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
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.
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;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022285
