基于频域多通道图特征感知的海面小目标检测

海洋物理环境和电磁环境日趋复杂，海杂波背景下的微弱慢速小目标检测始终是一个研究难点和重点。海面小目标的雷达散射截面积小、回波能量低，传统基于能量的检测方法存在性能瓶颈。基于特征的检测方法聚焦于提取纯杂波和目标回波的差异性特征来实现目标检测，且有效提升了检测性能。该文利用回波数据间频域中幅度的关联性，将图论的方法引入到特征检测中。首先将实测数据进行块白化处理，对海杂波进行一定的抑制，然后在频域提取各多普勒通道下的数据，借助图的处理方法，构建所提取数据的距离邻接矩阵，再转换为拉普拉斯矩阵。该方法计算不同时间序列下拉普拉斯矩阵的最大特征值，并将其与刻画频域能量信息的相对多普勒峰高进行融合，得到新的检验统计量来区分纯杂波和含有目标的回波。通过全相参的X波段(IPIX)实测数据验证，该文所提方法的检测性能更为优越。

Small Target Detection Based on Frequency Domain Multichannel Graph Feature Perception on Sea Surface

The marine physical environment and electromagnetic environment are becoming increasingly complex, making the weak and slow small target detection in the sea clutter background be both emphasis and difficulty of radar target detection research. Due to small radar cross sections and low energy of small targets on the sea surface, traditional energy-based detection methods have a performance bottleneck. Feature-based detection methods focus on extracting distinguishing features between pure sea clutter and target returns to achieve target detection, which improve effectively the detection performance. Using the correlation of amplitude of radar returns in frequency domain, the graph theory method to feature-based detection is introduced. Firstly, measured sea clutter data are block-whitened to suppress sea clutter. Then, the data from Doppler channels are extracted in the frequency domain. With the help of graph processing methods, a distance adjacency matrix of the extracted data is constructed, and then it is converted into a Laplacian matrix. The maximum eigenvalue of Laplacian matrix under different radar returns is calculated, and fused with the relative Doppler peak height, then a new test statistic is obtained. By comparing the value of test statistics, sea clutter and returns with targets can be distinguished. Verified by the measured Ice multiParameter Imaging X-band (IPIX) database, the proposed detector attains better detection performance.