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A wavelet packet approach for bistatic buried target classification with an AUV-borne synthetic aperture sonar

机译:一种小波包方法,用于带有AUV的合成孔径声纳的双基地掩埋目标分类

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摘要

Buried target detection and classification is of significant interest to the underwater mine countermeasures (MCM) community. In order to penetrate the seafloor, low frequency sources are required. For adequate temporal (cross-range) resolution, a wideband source is necessary. In this low frequency regime, the different scattering mechanisms behave as a strong function of frequency. Buried elastic targets, such as mines, exhibit strong modal behavior, generating responses delayed in time and occupying only a fraction of the full source frequency band. There is also much more sub-bottom interaction by the lower frequencies, and as such the statistics of the reverberation signal vary with frequency. This may impact the platform motion estimation capability in synthetic aperture sonar (SAS) applications. This paper details an algorithm for exploiting the temporal and frequency diversity of the signals for SAS target detection and classification. An example is provided using an at-sea bistatic SAS application with an AUV-borne sonar receiver.
机译:埋地目标检测和分类对水下矿区对策(MCM)社区具有重要兴趣。为了渗透海底,需要低频源。对于足够的时间(交叉量)分辨率,需要宽带源。在这种低频状态中,不同的散射机制表现为频率的强函数。埋藏的弹性目标,如矿山,表现出强大的模态行为,产生延迟的响应并仅占据全源频带的一部分。较低频率也有更多的子底部交互,并且随着混响信号的统计数据随频率而变化。这可能影响合成孔径声纳(SAS)应用中的平台运动估计能力。本文详细介绍了一种利用SAS目标检测和分类信号的时间和频率分集的算法。使用具有AUV传输的声纳接收器的海上双晶体SAS应用提供了一个例子。

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