Characterizing Refraction-Induced Ranging Bias in Underwater Collaborative Localization

2026-09-15

AI Summary

研究通过模拟实验探讨了声波折射引起的测距偏差对多代理水下协作定位的影响，特别是在千米尺度上，并使用HYCOM再分析数据和射线追踪技术来量化这种偏差。

Abstract

This work studies how refraction-induced bias on acoustic ranging affects multi-agent collaborative localization in a range of oceanographic conditions and spatial scales. While multi-agent range-aided navigation, which uses range measurements to either fixed infrastructure or other agents, is a promising solution to the challenges of large-scale underwater localization, its accuracy depends strongly on the quality of range measurements. Sound speed variability induces refraction (bending) of acoustic rays, yet, for algorithmic tractability, standard sensor fusion pipelines assume straight-line propagation. This refraction systematically biases range measurements to be longer than the straight-line assumption predicts. However, the effects of this bias on multi-agent collaborative localization on kilometer scales remains unexplored. We present a series of simulated experiments with several agents operating over kilometer scales. The simulation uses HYCOM reanalysis data to recreate realistic oceanographic conditions, ray tracing to generate refraction-informed ranges, and a centralized multi-agent factor graph estimator to quantify the resulting measurement bias on estimated trajectories. Preliminary results indicate that refraction-induced bias can induce significant degradation of estimated trajectories, particularly in regions with sharp sound-speed gradients. We also share the simulation environment to support further studies https://github.com/UMich-RobotExploration/manta-ray.

Problem

• refraction-induced bias
• acoustic ranging
• multi-agent collaborative localization
• sound speed variability

Innovation

• refraction-induced bias
• multi-agent collaborative localization
• ray tracing
• factor graph estimator
• HYCOM reanalysis data