Low-complexity-based reduced-dimension–multiple-signal classification (RD-MUSIC) is proposed with extrapolation for joint time delay of arrivals (TOA) and direction of arrivals (DOA) at automotive frequency-modulated continuous-wave (FMCW) radar systems. When a vehicle is driving on the road, the automotive FMCW radar can estimate the position of multiple other vehicles, because it can estimate multiple parameters, such as TOA and DOA. Over time, the requirement of the accuracy and resolution parameters of automotive FMCW radar is increasing. To accurately estimate the parameters of multiple vehicles, such as range and angle, it is difficult to use a low-resolution algorithm, such as the two-dimensional fast Fourier transform. To improve parameter estimation performance, high-resolution algorithms, such as the 2D-MUSIC, are required. However, the conventional high-resolution methods have a high complexity and, thus, are not applicable to a real-time radar system for a vehicle. Therefore, in this work, a low-complexity RD-MUSIC with extrapolation algorithm is proposed to have a resolution similar to that of a high-resolution algorithm to estimate the position of other vehicles. Compared with conventional low complexity high resolution, in experimental results, the proposed method had better performance.

中文翻译：

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基于低复杂度的RD-MUSIC，具有外推功能，可用于汽车FMCW雷达系统的TOA和DOA联合

提出了基于低复杂度的降维多信号分类（RD-MUSIC），并推论了汽车调频连续波（FMCW）雷达的联合到达时延（TOA）和到达方向（DOA）系统。当车辆在道路上行驶时，汽车FMCW雷达可以估计其他多个车辆的位置，因为它可以估计多个参数，例如TOA和DOA。随着时间的流逝，对汽车FMCW雷达精度和分辨率参数的要求越来越高。为了准确地估计多个车辆的参数，例如范围和角度，很难使用低分辨率算法，例如二维快速傅立叶变换。为了提高参数估计性能，需要诸如2D-MUSIC的高分辨率算法。然而，传统的高分辨率方法具有很高的复杂度，因此不适用于车辆的实时雷达系统。因此，在这项工作中，提出了一种具有外推算法的低复杂度RD-MUSIC，其分辨率类似于用于估计其他车辆位置的高分辨率算法。与常规的低复杂度高分辨率相比，在实验结果上，该方法具有更好的性能。