Ballistic Rockets Tracking: Kalman versus &amp;#x03B1;&amp;#x03B2;&amp;#x03B3; Filters

Abreu, José Alano Peres de; Neto, João Viana F.; Oliveira, Roberto Célio Limão de

doi:10.1109/uksim.2011.66

Cited by 3 publications

(3 citation statements)

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“…These studies aim to develop and apply more robust algorithms for real-world problems. Considering such requirements, the α-β filter (Kosuge, Ito, Okada & Mano, 2002;Ng, Yeong, Su & Wong, 2012;Tenne & Singh, 2002;Yadav, Naik, Ananthasayanam, Gaur & Singh, 2012) and SKF (Abreu, Neto & Oliveira, 2011;Chui & Chen, 2008;Kalman, 1960;Welch & Bishop, 2006) were the most widely used algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Despite its wide use, the α-β filter has the following limitations: It is suboptimal, and constant gains are computed a priori and provided as input to the tracking algorithm (Yadav, Naik, Ananthasayanam, Gaur & Singh, 2012). 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%

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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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Section: Introductionmentioning

confidence: 99%

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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“…Their application in the proposed system would significantly complicate the equations, without guarantee of significant improvements of the EKF accuracy. For this reason, authors of some publications neglect the influence of atmospheric mass movement or assume a constant wind model for the whole period of flight [14].…”

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confidence: 99%

Ballistic Target Tracking with Use of Cinetheodolites

Kaniewski

Smagowski

Konatowski

2019

International Journal of Aerospace Engineering

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The paper addresses a problem of ballistic object tracking with the use of the cinetheodolite electro-optical tracking system. Electro-optical systems are applied for acquiring the trajectory data of missiles, satellites, and rockets used for delivery of satellites to their prevised orbits. Despite the importance of such systems and their applications, in the open literature there are no publications describing tracking algorithms processing data from cinetheodolites. The paper describes a model-based algorithm of estimation of position and parameters of target motion for such a system, developed by the authors. The model of the system, nonlinear both in its description of the target dynamics and the measurement equations, is presented in detail. The proposed algorithm of estimation is also described, and chosen simulation results are included in the paper. Furthermore, a comparison of the proposed estimation algorithm with other possible, but simpler algorithms is presented.

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