计算机科学
杂乱
多向性
声学
人体回声定位
水听器
生物声学
跟踪(教育)
源跟踪
信号(编程语言)
滤波器(信号处理)
职位(财务)
人工智能
计算机视觉
雷达
电信
物理
万维网
经济
节点(物理)
程序设计语言
教育学
心理学
财务
作者
Pina Gruden,Eva-Marie Nosal,Erin M. Oleson
出处
期刊:Journal of the Acoustical Society of America
[Acoustical Society of America]
日期:2021-11-05
卷期号:150 (5): 3399-3416
被引量:3
摘要
Acoustic line transect surveys are often used in combination with visual methods to estimate the abundance of marine mammal populations. These surveys typically use towed linear hydrophone arrays and estimate the time differences of arrival (TDOAs) of the signal of interest between the pairs of hydrophones. The signal source TDOAs or bearings are then tracked through time to estimate the animal position, often manually. The process of estimating TDOAs from data and tracking them through time can be especially challenging in the presence of multiple acoustically active sources, missed detections, and clutter (false TDOAs). This study proposes a multi-target tracking method to automate TDOA tracking. The problem formulation is based on the Gaussian mixture probability hypothesis density filter and includes multiple sources, source appearance and disappearance, missed detections, and false alarms. It is shown that by using an extended measurement model and combining measurements from broadband echolocation clicks and narrowband whistles, more information can be extracted from the acoustic encounters. The method is demonstrated on false killer whale (Pseudorca crassidens) recordings from Hawaiian waters.
科研通智能强力驱动
Strongly Powered by AbleSci AI