Conventional cross-correlation (CC) and matched filter (MF) algorithms fail to resolve the multipath signals in cases when they are spaced within a Rayleigh resolution limit. Further post-processing of the CC outputs is necessary to improve the performance of CC. In this work, we propose two high-resolution time delay estimation algorithms by post-processing of the CC outputs. The first method applies a Richardson-Lucy deconvolution algorithm used in image deblurring to the CC outputs (Dec-CC). The proposed Dec-CC algorithm yields narrow beams to improve the estimation resolution and accuracy and achieve excellent performance in low signal-to-noise ratio (SNR) environments. The second method reformulates CC as a sparse signal recovery (SSR) problem and estimates time delay via weighted L1 norm minimization (CC-WL1). CC-WL1 also performs excellently for low SNR cases. Simulation results show that the proposed Dec-CC and CC-WL1 algorithms outperform other counterparts.

中文翻译：

---

互相关后处理的高分辨率时延估计算法

常规的互相关（CC）和匹配滤波器（MF）算法无法解决多径信号，如果它们在瑞利分辨率限制内间隔开的话。CC输出的进一步后处理对于改善CC性能是必要的。在这项工作中，我们通过CC输出的后处理提出了两种高分辨率时延估计算法。第一种方法将用于图像去模糊的Richardson-Lucy反卷积算法应用于CC输出（Dec-CC）。提出的Dec-CC算法产生窄波束，以提高估计分辨率和精度，并在低信噪比（SNR）环境中实现出色的性能。第二种方法将CC重新构造为稀疏信号恢复（SSR）问题，并通过加权L1范数最小化（CC-WL1）估计时间延迟。CC-WL1在低SNR情况下也表现出色。仿真结果表明，所提出的Dec-CC和CC-WL1算法优于其他算法。