Physics-Informed Data Denoising for Real-Life Sensing Systems
This provides a practical, cost-effective denoising solution for real-world sensor applications, addressing a common bottleneck in noisy, low-cost sensors.
The paper tackles the problem of sensor noise in real-life sensing systems by developing a physics-informed denoising model that uses inherent algebraic relationships from physics laws, eliminating the need for ground truth clean data. It achieves state-of-the-art performance in domains like inertial navigation and CO2 monitoring, with real-time processing (4ms for a 1s sequence) and results comparable to high-cost alternatives.
Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects performance and reliability of the systems they support. Classic filtering-based approaches introduce strong assumptions on the time or frequency characteristics of sensory measurements, while learning-based denoising approaches typically rely on using ground truth clean data to train a denoising model, which is often challenging or prohibitive to obtain for many real-world applications. We observe that in many scenarios, the relationships between different sensor measurements (e.g., location and acceleration) are analytically described by laws of physics (e.g., second-order differential equation). By incorporating such physics constraints, we can guide the denoising process to improve even in the absence of ground truth data. In light of this, we design a physics-informed denoising model that leverages the inherent algebraic relationships between different measurements governed by the underlying physics. By obviating the need for ground truth clean data, our method offers a practical denoising solution for real-world applications. We conducted experiments in various domains, including inertial navigation, CO2 monitoring, and HVAC control, and achieved state-of-the-art performance compared with existing denoising methods. Our method can denoise data in real time (4ms for a sequence of 1s) for low-cost noisy sensors and produces results that closely align with those from high-precision, high-cost alternatives, leading to an efficient, cost-effective approach for more accurate sensor-based systems.