通过知识辅助预测建模进行STAP训练[雷达信号处理]

N. Goodman, P. Gurram
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引用次数: 11

摘要

本文研究了一种用于时空自适应处理的干扰协方差矩阵的谱域估计方法。传统的干扰协方差矩阵估计方法是对多个距离箱的空时协方差矩阵求平均值。不幸的是,这些数据快照的光谱内容通常是变化的,这破坏了期望范围的协方差估计。我们建议使用知识来源来识别具有相同底层散射统计的角多普勒光谱区域。然后,我们使用实时数据形成合成孔径雷达图像,该图像本质上是对非移动地杂波的估计。然后,我们平均每个均匀区域内的SAR像素。所得到的杂波功率图与雷达系统和场景几何知识一起用于计算干扰协方差矩阵。通过模拟数据,我们证明了这种技术的潜在性能,证明了它依赖于精确的时空导向向量,并提供了一个使用数据来补偿不完美知识的例子。
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STAP training through knowledge-aided predictive modeling [radar signal processing]
In this paper, we investigate a spectral-domain approach to estimating the interference covariance matrix used in space-time adaptive processing. Traditionally, an estimate of the interference covariance matrix is obtained by averaging the space-time covariance matrices of multiple range bins. Unfortunately, the spectral content of these data snapshots usually varies, which corrupts the covariance estimate for the desired range. We propose to use knowledge sources to identify angle-Doppler spectral regions having the same underlying scattering statistics. Then, we use real-time data to form a synthetic aperture radar image, which is inherently an estimate of non-moving ground clutter. We then average the SAR pixels within each homogeneous region. The resulting clutter power map is used, along with knowledge of the radar system and scenario geometry, to compute the interference covariance matrix. Using simulated data, we demonstrate the potential performance of such a technique, demonstrate its dependence on accurate space-time steering vectors, and provide an example of using data to compensate for imperfect knowledge.
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