Weighted Information Filtering, Smoothing, and Out-of-Sequence Measurement Processing

TL;DR

This paper introduces a weighted filtering approach that emphasizes recent measurements and modifies the traditional Kalman filter structure to better handle unmodeled uncertainties and out-of-sequence data in dynamical systems.

Contribution

It proposes a unified, optimal filtering method using exponential weighting that extends Kalman filter capabilities to out-of-sequence and smoothing tasks.

Findings

The new method effectively handles out-of-sequence measurements.

It provides a unified framework for filtering, smoothing, and prediction.

The approach improves robustness to unmodeled system uncertainties.

Abstract

We consider the problem of state estimation in dynamical systems and propose a different mechanism for handling unmodeled system uncertainties. Instead of injecting random process noise, we assign different weights to measurements so that more recent measurements are assigned more weight. A specific choice of exponentially decaying weight function results in an algorithm with essentially the same recursive structure as the Kalman filter. It differs, however, in the manner in which old and new data are combined. While in the classical KF, the uncertainty associated with the previous estimate is inflated by adding the process noise covariance, in the present case, the uncertainty inflation is done by multiplying the previous covariance matrix by an exponential factor. This difference allows us to solve a larger variety of problems using essentially the same algorithm. We thus propose a unified and optimal, in the least-squares sense, method for filtering, prediction, smoothing and general out-of-sequence updates. All of these tasks require different Kalman-like algorithms when addressed in the classical manner.