Maximizing the coherent processing interval (CPI) is crucial when performing passive radar detection on weak signal reflections. In practice, however, the CPI is limited by the target movement. In this work, the extent of the range and Doppler migration effects occurring when using a long CPI to integrate the returns from an L-band digital aeronautical communication system (LDACS) based passive radar is studied. In particular, our simulations underline the extensive Doppler migration effect that arises even for non-accelerating targets. To this end, the Keystone transform and fractional Fourier transform techniques are combined with the standard passive radar processing to enable the compensation of both range and Doppler migration effects. This non-model-based approach is, however, shown to have limitations, in particular for low signal-to-noise ratios and/or multitarget scenarios. To address these shortcomings, a novel model-based framework that allows to perform joint target detection and parameter estimation is developed. For this, a super-resolution sparse Bayesian learning approach is employed. This technique uses a multitarget observation model, which accurately accounts for the underlying range and Doppler migration effects and provides super-resolution estimation capabilities. This is particularly advantageous in the LDACS case since the narrow bandwidth generally limits the separation of closely spaced targets. The simulation experiments demonstrate the effectiveness of the algorithm and the advantages it provides when compared to the standard migration compensation approach.

中文翻译：

---

使用超分辨率稀疏贝叶斯学习在无源雷达系统中进行长相干集成


对微弱信号反射执行无源雷达检测时，最大化相干处理间隔 (CPI) 至关重要。但实际上，CPI 受到目标变动的限制。在这项工作中，研究了使用长 CPI 对基于 L 波段数字航空通信系统 (LDACS) 的无源雷达的回波进行积分时发生的距离范围和多普勒偏移效应。特别是，我们的模拟强调了即使对于非加速目标也会产生广泛的多普勒偏移效应。为此，梯形变换和分数傅里叶变换技术与标准无源雷达处理相结合，能够补偿距离和多普勒偏移效应。然而，这种非基于模型的方法存在局限性，特别是对于低信噪比和/或多目标场景。为了解决这些缺点，开发了一种新颖的基于模型的框架，该框架允许执行联合目标检测和参数估计。为此，采用超分辨率稀疏贝叶斯学习方法。该技术使用多目标观测模型，可以准确地考虑潜在范围和多普勒偏移效应，并提供超分辨率估计功能。这在 LDACS 情况下特别有利，因为窄带宽通常限制了紧密间隔的目标的分离。仿真实验证明了该算法的有效性及其与标准偏移补偿方法相比所提供的优势。