Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning

2026-05-12

AI Summary

This study addresses the limitations of low-cost MEMS inertial measurement units (IMUs), whose hardware-constrained accuracy often falls short of high-precision navigation requirements. To overcome this challenge, the work introduces a conditional diffusion-based generative framework—the first to apply diffusion models to IMU signal enhancement—leveraging a U-Net architecture to synthesize high-fidelity virtual IMU signals. The model takes high-accuracy IMU data as a prior and low-cost IMU measurements as conditional inputs. By doing so, it effectively transcends the performance ceiling of low-cost sensors, yielding significantly improved localization and orientation estimates compared to raw measurements. Experimental validation in airborne mapping demonstrates that the enhanced signals produce denser, more consistent point clouds, confirming the method’s efficacy in multi-sensor integrated navigation systems.

Abstract

Inertial measurement units (IMUs) are fundamental sensing components in multi-source integrated navigation systems, and their performance directly determines the accuracy and reliability of solutions. However, the precision of low-cost IMUs is inherently constrained by hardware limitations. Recently, generative artificial intelligence has demonstrated remarkable capability in modeling complex data distributions and reconstructing high-fidelity signals. Motivated by this, we propose a diffusion-based generative learning framework for synthesizing high-fidelity virtual IMU data from low-cost IMU measurements. Specifically, a conditional diffusion model based on a U-Net architecture is constructed, where high-grade IMU measurements are utilized as ground-truth priors and low-cost IMU measurements are employed as conditional inputs. The virtual IMU data generated by the model is used for subsequent navigation and localization tasks. Experimental results demonstrate that the generated virtual IMU data significantly outperform the original low-cost IMU measurements in both positioning and attitude estimation. Furthermore, we transfer the model to airborne mapping experiments, where the proposed method produces thinner and more consistent point clouds. Overall, the proposed framework breaks the performance limits of low-cost IMU and demonstrates the potential of diffusion-based generative learning for virtual high-grade IMU data.

Problem

• MEMS IMU
• performance limitations
• low-cost IMU
• inertial measurement
• navigation accuracy

Innovation

• diffusion model
• generative learning
• virtual IMU
• MEMS IMU
• sensor enhancement