Abstract The ruggedness of emerging single adaptive approach that performs well in all types of operating conditions has led to the development of composite adaptive strategy. In this regard, the fusion of particular decisions of single adaptive schemes through suitable fusion rules can provide a better final detection. Particularly, the fusion of cell-averaging (CA), ordered statistics (OS) and trimmed-mean (TM) procedures can enhance the overall detection performance. Our goal in this paper is to analyze this developed model when the operating environment is heterogeneous. A χ 2 -distribution with two- and four-degrees of freedom is assumed for the fluctuation of primary and secondary extraneous targets. A closed form processor performance is derived for single pulse detection. The results show that for the non-homogeneous background the new approach is more practical. Particularly in multitarget situations, it exhibits higher robustness as compared to the CA, OS, or TM architectures. Additionally, the novel strategy has a homogeneous performance that surpasses performance of the classical Neyman-Pearson (N-P) detector, which can be employed as a yardstick for the analysis of different techniques in the CFAR world.

中文翻译：

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在异构环境中运行的自适应方案的标准变体的性能改进

摘要 在所有类型的操作条件下都表现良好的新兴单一自适应方法的坚固性导致了复合自适应策略的发展。在这方面，通过合适的融合规则融合单个自适应方案的特定决策可以提供更好的最终检测。特别是，单元平均 (CA)、有序统计 (OS) 和修剪均值 (TM) 程序的融合可以提高整体检测性能。我们在本文中的目标是在操作环境异构时分析这个开发的模型。对于主要和次要外部目标的波动，假定具有两个和四个自由度的 χ 2 分布。为单脉冲检测导出了封闭形式的处理器性能。结果表明，对于非均匀背景，新方法更实用。特别是在多目标情况下，与 CA、OS 或 TM 架构相比，它表现出更高的鲁棒性。此外，新策略具有超越经典 Neyman-Pearson (NP) 检测器的均匀性能，可用作分析 CFAR 世界中不同技术的标准。