Improved STAP performance using knowledge-aided secondary data selection

C. Capraro, G. Capraro, D. Weiner, M. Wicks, W. Baldygo
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引用次数: 32

Abstract

Secondary data selection for estimation of the clutter covariance matrix, needed in space-time adaptive processing (STAP), is normally obtained from range rings nearby the cell under test. The assumption is that these range rings contain cells that are representative of the clutter statistics in the test cell. However, in a nonhomogeneous terrain environment, this may not be true. An innovative approach is presented, in the area of knowledge-aided STAP, which utilizes terrain data from the United States Geological Survey (USGS) to aid in the selection of secondary data cells. Results have been obtained and compared with the sliding (cell averaging symmetric) window method of secondary data selection. This comparison indicates that making use of the surveillance terrain knowledge improves STAP performance.
使用知识辅助辅助数据选择提高了STAP性能
在时空自适应处理(STAP)中,用于杂波协方差矩阵估计的二次数据选择通常是从被测单元附近的距离环中获得的。假设这些距离环包含代表测试单元中的杂波统计信息的单元。然而,在非均匀地形环境中,这可能不是真的。在知识辅助STAP领域提出了一种创新的方法,该方法利用美国地质调查局(USGS)的地形数据来帮助选择次级数据单元。并与二次数据选择的滑动(单元平均对称)窗口法进行了比较。这一比较表明,利用监视地形知识可以提高STAP的性能。
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