Particle-Based Tuning of the Unscented Kalman Filter

Scardua, Leonardo Azevedo; Cruz, José Jaime da

doi:10.1007/s40313-015-0223-1

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…The state distribution is represented by using a minimal set of carefully selected sample points [12, 15, 16, 29].…”

Section: Ipp Problem Formulation and The Related Modelsmentioning

confidence: 99%

“…The UKF propagates the Gaussian random variables that represent the state through system dynamics by using a deterministic sampling approach. The state distribution is represented by using a minimal set of carefully selected sample points [12,15,16,29].…”

Section: Unscented Kalman Filtermentioning

confidence: 99%

“…The UKF tracking algorithm is a variant of the EKF and it uses a different linearisation process, which consists of transforming a set of sigma points that are selected deterministically [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks

Abreu

Oliveira

Neto

2021

IET Signal Processing

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One of the current ways to continue space research is to launch ballistic rockets that carry scientific payloads. To improve the accuracy of the instantaneous evolution of the payload impact on the Earths surface, it is necessary to estimate indirect measures more efficiently. This study proposes a comprehensive approach to determine the impact point prediction of ballistic rocket payloads. This approach combines tracking algorithms that are based on stochastic estimators with artificial neural network (ANN) models. Four existing stochastic estimators, namely a recursive Kalman filter (RKF), an extended Kalman filter (EKF), an unscented Kalman filter (UKF), and a particle filter (PF) are compared with four proposed stochastic estimators. These include a recursive Kalman filter aided by an ANN (RKFN), an extended Kalman filter aided by an ANN (EKFN), an unscented Kalman filter aided by an ANN (UKFN), and a particle filter aided by an ANN (PFN). This study shows that the results that are obtained through the proposed tracking algorithms RKFN, EKFN, UKFN, and PFN are better than those of the existing tracking algorithms RKF, EKF, UKF, and PF. The proposed estimators can be efficient low-cost tools to mitigate inaccuracies during tracking up to the payload's impact.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…The state distribution is represented by using a minimal set of carefully selected sample points [12, 15, 16, 29].…”

Section: Ipp Problem Formulation and The Related Modelsmentioning

confidence: 99%

Section: Unscented Kalman Filtermentioning

confidence: 99%

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A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks

Abreu

Oliveira

Neto

2021

IET Signal Processing

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“…Owing to the lack of recursion, this filter can cause aberrant results in the cases of abnormality in flight (Abreu, Neto & Oliveira, 2011). Other techniques have been studied and developed to overcome these limitations; for example, the EKF (Einicke & White, 1999;Farina, Ristic & Benvenuti, 2002) and UKF (Biswas, Southwell & Dempster, 2018;Garcia, Pardal, Kuga & Zanardi, 2019;Julier & Uhlmann, 2004;Scardua & da Cruz, 2016;Wan & Van Der Merwe, 2000).…”

Section: Introductionmentioning

confidence: 99%

Rocket tracking impact point prediction using α-β, standard Kalman, extended, Kalman, and unscented Kalman filters: a comparative analysis

Abreu

Oliveira

Neto

2020

RSD

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Accurate information about the impact point (IP) of a suborbital rocket on Earth’s surface during a launch is an important requirement for range safety operations. Four different estimators, i.e., the α-β filter, standard Kalman filter (SKF), extended Kalman filter (EKF), and unscented Kalman filter (UKF), are considered for the suborbital rocket tracking problem, whose data are used specifically for improving the accuracy of the IP prediction (IPP) of these vehicles. This paper presents a comparative analysis between the results of the estimators. Rocket flight data are discussed to demonstrate the advantages and disadvantages of the estimators and to determine the inherent limitations in predicting the aerodynamic effects found in certain flight situations. We discuss the appropriate mathematical model of a filter capable of running the real-time algorithm for the estimation of target position and velocity. This work uses actual data from a radar sensor to evaluate the tracking algorithms. We insert the filter result into the mathematical model developed to predict the rocket IP on Earth's surface. The main goal of this study is to evaluate the performance of four different estimators when specifically applied for the improvement of the IPP of suborbital rockets. It is demonstrated that the UKF outperforms all other tracking algorithms in terms of the accuracy and robustness of IP estimation.

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Parametric and Nonparametric Bayesian Filters for Autonomous Underwater Vehicle Localization

França

Pimentel

Saltón

2019

J Control Autom Electr Syst

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Particle-Based Tuning of the Unscented Kalman Filter

Cited by 6 publications

References 24 publications

A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks

A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks

Rocket tracking impact point prediction using α-β, standard Kalman, extended, Kalman, and unscented Kalman filters: a comparative analysis

Parametric and Nonparametric Bayesian Filters for Autonomous Underwater Vehicle Localization

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