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首页> 外文期刊>WSEAS Transactions on Signal Processing >Ships Classification Basing On Acoustic Signatures
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Ships Classification Basing On Acoustic Signatures

机译:基于声学特征的船舶分类

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摘要

The paper presents the technique of artificial neural networks used as classifier of hydroacoustic signatures generated by moving ship. The main task of proposed solution is to classify the objects which made the underwater noises. Firstly, the measurements were carried out dynamically by running ship past stationary hydrophones, mounted on tripods 1 m above the sea bottom. Secondly to identify the source of noise the level of vibration were measured on board by accelerometers, which were installed on important components of machinery. On the base of this measurement there was determined the sound pressure level, noise spectra and spectograms, transmission of acoustic energy via the hull into water. More over it was checked by using coherence function that components of underwater noise has its origin in vibrations of ship's mechanisms. Basing on this research it was possible to create the hydroacoustic signature or so called "acoustic portrait" of moving ship. Next during the complex ships' measurements on Polish Navy Test and Evaluation Acoustic Range hydroacoustic noises generated by moving ship were acquired. Basing on these results the classifier of acoustic signatures using artificial neural network was worked out. From the technique of artificial neural networks the Kohonen networks which belongs to group of self organizing networks where chosen to solve the research problem of classification. The choice was caused by some advantages of mentioned kind of neural networks like: they are ideal for finding relationships amongst complex sets of data, they have possibility to self expand the set of answers for new input vectors. To check the correctness of classifier work the research in which the number of right classification for presented and not presented before hydroacoustic signatures were made. Some results of research were presented on this paper. Described method actually is extended and its application is provided as assistant subsystem for hydrolocations systems of Polish Naval ships.
机译:本文提出了一种人工神经网络技术,作为移动船舶产生的水声信号特征的分类器。提出的解决方案的主要任务是对产生水下噪声的对象进行分类。首先,通过使船舶经过固定的水听器动态地进行测量,该水听器安装在海底上方1 m的三脚架上。其次,为了确定噪声源,使用加速度计在船上测量了振动水平,该加速度计安装在机械的重要组件上。在此测量的基础上,确定了声压级,噪声谱和频谱图,声能通过船体到水中的传输。此外,通过使用相干函数检查,水下噪声的成分起源于船舶机构的振动。基于此研究,有可能创建运动船舶的水声特征或所谓的“声像”。接下来,在波兰海军测试和评估的复杂舰船测量中,获取了由移动船舶产生的声程水声噪声。基于这些结果,得出了利用人工神经网络对声学特征进行分类的方法。从人工神经网络技术出发,选择属于自组织网络组的Kohonen网络来解决分类研究问题。该选择是由上述神经网络的一些优点引起的,例如:它们非常适合在复杂数据集之间查找关系,它们有可能自行扩展新输入向量的答案集。为了检查分类器工作的正确性,在进行水声签名之前先进行正确的分类,然后再进行正确的分类。本文提出了一些研究结果。所描述的方法实际上得到了扩展,并将其应用作为波兰海军舰船水位系统的辅助子系统。

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