Evaluation of Knowledge-Aided STAP Using Experimental Data

J. Bergin, D. Kirk, G. Chaney, S. McNeil, P. Zulch
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引用次数: 2

Abstract

Recent advances in knowledge-aided space-time adaptive processing (KA-STAP) have resulted in significant performance improvements for ground moving target indication (GMTI) radar systems. In particular, the use of prior knowledge including terrain, clutter discretes, and previously detected targets has been shown to be effective for mitigating the poor performance often encountered when operating in heterogeneous clutter environments. This paper provides an evaluation of KA-STAP techniques based on extensive processing of experimental data. Two major performance issues are addressed: high false alarm rates due to under-nulled clutter discretes and target cancellation due to corruption of the STAP training data by other targets in the scene. Each of these problems is demonstrated using experimental multi-channel X-band radar data. Methods for using prior knowledge to improve performance are presented and processing results using the experimental data are provided that show how KA-STAP can lead to significantly improved detection performance relative to conventional STAP processing.
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用实验数据评价知识辅助STAP
知识辅助时空自适应处理(KA-STAP)的最新进展已经导致地面移动目标指示(GMTI)雷达系统的显著性能改进。特别是,使用先验知识,包括地形、杂波离散和先前检测到的目标,已被证明可以有效地减轻在异构杂波环境中操作时经常遇到的不良性能。本文在对实验数据进行大量处理的基础上,对KA-STAP技术进行了评价。解决了两个主要的性能问题:由于空杂波离散导致的高虚警率和由于场景中其他目标损坏STAP训练数据而导致的目标取消。每个问题都使用实验多通道x波段雷达数据进行了演示。提出了使用先验知识提高性能的方法,并提供了使用实验数据的处理结果,表明相对于传统的STAP处理,KA-STAP如何显著提高检测性能。
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