Physics-Informed Data Denoising for Real-Life Sensing Systems
Xiyuan Zhang Xiaohan Fu Diyan Teng Chengyu Dong Keerthivasan Vijayakumar Jiayun Zhang Ranak Roy Chowdhury Junsheng Han Dezhi Hong Rashmi Kulkarni Jingbo Shang Rajesh Gupta
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.