Tracking a ballistic target by multiple model approach

Reali, Fabrizio; Palmerini, Giovanni B.; Farina, A.; Graziano, A.; Timmoneri, L.

doi:10.1109/aero.2009.4839486

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…The same ballistic objects adopted in [8, 12] have been used: three fin‐stabilised projectiles (respectively 60, 81 and 120 mm of gauge), whose attitude is maintained by means of small fins located in the end of central body and one spin‐stabilised projectile (155 mm) that is controlled by gyroscopic effect (in this case the natural stability of the longitudinal axis around which the rigid body is set in rapid rotation is exploited).…”

Section: Problem Statementmentioning

confidence: 99%

“…The avail of identify a specific target within a set of several possible candidates suggests the use of an MMF [8, 11–13]. This approach is based on the idea that no single generic filter is able to correctly identify and track an unspecified target belonging to a large class of very different objects.…”

Section: Multiple Model Filtermentioning

confidence: 99%

“…In this context, the tracking of projectiles offers an interesting application example as these objects can be fired by different mortars and have different shape, mass, inertia as well as kinematics conditions of exit from the rifle [10]. The identification of a projectile among a set of possible candidates and the prediction of its impact point by multiple model filter (MMF) has been performed in [11–13]. However, we think that the analysis proposed in [11] is a partial investigation.…”

Section: Introductionmentioning

confidence: 99%

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Parametric analysis of ballistic target‐tracking problem by multiple model approach

Reali

Palmerini

Farina

et al. 2013

IET Radar, Sonar & Navigation

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Radar tracking of a ballistic target is a topic presenting very complex issues especially when the characteristics of the objects\ud flying into the Earth’s atmosphere are poorly known. As the target identification is required to obtain good performance, a technique\ud frequently employed in the practical applications is the multiple model filter. This study, thought the increasing interest for this class of\ud estimators, aims to identify the key parameters of the problem by making recognisable their effects on the solution. Thus – along with\ud the ideal operative conditions – the relative geometry target-sensor, the presence of missing plots, the measurement uncertainties and\ud the credibility of filter predictions are investigated. In order to have a strict benchmark of evaluation the multiple model approach\ud performance is compared with the optimal estimate given by posterior Cramér–Rao lower bounds

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Section: Problem Statementmentioning

confidence: 99%

Section: Multiple Model Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parametric analysis of ballistic target‐tracking problem by multiple model approach

Reali

Palmerini

Farina

et al. 2013

IET Radar, Sonar & Navigation

Self Cite

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“…Specially during the first decade of twenty first century, many attempts [7], [8], [9], [10], [11] have been made to find out accurate and computationally effective tracking filter to track a ballistic target. The researchers used available advanced nonlinear estimators such as unscented Kalman filter [9], particle filter [7], [9], quadrature based filters [12], etc and compared the estimation accuracy among them and with the Cramer Rao lower bound [9].…”

Section: Introductionmentioning

confidence: 99%

Tracking of ballistic target on re-entry using ensemble Kalman filter

Singh

Bhaumik

Bhattacharya

2012

2012 Annual IEEE India Conference (INDICON)

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In this work, ground radar based ballistic target tracking problem in endo-atmospheric re-entry phase with unknown ballistic coefficient has been solved using ensemble Kalman filter (EnKF). EnKF, a powerful tool in nonlinear estimation, is being extensively used by meteorologist but almost unknown to target tracking community. Performance improvement, and computational burden of EnKF with increasing ensemble size have been studied. Performance of EnKF has been compared with most popular extended Kalman Filter (EKF) in terms of biasness, estimation accuracy, and computational efficiency. The simulation results reveal that the estimation accuracy of EnKF with sufficient ensemble size is much better than EKF.

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“…One disadvantage of these methods is that they assume the ballistic coefficient, which is one of key parameters for target dynamics, is known with sufficient accuracy. However, the ballistic coefficient can only be known crudely or even unknown in practice [9]. In such a case, one idea is to consider that the target dynamics updates according to one of a bank of models, as the models can differ in values of ballistic coefficient.…”

Section: Introductionmentioning

confidence: 99%

Approximate Chernoff fusion of Gaussian mixtures for ballistic target tracking in the re-entry phase

Zhou

Zhang³

2017

Aerospace Science and Technology

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A track-to-track fusion method to combine local estimates modelled with Gaussian mixture model is proposed for tracking a re-entry ballistic vehicle. An arbitrary power of a Gaussian mixture distribution is approximated with Gaussian mixture model using first order expansion approximation, which leads to an analytical fusion equation for approximate Chernoff fusion. In the end, we verify the effectiveness of the proposed fusion algorithm with a series of Monte Carlo simulations.

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Tracking a ballistic target by multiple model approach

Cited by 7 publications

References 10 publications

Parametric analysis of ballistic target‐tracking problem by multiple model approach

Parametric analysis of ballistic target‐tracking problem by multiple model approach

Tracking of ballistic target on re-entry using ensemble Kalman filter

Approximate Chernoff fusion of Gaussian mixtures for ballistic target tracking in the re-entry phase

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