The spatially and temporally correlated sea clutter with phase information is valuable for marine radar applications. The major difficulty of coherent sea clutter modeling is the generation of the continuous phases. This article presents a new phase retrieval approach for modeling the correlated complex sea clutter based on unsupervised neural networks. The unsupervised short-term and long-term neural networks have been developed for the phase retrieval on different term scales. Both these networks have the same input layer and feature extraction module, and however, the number of output neurons is different. The amplitude sea clutter series and the desired Doppler spectrum are fed into the network in parallel, and their features are extracted by two parallel bidirectional long short-term memory (Bi-LSTM) networks which sufficiently utilize the correlations of sea clutter data. These features are concatenated and fused by a residual network (ResNet). The phases can be successfully obtained by constraining to the desired Doppler spectrum and the given amplitudes of sea clutter series. This proposed approach has been verified by the measured Ice Multiparameter Imaging X-Band (IPIX) radar data, and it can precisely model the complex sea clutter with specified statistic characteristics and Doppler properties. The amplitude root mean square error (RMSE) between the obtained and measured Doppler spectra is only 1.5065 with the interval between adjacent frames equals to 32. The RMSE of Doppler central frequency and spectrum width is 6.9306 and 1.2293 Hz, respectively. It shows robustness with the change of range resolution and interval.

中文翻译：

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基于无监督相位检索的相关复杂海杂波建模

具有相位信息的时空相关的海杂波对于海洋雷达应用非常有价值。相干海杂波建模的主要困难是连续相的生成。本文提出了一种基于无监督神经网络的相关复杂海杂波建模的新相位检索方法。已经开发了无监督的短期和长期神经网络，用于在不同期限范围内进行相位检索。这两个网络具有相同的输入层和特征提取模块，但是输出神经元的数量不同。振幅海杂波序列和所需的多普勒频谱被并行馈入网络，它们的特征是通过两个并行的双向长期短期记忆（Bi-LSTM）网络提取的，这些网络充分利用了海杂波数据的相关性。这些功能由残差网络（ResNet）连接和融合。通过约束所需的多普勒频谱和给定的海浪序列振幅，可以成功获得相位。这项建议的方法已经通过实测的冰多参数成像X波段（IPIX）雷达数据进行了验证，并且可以使用指定的统计特征和多普勒特性精确地模拟复杂的海杂波。所获得和测量的多普勒频谱之间的幅度均方根误差（RMSE）仅1.5065，相邻帧之间的间隔等于32。多普勒中心频率和频谱宽度的均方根误差（RMSE）为6.9306和1.2293 Hz，分别。随着距离分辨率和间隔的变化，它显示出鲁棒性。