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Analysis of Parametric Adaptive Signal Detection with Applications to Radars and Hyperspectral Imaging

机译：参数自适应信号检测在雷达和高光谱成像中的应用分析

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New parametric space-time adaptive processing (STAP) based detectors are introduced and examined. Unlike conventional techniques that estimate the characteristics of the disturbance from only the secondary data, the proposed detectors obtain such knowledge jointly from the primary and secondary data. When the number of pulses within a coherent processing interval is sufficiently large, the proposed detectors can function even without any secondary data, making them strong candidates for detection in non-homogeneous environments. The proposed detectors are investigated by both theoretical analysis and numerical study using simulated and real radar data. Extensive comparison with conventional STAP methods shows that the proposed detectors can better deal with training-limited scenarios while being computationally simpler. The proposed techniques are also extended for target detection in hyperspectral imaging (HSI).

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