Guo Liang, Zhang Xin, Yao Di, Yang Qiang, Bai Yang, Deng Weibo
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
Key Laboratory of Marine Environmental Monitoring and Information Processing, Ministry of Industry and Information Technology, Harbin 150001, China.
Sensors (Basel). 2020 Jul 5;20(13):3773. doi: 10.3390/s20133773.
Due to the motion of the platform, the spectrum of first-order sea clutter will widen and mask low-velocity targets such as ships in shipborne high-frequency surface-wave radar (HFSWR). Limited by the quantity of qualified training samples, the performance of the generally used clutter-suppression method, space-time adaptive processing (STAP) degrades in shipborne HFSWR. To deal with this problem, an innovative training sample acquisition method is proposed, in the area of joint domain localized (JDL) reduced-rank STAP. In this clutter-suppression method, based on a single range of cell data, the unscented transformation is introduced as a preprocessing step to obtain adequate homogeneous secondary data and roughly estimated clutter covariance matrix (CCM). The accurate CCM is calculated by integrating the approximate CCM of different range of cells. Compared with existing clutter-suppression algorithms for shipborne HFSWR, the proposed approach has a better signal-to-clutter-plus-noise ratio (SCNR) improvement tested by real data.
由于平台的运动，舰载高频地波雷达（HFSWR）中一阶海杂波的频谱会变宽，并掩盖诸如船只等低速目标。受合格训练样本数量的限制，舰载HFSWR中常用的杂波抑制方法——空时自适应处理（STAP）的性能会下降。为了解决这个问题，在联合域局部化（JDL）降秩STAP领域提出了一种创新的训练样本获取方法。在这种杂波抑制方法中，基于单个距离单元的数据，引入无迹变换作为预处理步骤，以获得足够的均匀辅助数据和粗略估计的杂波协方差矩阵（CCM）。通过整合不同距离单元的近似CCM来计算精确的CCM。与现有的舰载HFSWR杂波抑制算法相比，该方法经实际数据测试具有更好的信杂噪比（SCNR）改善效果。
