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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.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="" target="_blank"></a>
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.subjectInteracting multiple model
dc.subjectManeuvering target tracking
dc.subjectParticle filter
dc.typeConference Paper
dc.description.sourcetitleFUSION 2007 - 2007 10th International Conference on Information Fusion
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