Tracking Algorithms Based on Improved Interacting Multiple Model Particle Filter

Liu Gui-xi; Gao En-ke; Fan Chun-yu

doi:10.3724/SP.J.1146.2006.01267

Volume 29 Issue 12

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(12): 2810-2813

Liu Gui-xi, Gao En-ke, Fan Chun-yu . Tracking Algorithms Based on Improved Interacting Multiple Model Particle Filter[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2810-2813. doi: 10.3724/SP.J.1146.2006.01267

Citation:

Liu Gui-xi, Gao En-ke, Fan Chun-yu . Tracking Algorithms Based on Improved Interacting Multiple Model Particle Filter[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2810-2813. doi: 10.3724/SP.J.1146.2006.01267

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Tracking Algorithms Based on Improved Interacting Multiple Model Particle Filter

doi: 10.3724/SP.J.1146.2006.01267

Received Date: 2006-08-18
Rev Recd Date: 2007-04-27
Publish Date: 2007-12-19

Abstract

Abstract

The general Interacting Multiple Model (IMM) based on Kalman Filter (IMMKF) or Extended Kalman Filter (IMMEKF) can not deal with non-Guassian problems and also does not work so well as the IMM based on the particle filter for the nonlinear problems. The particle filter can deal with nonlinear/non-Guassian problems and it has been introduced to the algorithm of IMM for higher precision. However, the disadvantage of the particle filter is heavy computational load, because a particle filter usually has a lot of particles, which will increase the computational load greatly. Here an improved interacting multiple model particle filter, which combines Kalman filter and particle filter using multiple models, is proposed to improve the computational efficiency compared with the usual Interacting Multiple Model Particle Filter ( IMMPF). For nonlinear/non-Guassian problems, the new algorithm shows to possess a good performance as the IMMPF, while for linear problems it performs as well as the IMMEKF and works better than the IMMPF.

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References(1)

References

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