DocumentCode
1835213
Title
Notice of Retraction
The polynomial predictive particle MeMBer filter
Author
Jianjun Yin ; Jianqiu Zhang ; Lihua Ni
Author_Institution
Electron. Eng. Dept., Fudan Univ., Shanghai, China
Volume
1
fYear
2010
fDate
1-3 Aug. 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A novel multi-target tracking algorithm, called the polynomial predictive particle Multi-target Multi-Bernoulli filter (PPP-MeMBer) filter was proposed. We firstly present a unified state space model where the state equation may describe any dynamics of the true targets, no matter linear or nonlinear and no matter we know them well or not, which is more common in practice. Then we apply the sequential Monte Carlo MeMBer (SMC-MeMBer) filter to the unified model. The analysis results show that the proposed PPP-MeMBer filter can deal with situations when we do not know the targets dynamics well. The multi-target tracking simulation results verify the effectiveness of the proposed method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A novel multi-target tracking algorithm, called the polynomial predictive particle Multi-target Multi-Bernoulli filter (PPP-MeMBer) filter was proposed. We firstly present a unified state space model where the state equation may describe any dynamics of the true targets, no matter linear or nonlinear and no matter we know them well or not, which is more common in practice. Then we apply the sequential Monte Carlo MeMBer (SMC-MeMBer) filter to the unified model. The analysis results show that the proposed PPP-MeMBer filter can deal with situations when we do not know the targets dynamics well. The multi-target tracking simulation results verify the effectiveness of the proposed method.
Keywords
Monte Carlo methods; particle filtering (numerical methods); target tracking; multitarget tracking algorithm; polynomial predictive particle multitarget multiBernoulli filter; sequential Monte Carlo MeMBer filter; state space model; Polynomials; Signal to noise ratio; Multi-target Multi-Bernoulli (MeMBer) filter; Multi-target tracking; Particle filter; Polynomial predictive;
fLanguage
English
Publisher
ieee
Conference_Titel
Mechanical and Electronics Engineering (ICMEE), 2010 2nd International Conference on
Conference_Location
Kyoto
Print_ISBN
978-1-4244-7479-0
Type
conf
DOI
10.1109/ICMEE.2010.5558603
Filename
5558603
Link To Document