InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics

TL;DR

This paper introduces InfoSSM, an interpretable, nonparametric state-space model using multiple Gaussian processes to capture complex, multi-modal dynamics in time-series data, with applications in system identification and air traffic control.

Contribution

It proposes a novel multi-modal Gaussian process-based framework with an information-theoretic regularizer for interpretability in system identification.

Findings

Successfully models complex multi-modal dynamics in simulations

Demonstrates improved interpretability over traditional models

Applicable to real-world air traffic tracking scenarios

Abstract

The goal of system identification is to learn about underlying physics dynamics behind the time-series data. To model the probabilistic and nonparametric dynamics model, Gaussian process (GP) have been widely used; GP can estimate the uncertainty of prediction and avoid over-fitting. Traditional GPSSMs, however, are based on Gaussian transition model, thus often have difficulty in describing a more complex transition model, e.g. aircraft motions. To resolve the challenge, this paper proposes a framework using multiple GP transition models which is capable of describing multi-modal dynamics. Furthermore, we extend the model to the information-theoretic framework, the so-called InfoSSM, by introducing a mutual information regularizer helping the model to learn interpretable and distinguishable multiple dynamics models. Two illustrative numerical experiments in simple Dubins vehicle and high-fidelity flight simulator are presented to demonstrate the performance and interpretability of the proposed model. Finally, this paper introduces a framework using InfoSSM with Bayesian filtering for air traffic control tracking.