Nonlinearity-Aware Partial-Update Schmidt Kalman Filter

EasyChair Preprint 10890

Abstract

The partial-update filter is a Kalman filter modification that can accommodate higher nonlinearities and uncertainties than a nominal and Schmidt Kalman filter. This robustness enhancement of the partial-update filter is attributed to its capability to limit the impact of incorrect updates by applying static percentages of the nominal Kalman update to user-selected states at any time step. To further extend the partial-update capabilities and applicability, this paper presents two methods for dynamically selecting the partial-update percentages based on nonlinearity metrics of the process and measurement model. By equipping the partial-update filter with a dynamic update percentage, the filter can effectively leverage situations where higher updates can be applied, and lower updates are deemed suitable, leading to filter statistical consistency and accuracy increase with respect to the nominal and static partial-update filters. Simulation results show that the proposed nonlinearity-aware partial-update methods achieve results near a manually tuned partial-update.

Keyphrases: Kalman filter, Schmidt filter, nonlinearity, partial update, robust

@booklet{EasyChair:10890,
  author    = {J. Humberto Ramos and Kevin Brink},
  title     = {Nonlinearity-Aware Partial-Update Schmidt Kalman Filter},
  howpublished = {EasyChair Preprint 10890},
  year      = {EasyChair, 2023}}