Abstract With good electronic counter-countermeasures capability, random pulse repetition interval (PRI) radars receive increasing attention recently. However, the problem of maneuvering target detection in random PRI radars is rarely studied yet. In this problem, the difficulty lies in not only the range migration (RM) and Doppler frequency migration (DFM) effects but also the non-uniform sampling pulses. This paper proposes a novel algorithm for this problem. In the proposed algorithm, we first combine the non-uniform resampling operation and the keystone transform to propose the resampling-keystone transform, which can eliminate the RM and resample the non-uniform sampling pulses into uniform ones in one step. Then, the dechirp process, whose implementation can benefit from the fast Fourier transform, is employed to accomplish coherent integration for target detection by compensating the DFM. The proposed algorithm is applicable for both single-target and multi-target scenarios. Besides, the available integration time and computational complexity of the proposed algorithm are analyzed. Finally, simulation results are given to show that the proposed algorithm can approach the optimal detection performance with a much lower computational cost than the well-known generalized Radon Fourier transform.

中文翻译：

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基于重采样-梯形变换的随机脉冲重复间隔雷达机动目标检测

摘要 随机脉冲重复间隔（PRI）雷达具有良好的电子对抗能力，近年来受到越来越多的关注。然而，关于随机PRI雷达机动目标检测问题的研究还很少。在这个问题中，困难不仅在于距离偏移（RM）和多普勒频率偏移（DFM）效应，还在于非均匀采样脉冲。本文针对这个问题提出了一种新的算法。在所提出的算法中，我们首先将非均匀重采样操作和梯形变换结合起来，提出了重采样-梯形变换，它可以消除RM，一步将非均匀采样脉冲重采样为均匀脉冲。然后， dechirp 过程，其实现可以受益于快速傅立叶变换，通过补偿 DFM 来实现目标检测的相干积分。所提出的算法适用于单目标和多目标场景。此外，分析了该算法的可用积分时间和计算复杂度。最后，仿真结果表明，与众所周知的广义 Radon Fourier 变换相比，所提出的算法可以以低得多的计算成本接近最佳检测性能。