Title :
Multirate Interacting Multiple Model Algorithm Combined with Particle Filter for Nonlinear/Non-Gaussian Target Tracking
Author :
Liu, Guixi ; Gao, Enke ; Fan, Chunyu
Author_Institution :
Dept. of Autom., Xidian Univ., Xi´´an
fDate :
Nov. 29 2006-Dec. 1 2006
Abstract :
In this paper, we propose a new interacting multiple model (IMM) algorithm combined with particle filter for nonlinear/non-Gaussian systems, which adopts the multirate technique to improve the computational efficiency. The interacting multiple model (IMM) algorithm is specially designed to track accurately targets, and the particle filter is aim to deal with nonlinear/non-Gaussian problems. But the problem of a particle filter is its expensive computation, especially when it is introduced into the IMM algorithm. Here, the multirate technique is to solve this problem and not making the performance of the algorithm bad. The experimental results show the multirate IMMPF (IMM particle filter) works as well as IMMPF with much lower computation load.
Keywords :
Gaussian processes; particle filtering (numerical methods); target tracking; multirate interacting multiple model algorithm; nonlinear/nonGaussian target tracking; particle filter; Algorithm design and analysis; Automation; Computational efficiency; Equations; Filtering algorithms; Particle filters; Particle tracking; Performance analysis; Steady-state; Target tracking; computation; interacting multiple model; multirate; nonlinear/non-Gaussian target tracking; particle filter;
Conference_Titel :
Artificial Reality and Telexistence--Workshops, 2006. ICAT '06. 16th International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
0-7695-2754-X
DOI :
10.1109/ICAT.2006.92