Bayesian detection and tracking of odontocetes in 3-D from their echolocation clicks

Localization and tracking of marine animals can reveal key insights into their behaviors underwater that would otherwise remain unexplored. A promising nonintrusive approach to obtaining location information of marine animals is to process their bioacoustic signals, which are passively recorded using multiple hydrophones. In this paper, a data processing chain that automatically detects and tracks multiple odontocetes (toothed whales) in three dimensions (3-D) from their echolocation clicks recorded with volumetric hydrophone arrays is proposed. First, the time-difference-of-arrival (TDOA) measurements are extracted with a generalized cross-correlation that whitens the received acoustic signals based on the instrument noise statistics. Subsequently, odontocetes are tracked in the TDOA domain using a graph-based multi-target tracking (MTT) method to reject false TDOA measurements and close gaps of missed detections. The resulting TDOA estimates are then used by another graph-based MTT stage that estimates odontocete tracks in 3-D. The tracking capability of the proposed data processing chain is demonstrated on real acoustic data provided by two volumetric hydrophone arrays that recorded echolocation clicks from Cuvier's beaked whales (Ziphius cavirostris). Simulation results show that the presented MTT method using 3-D can outperform an existing approach that relies on manual annotation.

Automated summary

This paper proposes a method of automatically detecting and tracking marine animals using their bioacoustic signals, based on processing the acoustic signals recorded by multiple hydrophones. The method extracts time-difference-of-arrival (TDOA) measurements using generalized cross-correlation and tracks odontocetes in the TDOA domain using a graph-based multi-target tracking (MTT) method. The tracking capability of the method is demonstrated on acoustic data of Cuvier's beaked whales.