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Clutter metric based on the Cramer-Rao lower bound on automatic target recognition

机译：基于自动目标识别的Cramer-Rao下限的杂波度量

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摘要

This is a performance evaluation on the implementation of the Cramer-Rao lower bound (CRLB) for background clutter measurement on automatic target recognition (ATR). In essence the background clutter evaluation problem for ATR is consistent with the deterministic parameter estimation problem. Thus, useful concepts and theories of deterministic parameter estimation can be introduced into the investigation of background clutter. In this paper, the CRLB is employed as a metric for clutter measurement. Requirements needed for this application are analyzed, and the approach for obtaining the CRLB of a scene image is produced. The flexibility of the CRLB metric is analyzed. Discussion and comparison are made on the relationship between the CRLB metric and the Sims signal-to-clutter metric. Finally, we illustrate how this metric defines the potential for false alarms by determining the correspondence level between a target and background through the application of the CRLB.
机译：这是对Cramer-Rao下限（CRLB）在自动目标识别（ATR）上用于背景杂波测量的实现的性能评估。实质上，ATR的背景杂波评估问题与确定性参数估计问题是一致的。因此，确定性参数估计的有用概念和理论可以引入背景杂波的研究中。在本文中，CRLB被用作杂波测量的度量。分析了此应用程序所需的要求，并生成了获取场景图像的CRLB的方法。分析了CRLB指标的灵活性。对CRLB度量与Sims信号杂波度量之间的关系进行了讨论和比较。最后，我们通过应用CRLB来确定目标和背景之间的对应级别，来说明此度量标准如何定义错误警报的可能性。

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