Multiaspect classification of airborne targets via physics-based HMMs and matching pursuits
Multiaspect classification of airborne targets via physics-based HMMs and matching pursuits
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DOI：
10.1109/7.937471
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发表时间：
2001-04-01
影响因子：
4.4
通讯作者：
Hughes, JA
中科院分区：
文献类型：
--
作者：
Bharadwaj, P;Runkle, P;Hughes, JA
从N个不同的目标-传感器方位散射的宽带电磁场被用于空中目标的分类。通过基于物理的匹配追踪解析每个散射波形，产生N个特征向量。将特征向量提交给隐马尔可夫模型（HMM），其每个状态的特征在于一组目标传感器取向，在该取向上相关联的特征向量相对静止。从多方位散射数据中提取的N个特征向量隐式地对目标的N个状态进行采样（某些状态可能被多次采样），其中状态序列在统计上被建模为马尔可夫过程，由于“隐藏”或未知的目标取向而导致HMM。在这里提出的工作中，观察给定特征向量的状态相关概率通过物理激励的线性分布来建模，以代替经典HISTORY中应用的传统高斯混合。此外，我们开发了一个方案，产生自主定义的方面相关的HMM状态。该范例适用于两个简单目标的合成散射数据。
Wideband electromagnetic fields scattered from N distinct target-sensor orientations are employed for classification of airborne targets. Each of the scattered waveforms is parsed via physics-based matching pursuits, yielding N feature vectors, The feature vectors are submitted to a hidden Markov model (HMM), each state of which is characterized by a set of target-sensor orientations over which the associated feature vectors are relatively stationary. The N feature vectors extracted from the multiaspect scattering data implicitly sample N states of the target (some states may be sampled more than once), with the state sequence modeled statistically as a Markov process, resulting in an HMM due to the "hidden" or unknown target orientation. In the work presented here, the state-dependent probability of observing a given feature vector is modeled via physics-motivated linear distributions, in lieu of the traditional Gaussian mixtures applied in classical HMMs. Further, we develop a scheme that yields autonomous definitions for the aspect-dependent HMM states. The paradigm is applied to synthetic scattering data for two simple targets.