In this letter, we deal with the problem of high-resolution time delay estimation (TDE) in multipath environments exploiting the matched filter (MF) outputs data. To this end, we develop a systematic post-processing framework, consisting of two sparsity-based algorithms and a refining procedure aimed at reducing the computational load. The TDE problem is formulated as a sparse signal recovery problem and efficiently solved resorting to a majorization-minimization paradigm and a cyclic procedure. At the design stage, we assume a complex-valued Gaussian distribution model for the MF samples and incorporate a module-product prior that promotes the sparsity more significantly than the conventional complex Laplacian distribution. The preliminary performance assessment, conducted on simulated data, shows that, at least for the considered parameter values, the proposed delay estimators approach the Cramér-Rao bound for different signal-to-noise ratios and bandwidths.

中文翻译：

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通过匹配滤波器输出的基于稀疏性的时延估计

在这封信中，我们利用匹配滤波器 (MF) 输出数据处理多路径环境中的高分辨率时间延迟估计 (TDE) 问题。为此，我们开发了一个系统的后处理框架，由两个基于稀疏性的算法和一个旨在减少计算负载的细化程序组成。TDE 问题被表述为稀疏信号恢复问题，并通过大化-最小化范式和循环过程有效地解决。在设计阶段，我们为 MF 样本假设一个复值高斯分布模型，并结合一个模块积先验，它比传统的复数拉普拉斯分布更显着地促进稀疏性。对模拟数据进行的初步绩效评估表明，