DocumentCode
554102
Title
Notice of Retraction
Adaptive weight particle filter for nor-linear noisy signals
Author
Xiangsheng Kong ; Jing Sun
Author_Institution
Dept. of Comput. Inf., Xin Xiang Univ., Xin Xiang, China
Volume
2
fYear
2011
fDate
26-28 July 2011
Firstpage
677
Lastpage
680
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.
The particle filtering(PF) does well in denoising the non-linear disturbed signals. Signals with non-Gauss noise can´t be done by Kalman filtering (KF),but they could be done by PF. The theory is widely used in the field of chaos signal denoise and target identification. But as the observing time extend, the PF will have problems with sample degeneration weight degeneracy. The paper presents an adaptive weight particle filtering (AWPF) theory which selects the samples using self-adaptive weight method. It makes the fission from samples with high weight value. The approach improves the estimation accuracy without decreasing computing speed.
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.
The particle filtering(PF) does well in denoising the non-linear disturbed signals. Signals with non-Gauss noise can´t be done by Kalman filtering (KF),but they could be done by PF. The theory is widely used in the field of chaos signal denoise and target identification. But as the observing time extend, the PF will have problems with sample degeneration weight degeneracy. The paper presents an adaptive weight particle filtering (AWPF) theory which selects the samples using self-adaptive weight method. It makes the fission from samples with high weight value. The approach improves the estimation accuracy without decreasing computing speed.
Keywords
particle filtering (numerical methods); signal denoising; adaptive weight particle filter; chaos signal denoise; nonGauss noise; nonlinear disturbed signals; nonlinear noisy signals; particle filtering; target identification; Accuracy; Chaos; Computational modeling; Educational institutions; Filtering; Noise; Noise measurement; adaptive weight particle filtering; non-linear filtering; sample degeneration; signal process; weight degeneracy;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022285
Filename
6022285
Link To Document