论文标题
线光谱通过无限采样
Line Spectral Estimation via Unlimited Sampling
论文作者
论文摘要
频率调制连续波（FMCW）雷达已被广泛应用于汽车反碰撞系统，自动巡航控制和室内监控。但是，当强大和弱目标在应用程序中共存时，传统的类似于数字转换器（ADC）可能会遭受大量信息损失。为了解决这个问题，提出了无限的采样（US）策略，该策略在采样之前应用了模型操作员。在本文中，我们在我们的背景下使用FMCW雷达研究了范围估计问题，可以通过我们通过我们将其作为一维线光谱估计（LSE）进行配合。通过利用过度采样属性并证明可以控制某个频带上的泄漏，我们在傅立叶和一阶差异域中建立了整数优化问题。然后，我们提出了一种基于动态的编程（DP）算法，然后提出正交匹配追踪（OMP）方法来求解它。此外，提出了两个阶段的US LSE（USLSE），其中首先通过迭代执行DP和OMP恢复线光谱信号，然后通过应用最先进的LSE算法来估计参数。实质性的数值模拟和实际实验表明，USLSE所提出的算法优于现有算法。
Frequency Modulated Continuous Wave (FMCW) radar has been widely applied in automotive anti-collision systems, automatic cruise control, and indoor monitoring. However, conventional analog-to-digital converters (ADCs) can suffer from significant information loss when strong and weak targets coexist in ranging applications. To address this issue, the Unlimited Sampling (US) strategy was proposed, which applies a modulo operator prior to sampling. In this paper, we investigate the range estimation problem using FMCW radar in the context of US, which can be formulated as a one-dimensional line spectral estimation (LSE) via US. By exploiting the oversampling property and proving that the leakage onto a certain frequency band can be controlled, we establish an integer optimization problem in the Fourier and first-order difference domain. We then propose a dynamic programming (DP) based algorithm followed by the orthogonal matching pursuit (OMP) method to solve it. In addition, a two-stage US LSE (USLSE) is proposed, where the line spectral signal is first recovered by iteratively executing DP and OMP, and then the parameters are estimated by applying a state-of-the-art LSE algorithm. Substantial numerical simulations and real experiments demonstrate that the proposed algorithm, USLSE, outperforms existing algorithms.
