Diffusion Denoiser-Aided Gyrocompassing

2025-07-28

AI Summary

To address the challenge of achieving high-precision initial heading estimation using low-cost gyroscopes without external navigation aids, this paper proposes a gyroscope-compass orientation method integrating diffusion-based denoising with deep learning. We introduce diffusion models into gyrocompass systems for the first time, enabling end-to-end signal denoising to effectively separate noise from heading-related dynamic features in low-quality inertial measurements. A lightweight deep network is then designed for robust heading angle prediction. Evaluated on both synthetic and real-world datasets, the method improves accuracy by 26% over conventional filtering approaches and by 15% over existing learning-based methods. It significantly enhances initial alignment accuracy and timeliness for resource-constrained autonomous platforms operating under strong noise conditions. The core innovation lies in the synergistic co-design of the diffusion mechanism and gyrocompass physical modeling, establishing a novel paradigm for passive heading estimation with low-precision sensors.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

An accurate initial heading angle is essential for efficient and safe navigation across diverse domains. Unlike magnetometers, gyroscopes can provide accurate heading reference independent of the magnetic disturbances in a process known as gyrocompassing. Yet, accurate and timely gyrocompassing, using low-cost gyroscopes, remains a significant challenge in scenarios where external navigation aids are unavailable. Such challenges are commonly addressed in real-world applications such as autonomous vehicles, where size, weight, and power limitations restrict sensor quality, and noisy measurements severely degrade gyrocompassing performance. To cope with this challenge, we propose a novel diffusion denoiser-aided gyrocompass approach. It integrates a diffusion-based denoising framework with an enhanced learning-based heading estimation model. The diffusion denoiser processes raw inertial sensor signals before input to the deep learning model, resulting in accurate gyrocompassing. Experiments using both simulated and real sensor data demonstrate that our proposed approach improves gyrocompassing accuracy by 26% compared to model-based gyrocompassing and by 15% compared to other learning-driven approaches. This advancement holds particular significance for ensuring accurate and robust navigation in autonomous platforms that incorporate low-cost gyroscopes within their navigation systems.

Problem

• Achieving accurate gyrocompassing with low-cost gyroscopes
• Reducing noise in inertial sensor signals for navigation
• Improving heading estimation in autonomous vehicles

Innovation

• Diffusion denoiser processes raw gyroscope signals
• Combines denoising with learning-based heading estimation
• Improves gyrocompassing accuracy by 26% over traditional methods