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dc.contributor.authorKumar, Kota Sumanth-
dc.contributor.authorDustaka, Nagarjuna Rao-
dc.contributor.authorJatoth, Ravi Kumar-
dc.date.accessioned2024-11-21T07:16:10Z-
dc.date.available2024-11-21T07:16:10Z-
dc.date.issued2010-
dc.identifier.citation10.1109/ICETET.2010.125en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/1616-
dc.descriptionNITWen_US
dc.description.abstractTracking a ballistic target in its reentry mode by considering the radar measurements is a highly complex problem in nonlinear filtering. Kalman Filter (KF) is used to estimate the position of target when the measurements are corrupted with noise. If the measurements are nonlinear (radar measurements) then Extended kalman filter (EKF) is used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation which is offline. Tuning an EKF is the process of estimating the process noise covariance matrix (Q) and measurement noise covariance matrix (R) .This paper presents a new method of tuning the EKF using different evolutionary algorithmsen_US
dc.language.isoenen_US
dc.publisherProceedings - 3rd International Conference on Emerging Trends in Engineering and Technology, ICETET 2010en_US
dc.subjectExtended Kalman Filteren_US
dc.subjectBallistic target trackingen_US
dc.titleEvolutionary Computational Tools Aided Extended Kalman Filter for Ballistic Target Trackingen_US
dc.typeOtheren_US
Appears in Collections:Electronics and Communication Engineering

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