The frequency and attenuation rate of a resonance in the late-time return of a radar signal are indicative of a target's geometry and conductivity, and hence they can be used as features in a variety of filtering and classification applications. However, late-time returns are typically observed over short windows at low signal-to-noise ratios (SNRs, averaged over the window), and often in the presence of sampling jitter. This can make the estimation of these parameters difficult, even when multiple measurement shots are available. In this article, we develop a new multi-shot estimation method that is based on models for the distribution of the roots of the z-transform of the received signal. Under an additive-Gaussian-noise model, we have a closed-form expression for the root distribution in terms of the resonance parameters, and the parameters are estimated by matching the model distribution to the empirical distribution. The root distribution has a strong dependence on the frequency and attenuation rate, and leads to significantly better estimates than existing techniques at low SNRs. By developing approximate models, we extend these performance advantages to scenarios with significant sampling jitter and synchronization offsets.

中文翻译：

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估计低 SNR 延迟时间雷达回波中的谐振与采样抖动


雷达信号后期返回时谐振的频率和衰减率表示目标的几何形状和电导率，因此它们可以用作各种滤波和分类应用中的特征。然而，延迟时间返回通常是在短窗口上以低信噪比 （SNR，窗口上的平均值） 观察到的，并且经常在存在采样抖动的情况下。这会使这些参数的估计变得困难，即使有多个测量镜头可用。在本文中，我们开发了一种新的多脉冲估计方法，该方法基于接收信号的 z 变换根分布模型。在加性高斯噪声模型下，我们有一个关于谐振参数的根分布的闭式表达式，并且通过将模型分布与经验分布相匹配来估计参数。根分布对频率和衰减率有很强的依赖性，并且在低 SNR 下导致比现有技术更好的估计。通过开发近似模型，我们将这些性能优势扩展到具有明显采样抖动和同步偏移的场景。