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首页> 外文会议>Ground/air multisensor interoperablity, integration, and networking for persistent ISR IV ; Open architecture/open business model net-centric systems and defense transformation 2013 >Acoustic Network Event Classification Using Swarm Optimization
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Acoustic Network Event Classification Using Swarm Optimization

机译：基于群体优化的声网络事件分类

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摘要

Classifying acoustic signals detected by distributed sensor networks is a difficult problem due to the wide variations that can occur in the transmission of terrestrial, subterranean, seismic and aerial events. An acoustic event classifier was developed that uses particle swarm optimization to perform a flexible time correlation of a sensed acoustic signature to reference data. In order to mitigate the effects from interference such as multipath, the classifier fuses signatures from multiple sensors to form a composite sensed acoustic signature and then automatically matches the composite signature with reference data. The approach can classify all types of acoustic events but is particularly well suited to explosive events such as gun shots, mortar blasts and improvised explosive devices that produce an acoustic signature having a shock wave component that is aperiodic and non-linear. The classifier was applied to field data and yielded excellent results in terms of reconstructing degraded acoustic signatures from multiple sensors and in classifying disparate acoustic events.
机译：由于在地面，地下，地震和空中事件的传输中可能发生很大的变化，因此对由分布式传感器网络检测到的声信号进行分类是一个难题。开发了一种声波事件分类器，该分类器使用粒子群优化技术来执行感知到的声波签名与参考数据的灵活时间相关性。为了减轻干扰（例如多径）的影响，分类器将来自多个传感器的签名融合在一起，以形成合成感测的声学签名，然后自动将合成签名与参考数据进行匹配。该方法可以对所有类型的声音事件进行分类，但特别适合爆炸事件，例如枪击，迫击炮爆炸和简易爆炸装置，这些爆炸装置会产生具有非周期性且非线性的冲击波分量的声音特征。该分类器应用于现场数据，并在从多个传感器重建退化的声学特征以及对不同的声学事件进行分类方面产生了出色的结果。

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