In this letter, a cascade algorithm combined subband decomposition with an eigenvalue-based detection scheme is proposed to detect moving targets in sea clutter for the radar system with short pulses. Using a discrete Fourier transform-modulated filter bank, on the one hand, subband decomposition can effectively suppress clutter as well as increase the coherent integration time. On the other hand, it transforms the scene where the target spectrum is separated from the clutter spectrum into the scene where the target spectrum overlaps with the clutter spectrum. In the target subband, the noncoherent method using amplitude difference is more favorable for detection due to the nonobvious phase difference caused by the overlap of the target spectrum and the clutter spectrum. Considering that the maximum eigenvalue can reflect the signal intensity and capture the signal correlations well, the maximum eigenvalue of the covariance matrix is adopted to discriminate the target from the clutter. Finally, the simulation results show that the proposed algorithm achieves superior performance.

中文翻译：

---

海杂波中雷达运动目标的子带最大特征值检测

在本文中，提出了一种将子带分解与基于特征值的检测方案相结合的级联算法，用于短脉冲雷达系统在海杂波中检测运动目标。使用离散傅立叶变换调制滤波器组，一方面，子带分解可以有效抑制杂波并增加相干积分时间。另一方面，它将目标频谱与杂波频谱分离的场景转换为目标频谱与杂波频谱重叠的场景。在目标子带中，由于目标频谱与杂波频谱重叠导致相位差不明显，因此利用幅度差的非相干方法更有利于检测。考虑到最大特征值能很好地反映信号强度并能很好地捕捉信号的相关性，采用协方差矩阵的最大特征值来区分目标和杂波。最后，仿真结果表明所提出的算法具有优越的性能。