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Multi-aspect target discrimination using hidden Markov models and neural networks

机译:使用隐马尔可夫模型和神经网络的多角度目标识别

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This paper presents a new multi-aspect pattern classification method using hidden Markov models (HMMs). Models are defined for each class, with the probability found by each model determining class membership. Each HMM model is enhanced by the use of a multilayer perception (MLP) network to generate emission probabilities. This hybrid system uses the MLP to find the probability of a state for an unknown pattern and the HMM to model the process underlying the state transitions. A new batch gradient descent-based method is introduced for optimal estimation of the transition and emission probabilities. A prediction method in conjunction with HMM model is also presented that attempts to improve the computation of transition probabilities by using the previous states to predict the next state. This method exploits the correlation information between consecutive aspects. These algorithms are then implemented and benchmarked on a multi-aspect underwater target classification problem using a realistic sonar data set collected in different bottom conditions.
机译:本文提出了一种新的使用隐马尔可夫模型(HMM)的多方面模式分类方法。为每个类别定义模型,每个模型找到的概率确定类别成员。通过使用多层感知(MLP)网络来生成发射概率,可以增强每个HMM模型。该混合系统使用MLP查找未知模式的状态概率,并使用HMM对状态转换基础的过程进行建模。引入了一种基于批次梯度下降的新方法,用于最佳估计过渡和发射概率。还提出了一种结合HMM模型的预测方法,该方法试图通过使用先前的状态来预测下一个状态来改善转换概率的计算。该方法利用连续方面之间的相关性信息。然后,使用在不同底部条件下收集的真实声纳数据集,在多方面水下目标分类问题上实现这些算法并对其进行基准测试。

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