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.

翻译：在当今互联世界中，测量真实物理过程的传感器无处不在。这些传感器固有地携带噪声，往往会对其所支持系统的性能和可靠性产生不利影响。经典的滤波方法对传感测量的时间或频率特性引入了强假设，而基于学习的去噪方法通常依赖使用地面真值清洁数据来训练去噪模型，这在许多实际应用中往往难以或无法获取。我们观察到，在许多场景中，不同传感器测量值（例如位置和加速度）之间的关系由物理定律（例如二阶微分方程）解析描述。通过引入此类物理约束，我们可以在缺乏地面真值数据的情况下引导去噪过程，从而提升性能。基于此，我们设计了一种基于物理信息的去噪模型，该模型利用由底层物理定律支配的不同测量值之间固有的代数关系。通过省去对地面真值清洁数据的需求，我们的方法为实际应用提供了一种实用的去噪解决方案。我们在惯性导航、CO₂监测及暖通空调控制等多个领域进行了实验，并与现有去噪方法相比取得了最先进的性能。我们的方法能够对低成本噪声传感器的数据实现实时去噪（1秒序列处理时间4毫秒），其结果与高精度、高成本替代方案高度吻合，从而为更精确的基于传感器的系统提供了一种高效、经济的方法。