Cascaded Filtering Using the Sigma Point Transformation (Extended Version)
This addresses state estimation accuracy in decentralized systems, offering an incremental improvement over existing cascaded filtering methods.
The paper tackles the problem of overconfident estimates in cascaded filtering by introducing a novel decentralized approach that approximates cross-covariances for distinct state vectors, showing it outperforms naive and Covariance Intersection filters and performs comparably to a full-state filter in simulations and experiments.
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.