The study of sea clutter reflectivity plays an important role in radar performance evaluations in the military industry. The industrial bodies are trying to apply the sea clutter intelligent processing technology to the radar system with the form of Internet of Things and Industry 4.0. Many sea clutter reflectivity models that have been proposed are difficult to fully adapt to the surrounding seas and different radar systems in China. This article proposes a model named multi-source input neural network (MSINN) sea clutter model using sea clutter collected by ultra high frequency (UHF) radar. In order to prepare sea clutter reflectivity data for training MSINN, the radar continuously collects sea clutter containing various disturbances. In the face of the challenges of preprocessing and storage of measured sea clutter big data, this article proposes a sea clutter preprocessing scheme based on yolov3-tiny model. Experimental results show that the average detection precision of test sea clutter Range-Pulse (RP) images is 75.3% and the effective region extraction time of sea clutter RP image is 0.003642 s, which can meet the requirement of real-time detection and data requirement of predicting sea clutter reflectivity based on MSINN. Compared with the traditional empirical model, the average prediction error of sea clutter reflectivity based on MSINN is 1.82 dB, which improves the prediction accuracy and is more suitable for the Yellow Sea in China.

Measurement of shipborne radar sea echo is instrumental in collecting the sea clutter data in open sea areas. However, the ship movement would introduce an extra Doppler component into the spectrum of the sea clutter, so the sea clutter inherent spectrum must be estimated prior to investigating the sea clutter Doppler characteristics from the shipborne radar sea echo. In this paper we show some results about a shipborne sea clutter measurement experiment that was conducted in the South China Sea in a period between 2017 and 2018; abundant clutter data have been collected by using a shipborne S-band clutter measurement radar. To obtain the sea clutter inherent Doppler spectrum from these data, an estimation method, based on the mapping relationship between the shipborne clutter spectrum and the inherent clutter spectrum, is proposed. This method is validated by shipborne clutter data sets under the same measuring conditions except for the ship speed. Using this method, the characteristics of the Doppler spectrum lineshapes in the South China Sea are calculated and analyzed according to different sea states, wave directions, and radar resolutions, which can be instrumental in designing the radar target detection algorithms.

海杂波的多普勒谱建模对采用多普勒处理技术的雷达进行有效的海杂波抑制和目标检测具有重要的意义.本文分别考虑Bragg，白冠和破碎波三种散射机制对应的多普勒谱分量的特征，对三种谱分量的频移和展宽进行分离，并引入附加速度频移量，提出了基于不同散射机制特征的雷达海杂波时变多普勒谱模型.该模型假设谱强度为受观测时间区间影响的随机变量，能够同时适用于平均多普勒谱与短时多普勒谱建模.通过分别对黄海海域实测的岸基P，S波段海杂波平均多普勒谱与短时多普勒谱建模测试，结果表明该模型相对传统模型的建模精度更高，尤其体现在短时谱的观测时间较长和平均谱形式较为复杂的情况下，建模误差显著减小.