Year: 2026
Type: article
Abstract: Accurate 3D fish localization supports understanding of movement, behavior, and habitat use. Traditional solvers such as approximate maximum likelihood (AML) are vulnerable to multipath, time-of-arrival (TOA) measurement errors, and synchronization errors, while Yet Another Positioning Solver (YAPS) can degrade with sparse detections. We introduce a physics-informed neural network (PINN) solver that models trajectories as continuous, physics-constrained functions, minimizes the negative log-likelihood of TOA noise, and quantifies uncertainty via ensemble training. We validated the approach using synthetic experiments and field deployments in the Salish Sea and a dam tailrace. Under synthetic scenarios with abrupt turns, accelerations, and gaps, the solver achieved sub-meter root mean square (RMS) errors of 0.30–0.80 m and >83% efficiency. In the Salish Sea, RMS error was 1.17–2.24 m with 23%–76% efficiency, reducing RMS by ~60% versus AML, and reducing RMS by >75% versus YAPS. In the tailrace, RMS error was 0.81–0.93 m with >92% efficiency, reducing RMS by ~35% versus AML and by >80% versus YAPS. The proposed PINN solver delivers accurate localization across challenging conditions, enabling fine-scale ecological monitoring.
Institutions: Pacific Northwest National Laboratory, University of Michigan
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204278101
FWCI:
Cites:
Cited by:
Related to: