基于盲信息理论的信号自动分类方法

V.J. Stolpman, S. Paranjpe, G.C. Orsak
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引用次数: 8

摘要

以前的经验信号分类的信息理论方法已经为来自每个信号源的标记训练数据可用的应用而开发。这些所谓的“通用”分类器已被证明在非常广泛的统计条件下对感兴趣的信号具有渐近最优性,并已成功地应用于通信信号调制分类、人脸识别进入控制以及宽带通信的CDMA接收器等问题。不幸的是,在许多重要的应用程序中,训练数据可能不容易获得或不可靠,或者获取成本太高。为了解决训练系统的这些限制,我们提出了一种新的通用分类器公式,它不需要明确的训练数据。这种新的盲分类器直接从测试数据中提取必要的训练信息,并将其最优地用于构建决策统计。给出了通信系统中的实例。结果表明,该接收机的性能超过了标准“训练”接收机的性能,接近于全局最优接收机的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A blind information theoretic approach to automatic signal classification
Previous information theoretic approaches for empirical signal classification have been developed for applications where labeled training data from each of the signal sources is available. These so-called "universal" classifiers have been shown to be asymptotically optimal under very broad statistical conditions on the signals of interest and have been successfully applied to problems in communication signal modulation classification, face recognition for entry control, and as a CDMA receiver for wideband communications. Unfortunately, there are many important applications where training data may not be readily available or is unreliable, or is simply too costly to obtain. To address these limitations of trained systems, we present a new formulation of the universal classifier which does not require explicit training data. This new blind classifier extracts the necessary training information directly from the test data and uses it optimally in constructing the decision statistics. Examples from communication systems are presented. The results show that the performance of this new receiver exceeds that of standard "trained" receivers and nearly matches that of the globally optimum receiver.
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