Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIF.2007.4407974
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dc.titleCombining IMM method with particle filters for 3D maneuvering target tracking
dc.contributor.authorPek, H.F.
dc.contributor.authorGee, W.N.
dc.date.accessioned2014-10-28T02:50:37Z
dc.date.available2014-10-28T02:50:37Z
dc.date.issued2007
dc.identifier.citationPek, H.F.,Gee, W.N. (2007). Combining IMM method with particle filters for 3D maneuvering target tracking. FUSION 2007 - 2007 10th International Conference on Information Fusion : -. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICIF.2007.4407974" target="_blank">https://doi.org/10.1109/ICIF.2007.4407974</a>
dc.identifier.isbn0662478304
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104544
dc.description.abstractThe Interacting Multiple Model (IMM) algorithm is a widely accepted state estimation scheme for solving maneuvering target tracking problems, which are generally nonlinear. During the IMM filtering process, serious errors can arise when a Gaussian mixture of posterior probability density functions is approximated by a single Gaussian. Particle filters (PFs) are effective in dealing with nonlinearity and non-Gaussianity. This work considers an IMM algorithm that includes a constant velocity model, a constant acceleration model and a 3D turning rate (3DTR) model for tracking three-dimensional (3D) target motion, using various combinations of nonlinear filters. In existing literature on combining IMM and particle filtering techniques to tackle difficult target maneuvers, a PF is usually used in every model. In comparison, simulation results show that by using a computationally economical PF in the 3DTR model and Kalman filters in the remaining models, superior performance can be achieved with significant reduction in computational costs.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICIF.2007.4407974
dc.sourceScopus
dc.subjectInteracting multiple model
dc.subjectManeuvering target tracking
dc.subjectParticle filter
dc.typeConference Paper
dc.contributor.departmentMATHEMATICS
dc.description.doi10.1109/ICIF.2007.4407974
dc.description.sourcetitleFUSION 2007 - 2007 10th International Conference on Information Fusion
dc.description.page-
dc.identifier.isiutNOT_IN_WOS
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