Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation

This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stoch...

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Published in:2024 27th International Conference on Information Fusion (FUSION) pp. 1 - 8
Main Authors: Dunik, Jindrich, Matousek, Jakub, Straka, Ondrej, Blasch, Erik, Hiles, John, Niu, Ruixin
Format: Conference Proceeding
Language:English
Published: ISIF 08.07.2024
Subjects:
Filtering
Filtering algorithms
Heuristic algorithms
Kalman filters
MATLAB
Nonlinear systems
Prediction
Prediction algorithms
Random variables
Smoothing
Smoothing methods
State estimation
Stochastic integration rule
Stochastic processes
Stone Soup
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Abstract This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective multi-step prediction and smoothing algorithms are developed in full and efficient square-root form. The stochastic-integration-rule-based algorithms are implemented in Python (within the Stone Soup framework) and in MATLAB® and are numerically evaluated and compared with the well-known unscented and extended Kalman filters using the Stone Soup defined tracking scenario.
AbstractList This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective multi-step prediction and smoothing algorithms are developed in full and efficient square-root form. The stochastic-integration-rule-based algorithms are implemented in Python (within the Stone Soup framework) and in MATLAB® and are numerically evaluated and compared with the well-known unscented and extended Kalman filters using the Stone Soup defined tracking scenario.
Author Niu, Ruixin
Blasch, Erik
Matousek, Jakub
Straka, Ondrej
Dunik, Jindrich
Hiles, John
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  fullname: Straka, Ondrej
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  organization: Virginia Commonwealth University,Dept. of ECE
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  givenname: Ruixin
  surname: Niu
  fullname: Niu, Ruixin
  email: rniu@vcu.edu
  organization: Virginia Commonwealth University,Dept. of ECE
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Snippet This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically...
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SubjectTerms Filtering
Filtering algorithms
Heuristic algorithms
Kalman filters
MATLAB
Nonlinear systems
Prediction
Prediction algorithms
Random variables
Smoothing
Smoothing methods
State estimation
Stochastic integration rule
Stochastic processes
Stone Soup
Title Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation
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