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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Long Coherent Integration in Passive Radar Systems Using Super-Resolution Sparse Bayesian Learning
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Long Coherent Integration in Passive Radar Systems Using Super-Resolution Sparse Bayesian Learning

机译：超分辨率稀疏贝叶斯学习无源雷达系统的长期连贯集成

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摘要

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 nonaccelerating 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 nonmodel-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.
机译：在对弱信号反射上执行被动雷达检测时，最大化相干处理间隔（CPI）是至关重要的。然而，在实践中，CPI受到目标运动的限制。在这项工作中，研究了使用长CPI以集成基于L波段数字航空通信系统（LDACS）的无源雷达的返回时发生的范围和多普勒迁移效应的程度。特别是，我们的模拟强调了即使对于非核化目标也产生了广泛的多普勒迁移效果。为此，梯形转换和分数傅立叶变换技术与标准无源雷达处理相结合，以实现两个范围和多普勒迁移效应的补偿。然而，这种基于非模型的方法是具有限制的，特别是对于低信噪比和/或多数场景。为了解决这些缺点，开发了一种允许执行联合目标检测和参数估计的基于模型的基于模型的框架。为此，采用超分辨率稀疏贝叶斯学习方法。该技术使用多靶观察模型，该模型可准确地占据底层范围和多普勒迁移效果，并提供超分辨率的估计能力。这在LDACS壳体中特别有利，因为窄带宽通常限制紧密间隔目标的分离。仿真实验证明了算法的有效性和与标准迁移补偿方法相比提供的优点。

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