In this article, a robust constant false alarm rate detector (CFARD) is proposed for dense outlier situations such as the presence of high-density interfering targets within the reference window. Harmonic mean mitigates the impact of large outliers better than the arithmetic mean. We propose a CFARD called censored harmonic averaging (CHA)-CFARD, by using the modified harmonic averaging instead of the common arithmetic averaging in order to estimate the local clutter level. In practice, the number of cells which contain outliers is not known precisely. This poses a major challenge for censoring and truncating methods which aim to eliminate outliers from the reference window. The proposed detector reduces the undesirable effects of strong outliers without this restriction. Simulation results show that the CHA-CFARD outperforms the well-known CFARDs in the presence of interfering targets. Moreover, at the clutter edges, the proposed method shows an acceptable performance. Experimental results with TerraSAR-X images demonstrate that the proposed method is also effective and efficient for ship detection applications in nonhomogeneous sea clutter environments.

中文翻译：

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异构杂波中基于删失谐波平均的鲁棒CFAR检测器

在本文中，针对密集异常值情况（例如参考窗口内存在高密度干扰目标），提出了一种稳健的恒定误报率检测器 (CFARD)。调和平均值比算术平均值更好地减轻了大异常值的影响。我们提出了一种称为删失谐波平均 (CHA)-CFARD 的 CFARD，通过使用改进的谐波平均而不是普通算术平均来估计局部杂波水平。在实践中，包含异常值的单元格数量并不准确。这对旨在消除参考窗口中的异常值的审查和截断方法提出了重大挑战。所提出的检测器在没有这种限制的情况下减少了强异常值的不良影响。仿真结果表明，在存在干扰目标的情况下，CHA-CFARD 的性能优于众所周知的 CFARD。此外，在杂波边缘，所提出的方法显示出可接受的性能。TerraSAR-X 图像的实验结果表明，所提出的方法对于非均匀海杂波环境中的船舶检测应用也是有效和高效的。