本、能耗和存储数据量。研究了来自噪声单比特数据的关节角度和多普勒频率估计。通过证明噪声对一位采样的影响等效于稀疏脉冲扰动的影响，我们提出了一位 ℓ 1 -正则化原子范数最小化 (1b-ANM-L1) 问题，以实现高无网格参数估计。准确性。我们还开发了一种迭代方法，通过乘法器的交替方向方法来解决 1b-ANM-L1 问题。分析了1b-MIMO雷达的Cramér-Rao界（CRB），并讨论了两种不同阈值策略下的一位采样的分析性能。数值实验表明，1b-MIMO雷达可以在大幅减少数据量的情况下实现高分辨率参数估计。