Bridging Echolocation Gaps in Automated Beaked Whale Tracking

2026-09-09

AI Summary

本文提出了一种多阶段目标估计方法，结合信念传播的多目标跟踪与轨迹平滑和拼接技术，以解决喙鲸回声定位点击序列中断导致的轨迹片段化问题。

Abstract

Passive acoustic monitoring (PAM) is an effective and widely used tool for tracking marine mammals, particularly beaked whales, which are infrequently observed visually because of their deep-diving behavior. However, the large data sets generated by PAM methods often require time-consuming hand labeling to identify whale trajectories in the recorded audio. Automated multi-target tracking (MTT) methods could significantly reduce human workload, but current methods have difficulty forming continuous tracks because of the irregularity of beaked whale echolocation clicks. More precisely, regular sequences of clicks are often interrupted by longer pauses that occur when whales face away from the sensors or stop clicking. Consequently, the probability of detection is difficult to model accurately, and MTT trajectories become fragmented at these pauses. In this paper, we propose a multistage target-estimation method aimed at bridging large gaps in click sequences by combining belief propagation-based MTT with track smoothing and stitching. We validate our method using acoustic recordings of clicks from goose-beaked whales (Ziphius cavirostris), and demonstrate that it improves track estimates and reduces fragmentation in the presence of consecutive missed detections. When evaluated with the generalized optimal subpattern assignment (GOSPA) metric, our method outperforms existing MTT reference methods through reductions in missed-target errors.

Problem

• Passive Acoustic Monitoring
• Beaked Whales
• Echolocation Clicks
• Multi-Target Tracking
• Trajectory Fragmentation

Innovation

• belief propagation
• multi-target tracking
• track smoothing and stitching
• echolocation click sequences