Robust fixed-lag smoothing under model perturbations

TL;DR

This paper introduces a robust fixed-lag smoothing method that accounts for model uncertainties by framing the problem as a dynamic game, providing an efficient implementation and demonstrating its effectiveness in tracking and estimation tasks.

Contribution

It presents a novel robust smoothing approach based on a game-theoretic framework to handle model mismatches, with an efficient implementation and practical examples.

Findings

Effective in target tracking and parameter estimation

Handles model uncertainties robustly

Provides an efficient computational approach

Abstract

A robust fixed-lag smoothing approach is proposed in the case there is a mismatch between the nominal model and the actual model. The resulting robust smoother is characterized by a dynamic game between two players: one player selects the least favorable model in a prescribed ambiguity set, while the other player selects the fixed-lag smoother minimizing the smoothing error with respect to least favorable model. We propose an efficient implementation of the proposed smoother. Moreover, we characterize the corresponding least favorable model over a finite time horizon. Finally, we test the robust fixed-lag smoother in two examples. The first one regards a target tracking problem, while the second one regards a parameter estimation problem.