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Hybrid integration for highly maneuvering radar target detection based on generalized radon-fourier transform

机译：基于广义ra-傅里叶变换的高机动雷达目标检测混合集成

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摘要

It is known that long-time integration is an effective method for improving the detection performance ofweak and maneuvering radar targets.However, with the increase of integration time, the problems of across range unit (ARU) and across Doppler unit (ADU) may severely limit the integration performance for high-speed and maneuvering targets. The generalized Radon-Fourier transform (GRFT) can optimally deal with the above problems, but its implementation needs an ergodic multidimensional search. To reduce the complexity, a novel hybrid integration approach is proposed in this paper to speed up the realization of GRFT. It divides a long integration time into several subapertures and implements coherent integration for each subaperture via Doppler filter banks. Subsequently, it accomplishes noncoherent integration among all subapertures with compensation of high-order ARU and ADU motions via generalized Radon transform. Due to the hybrid integration over a long time, the proposed method strikes a balance between integration performance and computational efficiency for the detection of high-speed and highly maneuvering targets. Finally, some results of numerical experiments are provided to demonstrate the effectiveness of the proposed method.
机译：众所周知，长时间积分是提高弱小机动目标探测性能的有效方法。但是，随着积分时间的增加，跨距单元（ARU）和多普勒单元（ADU）的问题可能会更加严重。限制了高速机动目标的集成性能。广义Radon-Fourier变换（GRFT）可以最佳地解决上述问题，但是其实现需要遍历多维搜索。为了降低复杂度，本文提出了一种新颖的混合集成方法，以加快GRFT的实现。它将较长的积分时间划分为几个子孔径，并通过多普勒滤波器组为每个子孔径实现相干积分。随后，它通过广义Radon变换补偿高阶ARU和ADU运动，从而完成所有子孔径之间的非相干积分。由于长期以来的混合集成，该方法在检测高速和高机动目标的集成性能和计算效率之间取得了平衡。最后，提供了一些数值实验结果，证明了该方法的有效性。

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