加利福尼亚附近海洋哺乳动物的基于模型的自动定位

C. Tiemann, M. B. Porter, J. Hildebrand
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引用次数: 10

摘要

在之前的一项工作中,我们开发了一种算法,用于在夏威夷附近对唱歌的座头鲸进行声学跟踪。通过纯相位相关过程测量的鲸鱼叫声到达时的两两时差与声学传播模型预测的时间滞后进行了比较。测量和模拟时间滞后之间的差异定义了一个模糊面,该模糊面确定了在阵列周围的水平面上最可能的鲸鱼位置。在这项工作中,我们描述了这项技术在一个非常不同的环境场景中的应用,涉及加利福尼亚海岸的蓝鲸。鲸鱼叫声的频率要低得多,接收器是海底地震仪。该算法再次表现得非常好,提供了实时、自动监控和警报的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated model-based localization of marine mammals near California
In a previous work, we developed an algorithm for acoustically tracking singing humpback whales near Hawaii. Pair-wise time-differences in arrival of whale calls as measured by a phase-only correlation process are compared to time-lags predicted by an acoustic propagation model. Differences between measured and modeled time-lags defined an ambiguity surface that identifies the most probable whale location in a horizontal plane around an array. In this work, we describe the application of this technique to a very different environmental scenario involving blue whales off the coast of California. The whale calls are much lower in frequency and the receivers are ocean bottom seismometers. Again the algorithm performs extremely well, providing the capability for real-time, automated monitoring and alert.
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