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首页> 外文会议>International Conference on Information Fusion >Selective Information Transmission using Convolutional Neural Networks for Cooperative Underwater Surveillance
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Selective Information Transmission using Convolutional Neural Networks for Cooperative Underwater Surveillance

机译：使用卷积神经网络进行协同水下监视的选择性信息传输

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摘要

Cooperation among multiple autonomous surface and underwater vehicles is an important capability for detection and tracking of underwater objects. Cooperative autonomy in the underwater environment, however, is challenged by the communication bandwidth. In this work, we propose a selective communication scheme that underpins collaborative surveillance under communication constraints. This scheme classifies signal reflections of sonar pulses that are detected by on-board sensor processing as contacts with the object of interest or background using a convolutional neural network. This network is trained using previously labelled contact spectrograms obtained during three sea trials carried out between 2016-2018. The classification scores at the CNN output are ordered to select the few contacts that the underwater modem bandwidth allows for transmission to the network. First, we evaluate the accuracy of the data-driven information selection scheme using recall scores and similar performance measures. Then, we find the accuracy in Bayesian recursive filtering (tracking) of these contacts for different communication rates using established error metrics. The results suggest that the selective scheme yields a favourable surveillance performance communication cost trade-off.
机译：多个自主水面和水下航行器之间的合作是检测和跟踪水下物体的重要能力。但是，水下环境中的协作自主性受到通信带宽的挑战。在这项工作中，我们提出了一种选择性的通信方案，该方案可在通信约束下为协作监视提供基础。此方案使用卷积神经网络将通过车载传感器处理检测到的声纳脉冲信号反射分类为与感兴趣对象或背景的接触。该网络使用在2016-2018年之间进行的三次海试期间获得的先前标记的接触光谱图进行了训练。 CNN输出上的分类分数被排序为选择水下调制解调器带宽允许传输到网络的少量联系人。首先，我们使用召回得分和类似的绩效指标来评估数据驱动信息选择方案的准确性。然后，我们使用已建立的误差度量，针对不同的通信速率在这些联系人的贝叶斯递归过滤（跟踪）中找到了准确性。结果表明，选择方案产生了良好的监视性能通信成本权衡。

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