Multi-AUV Marine Life Tracking with Single Hydrophone Payloads via a Hidden Markov Model Equipped Particle Filter

2026-06-21

AI Summary

This study addresses the challenge of persistently tracking highly maneuverable marine animals, which is difficult for conventional single-hydrophone systems, while multi-hydrophone AUV configurations suffer from large size and high hydrodynamic drag that degrade tracking performance. To overcome these limitations, this work proposes a cooperative multi-AUV tracking approach in which each AUV carries only a single compact hydrophone. By embedding a hidden Markov model (HMM) behavioral prior into a multi-platform particle filtering framework, the method fuses acoustic tag observations to achieve high-precision localization. This is the first implementation of an HMM-based behavioral model in a multi-AUV tracking system, substantially reducing both system complexity and fluid resistance. Field trials yielded a short-term root-mean-square positioning error of approximately 10 meters, while large-scale, long-duration simulations demonstrated errors around 15 meters—outperforming baseline approaches using generic velocity models and random walks.

Abstract

Researchers tag and track marine animals to study migration patterns, human impacts on behavior, and behavioral shifts due to climate change. Accurate data collection often requires tagging individual animals to collect spatio-temporal state estimates of the animal's geo-position and depth. Acoustic transmitters are prominent due to their continuous communication without requiring retrieval or surfacing to collect data. These transmitters emit underwater acoustic pulses that can be detected by hydrophones. However, the frequent movement of aquatic animals results in high data loss when the animal moves out of the detection range of a stationary hydrophone. Autonomous underwater vehicle (AUV) systems offer a solution for localizing transmitters with higher resolution over longer periods of time. Such systems previously deployed have often required multiple hydrophones mounted on a large frame carried by the AUV. This increases drag, limiting the speed at which the AUV can track highly mobile animals. This work provides an alternative by equipping multiple AUVs with a single compact hydrophone payload. A particle filter algorithm equipped with a hidden Markov model (HMM) behavioral motion model fuses measurements from multiple AUVs to estimate the transmitter's position. Real-world data shows a root mean square error (RMSE) of approximately 10 meters for short-term deployments, and a larger simulated dataset shows an RMSE of approximately 15 meters for longer deployments over a larger area. The HMM fit to historical animal movement data outperforms a generic velocity motion model, and both outperform a baseline random walk motion model.

Problem

• marine life tracking
• acoustic transmitter localization
• Autonomous Underwater Vehicle (AUV)
• hydrophone payload
• data loss

Innovation

• multi-AUV coordination
• single hydrophone tracking
• HMM-enhanced particle filter
• acoustic telemetry
• marine animal localization