Passive Acoustic Tracking of Whales in 3-D
Passive Acoustic Tracking of Whales in 3-D
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DOI：
10.1109/icassp49357.2023.10096584
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Junsu Jang;Florian Meyer;Eric R. Snyder;S. Wiggins;S. Baumann‐Pickering;J. Hildebrand
中科院分区：
文献类型：
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作者：
Junsu Jang;Florian Meyer;Eric R. Snyder;S. Wiggins;S. Baumann‐Pickering;J. Hildebrand
被动声监测(PAM)是一种非侵入性的方法，用于研究海洋生物在水下发声的行为，否则这些行为将一直未被探索。本文提出了一种能够从被动记录的水声信号中检测和跟踪三维多头鲸鱼的数据处理链。具体地说，通过噪声白化互相关从体积水听器阵列的声学数据中提取回声定位点击的到达时间差(TDOA)测量。对于多目标跟踪，TDOA测量值随后由基于和积算法(SPA)的贝叶斯推理引擎处理，该引擎由两个阶段组成。粒子流被嵌入到SPA中，使得在所考虑的非线性和高维场景中跟踪在计算上是可行的。在模拟和真实数据的场景中，展示了在没有人类干预的情况下跟踪多头鲸鱼的能力。
Passive acoustic monitoring (PAM) is a nonintrusive approach to studying behaviors of vocalizing marine organisms underwater that otherwise would remain unexplored. In this paper, we propose a data processing chain that can detect and track multiple whales in 3-D from passively recorded underwater acoustic signals. In particular, time-difference-of-arrival (TDOA) measurements of echolocation clicks are extracted from a volumetric hydrophone array’s acoustic data by using a noise-whitening cross-correlation. For multi-target tracking, the TDOA measurements are then processed by a Bayesian inference engine consisting of two stages that is based on the sum-product algorithm (SPA). Particle flow is embedded in the SPA to make tracking computationally feasible in the considered nonlinear and high-dimensional scenario. The capability to track multiple whales without human intervention is demonstrated in scenarios with simulated and real data.