Efficient robust AMF using the enhanced FRACTA algorithm: results from KASSPER I & II [target detection]

S. Blunt, K. Gerlach
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引用次数: 3

Abstract

This paper presents further developments and results of the FRACTA algorithm which has been shown to be robust to nonhomogeneous environments containing outliers. The main focus here is upon the detection of targets in the KASSPER I challenge data cube which possesses dense clusters of targets and the highly nonhomogeneous KASSPER II data in which severe clutter is present over all ranges and Dopplers thereby hindering the identification of a dominant clutter ridge. The KASSPER II dataset is further exacerbated by dense clusters of targets as well as the presence of several deep shadow regions that not only prevent target detection but may also skew covariance matrix estimation. A doppler-dependent thresholding technique is developed which is then incorporated into the FRACTA.E framework and then applied to the KASSPER II dataset. Simulation results are compared with the standard sliding window scheme as well as when clairvoyant knowledge of the covariance matrices is employed. Results verify the improved performance of the FRACTA.E algorithm.
使用增强FRACTA算法的高效鲁棒AMF:来自KASSPER I和II[目标检测]的结果
本文介绍了FRACTA算法的进一步发展和结果,该算法已被证明对包含异常值的非均匀环境具有鲁棒性。本文的主要重点是在KASSPER I挑战数据立方体中检测目标,该立方体具有密集的目标簇,而KASSPER II数据高度非均匀,其中所有距离和多普勒都存在严重的杂波,从而阻碍了优势杂波山脊的识别。KASSPER II数据集进一步恶化了密集的目标簇以及几个深阴影区域的存在,这些阴影区域不仅阻止了目标检测,而且可能会扭曲协方差矩阵估计。开发了一种多普勒依赖阈值技术,然后将其纳入FRACTA。E框架,然后应用于KASSPER II数据集。仿真结果与标准滑动窗口方案以及使用协方差矩阵的透视知识时进行了比较。结果验证了FRACTA改进后的性能。E算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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