基于有色载入技术的天基雷达信号处理方法

Signal Process Algorithm for Space-Based Radar Based on Colored-Loading

为了消除天基监视雷达非稳态杂波图景的影响,文中讨论一种利用有色载入(Colored-loading,CL)技术计算杂波协方差矩阵的空-时自适应处理(Space-time adaptive processing,STAP)方法。首先根据先前相干处理间隔(Coherent process interval,CPI)立体数据和先验知识,对杂波区域分块定位。并根据最大似然估计准则,估计各杂波块回波强度。对杂波回波强度进行归一化,并在多个CPI上取平均,得到杂波反射特性地图。结合该地图和先验知识,计算当前CPI的杂波协方差修正矩阵,将该修正矩阵有色载入当前CPI的杂波协方差矩阵,并对有色载入后的协方差矩阵进行主要特征值分析和重构。仿真结果表明,在天基雷达杂波环境中,该知识辅助CLSTAP算法远优于传统的滑窗处理(Sliding window processing,SWP)STAP算法性能,且与理想STAP算法的性能接近。

To eliminate the effect of non-stationary clutter scene in application of space-based radar （SBR）, a space-time adaptive processing （STAP） algorithm based on colored-loading （CL） process is derived. Scatter points are located according to past CPI（Coherent process interval） data cube and priority knowledge firstly. Then clutter return strengths are estimated by maximum likelihood estimator, and clutter reflectivity map is derived by normalizing return strengths and averaging the results from multiple CPIs. The clutter covarianee correction matrix of current-CPI is calculated by the map and priority knowledge. The correction matrix is loaded to clutter eovarianee estimated matrix of current- CPI. The corrected eovarianee matrix is reconstructed according to its dominant eigenvalues and eigenvectors. Experimental results show that the performance of knowledge-aids CL STAP case is notably better than that of conventional sliding window processing（SWP） STAP case in the scene of SBR,and it is similar to that of ideal STAP case.