We consider the problem of range-Doppler imaging using one-bit automotive LFMCW ¹ or PMCW radar that utilizes one-bit ADC sampling with time-varying thresholds at the receiver. The one-bit sampling technique can significantly reduce the cost as well as the power consumption of automotive radar systems. We formulate the one-bit LFMCW/PMCW radar range-Doppler imaging problem as one-bit sparse parameter estimation. The recently proposed hyperparameter-free (and hence user friendly) weighted SPICE algorithms, including SPICE, LIKES, SLIM and IAA, achieve excellent parameter estimation performance for data sampled with high precision. However, these algorithms cannot be used directly for one-bit data. In this paper we first present a regularized minimization algorithm, referred to as 1bSLIM, for accurate range-Doppler imaging using one-bit radar systems. Then, we describe how to extend the SPICE, LIKES and IAA algorithms to the one-bit data case, and refer to these extensions as 1bSPICE, 1bLIKES and 1bIAA. These one-bit hyperparameter-free algorithms are unified within the one-bit weighted SPICE framework. Moreover, efficient implementations of the aforementioned algorithms are investigated that rely heavily on the use of FFTs. Finally, both simulated and experimental examples are provided to demonstrate the effectiveness of the proposed algorithms for range-Doppler imaging using one-bit automotive radar systems.

中文翻译：

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使用一位汽车雷达进行距离多普勒成像的加权 SPICE 算法

我们考虑使用一位汽车 LFMCW ¹的距离多普勒成像问题 或 PMCW 雷达，它利用一位 ADC 采样，在接收器处具有随时间变化的阈值。一位采样技术可以显着降低汽车雷达系统的成本和功耗。我们将一位 LFMCW/PMCW 雷达距离多普勒成像问题表述为一位稀疏参数估计。最近提出的无超参数（因此用户友好）加权 SPICE 算法，包括 SPICE、LIKES、SLIM 和 IAA，为高精度采样的数据实现了出色的参数估计性能。但是，这些算法不能直接用于一位数据。在本文中，我们首先提出了一种称为 1bSLIM 的正则化最小化算法，用于使用一位雷达系统进行精确的距离多普勒成像。然后，我们描述如何扩展 SPICE，LIKES 和 IAA 算法适用于一位数据情况，并将这些扩展称为 1bSPICE、1bLIKES 和 1bIAA。这些一位无超参数算法统一在一位加权 SPICE 框架内。此外，研究了严重依赖于 FFT 的使用的上述算法的有效实现。最后，提供了模拟和实验示例，以证明所提出的算法对使用一位汽车雷达系统进行距离多普勒成像的有效性。