Cascaded Filtering Using the Sigma Point Transformation (Extended Version)

TL;DR

This paper introduces a novel cascaded and decentralized filtering method that effectively approximates cross-covariances between local estimators with distinct state vectors, improving accuracy over naive methods.

Contribution

It presents a new filtering approach that handles cross-covariances in decentralized systems with different state vectors, validated through simulations and experiments.

Findings

Outperforms naive cascaded filtering and Covariance Intersection filters

Performs comparably to full-state filter in experiments

Validated in 3D attitude and position estimation

Abstract

It is often convenient to separate a state estimation task into smaller "local" tasks, where each local estimator estimates a subset of the overall system state. However, neglecting cross-covariance terms between state estimates can result in overconfident estimates, which can ultimately degrade the accuracy of the estimator. Common cascaded filtering techniques focus on the problem of modelling cross-covariances when the local estimators share a common state vector. This letter introduces a novel cascaded and decentralized filtering approach that approximates the cross-covariances when the local estimators consider distinct state vectors. The proposed estimator is validated in simulations and in experiments on a three-dimensional attitude and position estimation problem. The proposed approach is compared to a naive cascaded filtering approach that neglects cross-covariance terms, a sigma point-based Covariance Intersection filter, and a full-state filter. In both simulations and experiments, the proposed filter outperforms the naive and the Covariance Intersection filters, while performing comparatively to the full-state filter.