Maximum Likelihood Decision Fusion for Weapon Classification in Wireless Acoustic Sensor Networks
Maximum Likelihood Decision Fusion for Weapon Classification in Wireless Acoustic Sensor Networks
复制标题
DOI：
10.1109/taslp.2017.2690579
复制
发表时间：
2017-06
期刊：
影响因子：
--
通讯作者：
Héctor A. Sánchez-Hevia;D. Ayllón;R. Gil-Pita;M. Rosa-Zurera
中科院分区：
文献类型：
--
作者：
Héctor A. Sánchez-Hevia;D. Ayllón;R. Gil-Pita;M. Rosa-Zurera
枪击声分析是一个有许多实际应用的领域，但由于枪支声特征的产生涉及众多因素，因此它不是一项简单的任务。主要的问题是，即使在处理同一武器时，记录的波形也显示出强烈的空间依赖性。然而，这可以通过使用诸如无线声学传感器网络的空间分集接收器来减少。针对多通道声武器分类问题，提出了一种基于空间信息的多通道声武器分类决策融合方法，该方法利用多个分类器集成的优点，对传统的决策融合方法进行了改进，提出了一种基于最大似然估计的决策融合方法。分类器多样性来自于在每个节点局部执行的空间分割。同样的分割也被用来提高局部分类的准确性，通过分而治之的方法。
Gunshot acoustic analysis is a field with many practical applications, but due to the multitude of factors involved in the generation of the acoustic signature of firearms, it is not a trivial task. The main problem arises with the strong spatial dependence shown by the recorded waveforms even when dealing with the same weapon. However, this can be lessen by using a spatially diverse receiver such as a wireless acoustic sensor network. In this work, we address multichannel acoustic weapon classification using spatial information and a novel decision fusion rule based on it. We propose a fusion rule based on maximum likelihood estimation that takes advantage of diverse classifier ensembles to improve upon classic decision fusion techniques. Classifier diversity comes from a spatial segmentation that is performed locally at each node. The same segmentation is also used to improve the accuracy of the local classification by means of a divide and conquer approach.