摘要
本文介绍了两种在广义旁瓣相消器结构上的降秩空时自适应处理方法:主分量法和互谱法,这两种方法是利用杂波和干扰的低秩属性,应用采样数据来构造降维转换矩阵。最后对MountainTop计划进行了详细的描述,并对从MountainTop计划测得的数据,应用上述两种降秩方法对其进行了分析。仿真实验表明了这两种方法的可行性,减少了计算量,并提高了有效可测收敛性。
This paper describes two reduced-rank STAP based on the generic sidelobe canceller (GSC): eigen-based principal component and cross-spectral metric. Those two approaches all take advantage of the low rank nature of clutter and jamming observations, and the reduced-dimension transformation applied to the data are necessarily data dependent. And lastly the Mountain Top program is introduced, and the measure data from the Mountain Top program is used to conduct a comparative analysis of the two reduced-rank STAP technique. The measure data analysis shows that those two approaches reduced computational burden and improved convergence measure of effectiveness.
基金
国家重点基础研究发展规划(973)项目资助(2001CB309403)
