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Bayesian gamma mixture model approach to radar target recognition

机译：贝叶斯伽玛混合模型雷达目标识别方法

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摘要

This paper develops a Bayesian gamma mixture model approach to automatic target recognition (ATR). The specific problem considered is the classification of radar range profiles (RRPs) of military ships. However, the approach developed is relevant to the generic discrimination problem. We model the radar returns (data measurements) from each target as a gamma mixture distribution. Several different motivations for the use of mixture models are put forward, with gamma components being chosen through a physical consideration of radar returns. Bayesian formalism is adopted and we obtain posterior distributions for the parameters of our mixture models. The distributions obtained are too complicated for direct analytical use in a classifier, so Markov chain Monte Carlo (MCMC) techniques are used to provide samples from the distributions. The classification results on the ship data compare favorably with those obtained from two previously published techniques, namely a self-organizing map and a maximum likelihood gamma mixture model classifier.
机译：本文开发了贝叶斯伽马混合物模型方法，以自动目标识别（ATR）。所考虑的具体问题是军事船舶雷达范围谱（RRP）的分类。然而，发展方法与通用歧视问题相关。我们将雷达返回（数据测量）从每个目标返回（数据测量）作为伽马混合物分布。提出了用于使用混合模型的几种不同动机，通过物理考虑雷达返回来选择伽马组件。采用贝叶斯形式主义，我们获得了混合模型参数的后分布。获得的分布太复杂，对于分类器中的直接分析使用，所以马尔可夫链蒙特卡罗（MCMC）技术用于提供来自分布的样本。船舶数据的分类结果与从两种先前公布的技术获得的那些相比，即自组织地图和最大似然伽马混合物模型分类器。

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