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Stap using knowledge-aided covariance estimation and the fracta algorithm

机译：使用知识辅助协方差估计和分形算法进行装订

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摘要

In the airborne space-time adaptive processing (STAP) setting, a priori information via knowledge-aided covariance estimation (KACE) is employed in order to reduce the required sample support for application to heterogeneous clutter scenarios. The enhanced FRACTA (FRACTA.E) algorithm with KACE as well as Doppler-sensitive adaptive coherence estimation (DS-ACE) is applied to the KASSPER I & II data sets where it is shown via simulation that near-clairvoyant detection performance is maintained with as little as 1/3 of the normally required number of training data samples. The KASSPER I & II data sets are simulated high-fidelity heterogeneous clutter scenarios which possess several groups of dense targets. KACE provides a priori information about the clutter covariance matrix by exploiting approximately known operating parameters about the radar platform such as pulse repetition frequency (PRF), crab angle, and platform velocity. In addition, the DS-ACE detector is presented which provides greater robustness for low sample support by mitigating false alarms from undernulled clutter near the clutter ridge while maintaining sufficient sensitivity away from the clutter ridge to enable effective target detection performance
机译：在机载空时自适应处理（STAP）设置中，采用了通过知识辅助协方差估计（KACE）的先验信息，以减少应用于异构杂波场景所需的样本支持。带有KACE的增强型FRACTA（FRACTA.E）算法以及对多普勒敏感的自适应相干估计（DS-ACE）被应用于KASSPER I和II数据集，通过仿真表明，该方法可保持近乎透视的检测性能仅为通常所需训练数据样本数量的1/3。 KASSPER I和II数据集是模拟的高保真异类杂物场景，其中包含几组密集的目标。 KACE通过利用有关雷达平台的已知操作参数（例如脉冲重复频率（PRF），蟹角和平台速度）来提供有关杂波协方差矩阵的先验信息。此外，提出了DS-ACE检测器，该检测器通过减轻杂波脊附近零位杂波的误报，同时保持远离杂波脊的足够灵敏度以实现有效的目标检测性能，从而为低样本支持量提供了更高的鲁棒性

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