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Adaptive Radon–Fourier Transform for Weak Radar Target Detection

机译：弱雷达目标检测的自适应Radon-Fourier变换

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摘要

The Radon-Fourier transform (RFT) with a long coherent integration time has recently been proposed for detecting a moving target with an across range cell (ARC) effect. However, without effective clutter suppression, clutter will also be integrated via the RFT, which may affect weak target detection. Based on the maximal signal-to-clutter-plus-noise ratio (SCNR) criteria, a novel adaptive RFT (ARFT) is proposed in this paper to effectively detect a “low-observable” target in a clutter background. The proposed ARFT can combine RFT and adaptive clutter suppression by introducing an optimal filter weight, which is determined from the clutter's covariance matrix as well as a steering vector for a moving target with the ARC effect. In the transformed range-velocity space, the proposed ARFT can suppress background clutter and optimally integrate the target's energy. Nevertheless, with the increase in the integration time, the ARFT needs to address two difficulties in its real implementation. One is the lack of independently and identically distributed (i.i.d.) training samples in a heterogeneous clutter background, and the other is that the computational complexity is too high due to the large number of pulse samples. Therefore, a subaperture ARFT (SA-ARFT) is further proposed in this paper. It divides all coherent pulse samples into several subapertures and accomplishes adaptive clutter suppression in each subaperture. Subsequently, SA-ARFT implements coherent integration among the outputs of different subapertures. The proposed SA-ARFT method can obtain a similar SCNR improvement factor (SCNR IF) with a large number of i.i.d. training samples, while it can obtain a much higher SCNR IF than the ARFT with limited i.i.d. training samples and much lower computational complexity in a heterogeneous clutter background. Finally, some numerical results are provided to demonstrate the effectiveness of the two proposed methods.
机译：最近，提出了具有长相干积分时间的Radon-Fourier变换（RFT），用于检测具有跨范围像元（ARC）效应的运动目标。但是，如果没有有效的杂波抑制功能，杂波也会通过RFT进行整合，这可能会影响弱目标检测。基于最大信噪比（SCNR）标准，本文提出了一种新型自适应RFT（ARFT），可有效检测杂波背景下的“低可观察”目标。所提出的ARFT可以通过引入最佳滤波器权重来结合RFT和自适应杂波抑制，该最佳滤波器权重由杂波的协方差矩阵以及具有ARC效果的运动目标的转向矢量确定。在变换后的速度范围内，提出的ARFT可以抑制背景杂波并优化地整合目标能量。然而，随着集成时间的增加，ARFT需要在实际实施中解决两个困难。一种是在异构背景下缺乏独立且均匀分布的（i.d.）训练样本，另一种是由于大量的脉冲样本而导致计算复杂度过高。因此，本文进一步提出了一种亚孔径ARFT（SA-ARFT）。它将所有相干脉冲样本分为几个子孔径，并在每个子孔径中实现自适应杂波抑制。随后，SA-ARFT在不同子孔径的输出之间实现了一致的集成。提出的SA-ARFT方法可以获得大量i.i.d的相似SCNR改进因子（SCNR IF）。训练样本，尽管它可以获得比iFT有限的ARFT高得多的SCNR IF。在异构背景下训练样本并降低计算复杂度。最后，提供了一些数值结果来证明这两种方法的有效性。

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