ID 原文 译文
54477 最终，本文对算法分段结果的应用前景进行展望。 Finally, this paper has an outlook on application of segmentation results.
54478 雷达处于低掠射角探测时，干涉、衍射以及遮挡等效应导致电磁波与海面的相互作用更加复杂，海杂波的非均匀、非高斯、非线性特性明显，使得海杂波建模难度大， At low grazing angle, the interaction of electromagnetic wave and the sea surface is more complex due to the interference, diffraction and shade effect. The non-uniform, non-Gaussian and nonlinear characteristics of sea clutter make it difficult to model.
54479 同时，雷达海杂波在多方位下的特性是否存在差异也是一个问题，进一步增大了低掠射条件下海杂波的认知难度。 Furthermore, the characteristic difference of radar sea clutter under multi-azimuth angle increase the cognitive difficulty.
54480 为了提高海杂波的建模精度和多方位特性的认知，亟需开展低掠射雷达海杂波的多方位幅度特性分析。 To better understand sea clutter, multi-azimuth amplitude analysis of sea clutter was carried out at low grazing angle by using the airborne radar data.
54481 在此基础上，利用多个海杂波模型对低掠射、多方位下的海杂波进行拟合优度分析。实现了低掠射角条件下海杂波的多方位特性差异认知，包括不同方位下的带宽和最优海杂波模型。 On the basis, the goodness of fit of sea clutter models was analyzed at different azimuth angles.
54482 实验结果表明，雷达前视状态带宽最窄，侧视最宽; The results show that the bandwidth is the narrowest at forward-looking and widest at side-looking.
54483 对于多方位下的最优海杂波拟合，雷达前视时推荐G0分布，而侧视模式下，海杂波更接近K和GΓD分布。 For multi-azimuth sea clutter modeling, G0 distribution is recommended in forward-looking, and K and GΓD are recommended in side-looking.
54484 多维特征检测技术是提高海面小目标检测的有效途径。 Multi-dimensional feature detection technology is an effective way to improve detection performance of sea-surface small targets.
54485 为了进一步提升海面小目标检测性能，本文提出基于多域多维特征融合的检测方法。 A detection method based on multi-domain and multi-dimensional feature fusion is proposed to further improve performance in this paper.
54486 首先，从时域、频域、时频域、极化域等多域，充分挖掘海杂波和含目标回波的差异性，并将这些差异性表征为多维特征，构建高维特征空间。 First, the differences between sea clutter and target returns are fully explored in time domain, frequency domain, time-frequency domain and polarization domain, which are represented as multi-dimensional features to construct high-dimensional feature space.