Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIF.2007.4407974
Title: Combining IMM method with particle filters for 3D maneuvering target tracking
Authors: Pek, H.F. 
Gee, W.N.
Keywords: Interacting multiple model
Maneuvering target tracking
Particle filter
Issue Date: 2007
Source: Pek, 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. https://doi.org/10.1109/ICIF.2007.4407974
Abstract: The 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.
Source Title: FUSION 2007 - 2007 10th International Conference on Information Fusion
URI: http://scholarbank.nus.edu.sg/handle/10635/104544
ISBN: 0662478304
DOI: 10.1109/ICIF.2007.4407974
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