System Identification through Online Sparse Gaussian Process Regression with Input Noise

TL;DR

This paper introduces SONIG, an efficient online Gaussian Process regression method that handles noisy inputs, enabling real-time system identification with improved accuracy over existing algorithms.

Contribution

The paper presents SONIG, a novel sparse online GP regression algorithm that efficiently incorporates noisy measurements in real-time, addressing key limitations of traditional GP methods.

Findings

More accurate than existing regression algorithms

Operates in constant runtime for new measurements

Performs competitively in non-linear system modeling

Abstract

There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have three important downsides: (1) it is computationally intensive, (2) it cannot efficiently implement newly obtained measurements online, and (3) it cannot deal with stochastic (noisy) input points. In this paper we present an algorithm tackling all these three issues simultaneously. The resulting Sparse Online Noisy Input GP (SONIG) regression algorithm can incorporate new noisy measurements in constant runtime. A comparison has shown that it is more accurate than similar existing regression algorithms. When applied to non-linear black-box system modeling, its performance is competitive with existing non-linear ARX models.