A comparison of several nonlinear filters for ballistic missile tracking on re-entry

Singh, Nikhil Kumar; Bhaumik, Shovan; Bhattacharya, Sayantan

doi:10.1109/cmi.2016.7413790

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…In terms of Taylor series expansion, assuming Gaussian distributions, the UKF can achieve at least second‐order accuracy in the face of an arbitrary nonlinear function (Merwe, 2004), while the standard EnKF can achieve at least first‐order accuracy (Luo & Moroz, 2009). Comparison studies in other fields (Gillijns et al., 2006; Gross et al., 2010; Gupta et al., 2015; Kim et al., 2012; H. Liu et al., 2020; Meng et al., 2018; Singh et al., 2016) show that the UKF and CKF are competitive methods for state estimation to the popular PF and EnKF, which achieve a satisfactory compromise between the filter performance and computational efficiency (i.e., good estimation accuracy and stable performance are obtained within moderate computational time). Sun et al.…”

Section: Introductionmentioning

confidence: 99%

“…Comparison studies in other fields (Gillijns et al, 2006;Gross et al, 2010;Gupta et al, 2015;Kim et al, 2012;H. Liu et al, 2020;Meng et al, 2018;Singh et al, 2016) show that the UKF and CKF are competitive methods for state estimation to the popular PF and EnKF, which achieve a satisfactory compromise between the filter performance and computational efficiency (i.e., good estimation accuracy and stable performance are obtained within moderate computational time). Sun et al (2020) introduced the UKF to improve the forecast performance of a conceptual hydrologic model (Wageningen Lowland Runoff Simulator, WALRUS) by updating the states and comparing the UKF with the stochastic EnKF.…”

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confidence: 99%

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Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter

Sun

Tian

Bao

et al. 2023

Water Resources Research

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Hydrological models are widely used for flood forecasting, and proper initialization of hydrological models is essential. Although the unscented Kalman filter (UKF) has shown promise in the context of operational forecasting, it is limited by its intrinsic instability caused by the loss of positive semidefiniteness of the covariance matrix. Effective methods for tackling this problem have not been seen in the literature. The cubature Kalman filter (CKF) is a powerful nonlinear filter for state estimation, which has received little attention in the hydrologic context. In this paper, to overcome the instability problem of the UKF, a new modified UKF (UKF‐M) is proposed by modifying the default UKF tuning strategy, and a novel nonlinear filter (CKF) is introduced to update the states of hydrological models. The methods are tested using a lumped hydrological model. The results of the stability experiment suggest that both the UKF‐M and CKF can overcome the instability problem, while the original UKF can fail due to the loss of positive semidefiniteness of the covariance matrix. The results of a reforecast experiment in an operational context show that both filters can improve the forecast performance by updating the model state based on the observed discharge, with the UKF‐M achieving better forecast performances. The UKF‐M and CKF can be used to overcome the instability problem of the original UKF and are valuable methods for improving forecast performances of hydrological models by state updating.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter

Sun

Tian

Bao

et al. 2023

Water Resources Research

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“…We found some studies that deal with this subject [3,4]. Comparative studies have been developed to implement more accurate and robust tracking algorithms to solve real problems in this area [5][6][7]. In this study, four different tracking algorithms are researched in terms of performance and computational complexities.…”

Section: Introductionmentioning

confidence: 99%

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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Estimation and Tracking of a Ballistic Target Using Sequential Importance Sampling Method

Ramnarayan

Anita

Sudheesh

2017

Communications in Computer and Information Science

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A comparison of several nonlinear filters for ballistic missile tracking on re-entry

Cited by 8 publications

References 13 publications

Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter

Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter

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

Estimation and Tracking of a Ballistic Target Using Sequential Importance Sampling Method

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