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针对多种动物的声学跟踪的MAMBAT框架

Pina Gruden¹, Eva-Marie Nosal², E Elizabeth Henderson³

¹Ocean and Resources Engineering, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA. pgruden@hawaii.edu.

Scientific reports

|May 13, 2025

概括

使用新的多种动物模型基于声学跟踪 (MAMBAT) 框架的被动声学监测 (PAM) 可以自动跟踪多种海洋哺乳动物. 这项技术改善了海洋哺乳动物种群研究和保护工作.

科学领域:

海洋生物学海洋生物学
生物声学是一种生物声学.
声学信号处理声学信号处理

背景情况:

被动声学监测 (PAM) 对海洋哺乳动物研究至关重要,但产生复杂的数据与重叠的信号.
需要自动化工具来处理诸如多源,错过检测和PAM数据中的错误报警等挑战.

研究的目的:

引入基于多种动物模型的声学跟踪 (MAMBAT) 框架,用于自动化多源声音跟踪.
为了证明MAMBAT在分析现实世界海洋哺乳动物声学数据方面的能力.

主要方法:

基于模型的本地化与贝叶斯式多目标跟踪的整合.
实施"追踪之前定位"的战略,其次是"定位然后追踪"的战略.
消除了对明确检测,分类或关联步骤的需求.

主要成果:

从复杂的声学数据中成功地自动跟踪多个声音源.
在来自两个海洋盆地的精油的真实世界数据集上证明了有效性.
验证了一种新的方法,它绕过了传统的检测和关联步骤.

结论:

MAMBAT提供了一种先进的自动化解决方案,用于使用PAM数据跟踪多种海洋哺乳动物.
该框架增强了监测海洋哺乳动物分布,数量和行为的能力.
马姆巴特为海洋保护和管理策略提供了宝贵的见解.

关键词:

贝叶斯的多目标追踪.多种动物追踪多种动物追踪被动声学局部化被动声学局部化精子是一种精子.

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