Improved Discrete-Time Kalman Filtering within Singular Value Decomposition

TL;DR

This paper introduces a novel SVD-based Kalman filter implementation that improves numerical stability and estimation accuracy in ill-conditioned scenarios, addressing roundoff errors more effectively than previous methods.

Contribution

A new SVD-based Kalman filter variant is developed, enhancing robustness against roundoff errors and ill-conditioned problems, with detailed derivation and stability analysis.

Findings

Outperforms previous SVD-based methods in accuracy

Maintains algebraic equivalence with conventional KF

Demonstrates improved stability in numerical experiments

Abstract

The paper presents a new Kalman filter (KF) implementation useful in applications where the accuracy of numerical solution of the associated Riccati equation might be crucially reduced by influence of roundoff errors. Since the appearance of the KF in 1960s, it has been recognized that the factored-form of the KF is preferable for practical implementation. The most popular and beneficial techniques are found in the class of square-root algorithms based on the Cholesky decomposition of error covariance matrix. Another important matrix factorization method is the singular value decomposition (SVD) and, hence, further encouraging implementations might be found under this approach. The analysis presented here exposes that the previously proposed SVD-based KF variant is still sensitive to roundoff errors and poorly treats ill-conditioned situations, although the SVD-based strategy is inherently more stable than the conventional KF approach. In this paper we design a new SVD-based KF implementation for enhancing the robustness against roundoff errors, provide its detailed derivation, and discuss the numerical stability issues. A set of numerical experiments are performed for comparative study. The obtained results illustrate that the new SVD-based method is algebraically equivalent to the conventional KF and to the previously proposed SVD-based method, but it outperforms the mentioned techniques for estimation accuracy in ill-conditioned situations.