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Joint imaging and change detection for robust exploitation in interrupted SAR environments

机译：联合成像和变化检测，可在中断的SAR环境中进行可靠的开发

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摘要

Modern radar systems equipped with agile-beam technology support multiple modes of operation, including, for example, tracking, automated target recognition (ATR), and synthetic aperture radar imaging (SAR). In a multi-mode operating environment, the services compete for radar resources and leave gaps in the coherent collection aperture devoted to SAR imaging. Such gapped collections, referred to as interrupted SAR, typically result in significant image distortion and can substantially degrade subsequent exploitation tasks, such as change detection. In this work we present a new form of exploitation that jointly performs imaging and coherent change detection in interrupted environments. We adopt a Bayesian approach that inherently accommodates different interrupt patterns and compensates for missing data via exploitation of 1) a partially coherent model for reference-pass to mission-pass pixel transitions, and 2) the a priori notion that changes between passes are generally sparse and spatially clustered. We employ approximate message passing for computationally efficient Bayesian inference and demonstrate performance on measured and synthetic SAR data. The results demonstrate near optimal (ungapped) performance with pulse loss rates up to ～ 50% and highlight orders of magnitude reduction in false alarm rates compared to traditional methods.
机译：配备了敏捷波束技术的现代雷达系统支持多种操作模式，包括跟踪，自动目标识别（ATR）和合成孔径雷达成像（SAR）。在多模式操作环境中，服务竞争雷达资源，并在专用于SAR成像的相干收集孔径中留下了空白。这样的间隔收集（称为中断SAR）通常会导致严重的图像失真，并可能大大降低后续的开发任务，例如更改检测。在这项工作中，我们提出了一种新的利用形式，可以在中断的环境中共同执行成像和相干变化检测。我们采用一种贝叶斯方法，该方法固有地适应不同的中断模式并通过利用以下方法补偿丢失的数据：1）用于参考通过到任务通过像素转换的部分相干模型，以及2）通过之间的变化通常是稀疏的先验概念并在空间上聚集。我们将近似消息传递用于计算上高效的贝叶斯推断，并演示对实测和合成SAR数据的性能。结果表明，与传统方法相比，脉冲丢失率高达〜50％时，具有近乎最佳的性能（无缺口），并突出了误报率的数量级降低。

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