Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes

When signals are measured through physical sensors, they are perturbed by noise. To reduce noise, low-pass filters are commonly employed in order to attenuate high frequency components in the incoming signal, regardless if they come from noise or the actual signal. Therefore, low-pass filters must be carefully tuned in order to avoid significant deterioration of the signal. This tuning requires prior knowledge about the signal, which is often not available in applications such as reinforcement learning or learning-based control. In order to overcome this limitation, we propose an adaptive low-pass filter based on Gaussian process regression. By considering a constant window of previous observations, updates and predictions fast enough for real-world filtering applications can be realized. Moreover, the online optimization of hyperparameters leads to an adaptation of the low-pass behavior, such that no prior tuning is necessary. We show that the estimation error of the proposed method is uniformly bounded, and demonstrate the flexibility and efficiency of the approach in several simulations.


page 1

page 2

page 3

page 4


Combining Slow and Fast: Complementary Filtering for Dynamics Learning

Modeling an unknown dynamical system is crucial in order to predict the ...

Low-pass filtering as Bayesian inference

We propose a Bayesian nonparametric method for low-pass filtering that c...

Frequency selective extrapolation with residual filtering for image error concealment

The purpose of signal extrapolation is to estimate unknown signal parts ...

Personalized Graph Signal Processing for Collaborative Filtering

The collaborative filtering (CF) problem with only user-item interaction...

Parzen Filters for Spectral Decomposition of Signals

We propose a novel family of band-pass filters for efficient spectral de...

A User Guide to Low-Pass Graph Signal Processing and its Applications

The notion of graph filters can be used to define generative models for ...

Multi-Architecture Multi-Expert Diffusion Models

Diffusion models have achieved impressive results in generating diverse ...

Please sign up or login with your details

Forgot password? Click here to reset