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RANDOM FOREST ESTIMATOR FOR ENHANCED TARGET DETECTION

机译：增强目标检测能力的随机森林估计器

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The important work of improving signal to noise ratios for improved target detection presents one way to improve the target detection process. Dimensionality analysis of the data and the removal of uninteresting data is an effective method for target detection especially since it does not correlate the existing data. The process of deciding whether an anomaly in the data is a target is also an important part of target detection and this process may be just as important as uncovering the target from buried noise through the analysis of high dimensional data sets and the interrelated frequency contents, said in a different way, the noise and clutter removal processing may not always be able to help pull the target out of the high dimensional data enough to be able to detect the target with a simple thresholding approach. In this paper, we utilize the random forest technique to try and improve the decision making process in the detection of targets buried in noise.
机译：改善信噪比以改善目标检测的重要工作提出了一种改善目标检测过程的方法。数据的维数分析和不感兴趣的数据的删除是一种有效的目标检测方法，尤其是因为它与现有数据不相关。判断数据中的异常是否是目标的过程也是目标检测的重要部分，这一过程可能与通过分析高维数据集和相关的频率内容从埋藏的噪声中发现目标一样重要，换句话说，噪声和杂波去除处理可能并不总是能够帮助将目标从高维数据中拉出，足以通过简单的阈值方法检测目标。在本文中，我们利用随机森林技术来尝试和改进决策过程，以检测被噪声掩埋的目标。

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