The adaptive matched filter (AMF) uses a number of training samples observed by the radar to estimate the unknown disturbance covariance matrix of a cell under test. In general, as the number of homogeneous training samples increases, the detection performance of the AMF improves up to a theoretical limit (defined by the performance of a matched filter detector where the disturbance covariance is known). However, radar data are nonhomogeneous in practice. Consequently, a high number of training samples is typically undesirable, since nonhomogeneous training data cause detection performance to suffer. Thus, a decision maker (DM) must consider these tradeoffs when selecting this number of training samples, along with other decision parameters for the AMF. Using the concept of information elasticity, this tradeoff behavior is characterized for decisions that are relevant to a DM. A simple user defined constraint function is proposed, characterizing the relative cost of selecting different decisions. Using a multi-objective optimization (MOO) technique known as compromise programming, information overload is observed, in that increasing the cost of decisions improves performance up to a point, beyond which increasing the cost no longer provides meaningful benefit. Using this framework, a cost-efficient solution is selected.

中文翻译：

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自适应匹配滤波器的信息弹性框架

自适应匹配滤波器 (AMF) 使用雷达观察到的大量训练样本来估计被测单元的未知干扰协方差矩阵。一般来说，随着同质训练样本数量的增加，AMF 的检测性能提高到理论极限（由已知干扰协方差的匹配滤波器检测器的性能定义）。然而，雷达数据在实践中是不均匀的。因此，大量的训练样本通常是不可取的，因为非同质的训练数据会导致检测性能下降。因此，决策者 (DM) 在选择此数量的训练样本以及 AMF 的其他决策参数时必须考虑这些权衡。利用信息弹性的概念，这种权衡行为的特点是与 DM 相关的决策。提出了一个简单的用户定义约束函数，表征选择不同决策的相对成本。使用称为折衷编程的多目标优化 (MOO) 技术，可以观察到信息过载，因为增加决策成本可以将性能提高到一定程度，超过这一点，增加成本不再提供有意义的收益。使用此框架，可以选择具有成本效益的解决方案。因为增加决策成本可以在一定程度上提高性能，超过这一点，增加成本不再提供有意义的好处。使用此框架，可以选择具有成本效益的解决方案。因为增加决策成本可以在一定程度上提高性能，超过这一点，增加成本不再提供有意义的好处。使用此框架，可以选择具有成本效益的解决方案。