To achieve high resolution, orthogonal frequency division multiplex (OFDM) radars deploy two-dimensional multiple signal classification (2D-MUSIC) in the joint range-velocity estimator. However, it is obvious that both of the signal reconstruction and 2D smoothing affect the noise statistical distribution and virtual array aperture in the joint range-velocity estimator with 2D-MUSIC. Therefore, the conventional accuracy analysis methods for the MUSIC are no longer suitable. In this paper, we propose an estimation accuracy analysis for the joint range-velocity estimator with 2D-MUSIC. Firstly, we present that the noise statistical distribution of the signals being reconstructed and 2D smoothed has a 2-fold Hankel structure. Then, the closed-forms of the range and velocity estimation accuracies are derived, respectively, which both show that the estimation accuracies highly depend on the 2D smoothing and could be improved by properly choosing the 2D smoothing window. Further, we formulate the smoothing optimization and propose the quasi-optimal size of the 2D smoothing window. Finally, both theoretical analyses and simulations validate that the proposed smoothing optimization could significantly improve the estimation accuracies with a lower computational burden than the conventional smoothing.

中文翻译：

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OFDM 雷达中 2D-MUSIC 联合距离速度估计器的性能分析


为了实现高分辨率，正交频分复用 (OFDM) 雷达在联合距离-速度估计器中部署二维多信号分类 (2D-MUSIC)。然而，很明显，信号重构和2D平滑都会影响2D-MUSIC联合距离-速度估计器中的噪声统计分布和虚拟阵列孔径。因此，传统的MUSIC精度分析方法已经不再适用。在本文中，我们提出了 2D-MUSIC 联合距离-速度估计器的估计精度分析。首先，我们提出被重建和二维平滑的信号的噪声统计分布具有2倍汉克尔结构。然后，分别推导了距离和速度估计精度的闭合形式，这都表明估计精度高度依赖于二维平滑，并且可以通过适当选择二维平滑窗口来提高估计精度。此外，我们制定了平滑优化并提出了 2D 平滑窗口的准最优大小。最后，理论分析和仿真都验证了所提出的平滑优化可以显着提高估计精度，并且比传统平滑的计算负担更低。