Kalman-Takens filtering in the presence of dynamical noise

TL;DR

This paper introduces a hybrid Kalman-Takens filtering method that reconstructs dynamics from data and effectively separates observational and dynamical noise without requiring a parametric model.

Contribution

The paper presents a novel hybrid filtering approach combining Kalman filtering with delay-coordinate reconstruction, enabling noise separation in a nonparametric setting.

Findings

Comparable efficiency to parametric methods in identifying dynamics

Effective separation of observational and dynamical noise

Adaptive filtering estimates noise statistics accurately

Abstract

The use of data assimilation for the merging of observed data with dynamical models is becoming standard in modern physics. If a parametric model is known, methods such as Kalman filtering have been developed for this purpose. If no model is known, a hybrid Kalman-Takens method has been recently introduced, in order to exploit the advantages of optimal filtering in a nonparametric setting. This procedure replaces the parametric model with dynamics reconstructed from delay coordinates, while using the Kalman update formulation to assimilate new observations. We find that this hybrid approach results in comparable efficiency to parametric methods in identifying underlying dynamics, even in the presence of dynamical noise. By combining the Kalman-Takens method with an adaptive filtering procedure we are able to estimate the statistics of the observational and dynamical noise. This solves a long standing problem of separating dynamical and observational noise in time series data, which is especially challenging when no dynamical model is specified.