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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Bayesian gamma mixture model approach to radar target recognition
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Bayesian gamma mixture model approach to radar target recognition

机译：贝叶斯伽玛混合模型在雷达目标识别中的应用

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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：本文开发了一种用于自动目标识别（ATR）的贝叶斯伽玛混合模型方法。所考虑的特定问题是军舰雷达测距图（RRP）的分类。但是，开发的方法与一般歧视问题有关。我们将每个目标的雷达回波（数据测量）建模为伽玛混合分布。提出了使用混合模型的几种不同动机，并通过对雷达回波的物理考虑来选择伽玛分量。采用贝叶斯形式主义，我们获得了混合模型参数的后验分布。所获得的分布太复杂，无法在分类器中直接进行分析，因此使用马尔可夫链蒙特卡洛（MCMC）技术从分布中提供样本。舰船数据的分类结果与从两种先前发布的技术（即自组织图和最大似然伽玛混合模型分类器）获得的结果相比具有优势。

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