Adaptive weight particle filter for nor-linear noisy signals

机译：非线性噪声信号的自适应权重粒子滤波器

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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.

机译：粒子滤波（PF）在去噪非线性干扰信号方面做得很好。具有非高斯噪声的信号无法通过卡尔曼滤波（KF）来完成，但可以通过PF来完成。该理论被广泛应用于混沌信号降噪和目标识别领域。但是随着观察时间的延长，PF会出现样品变性权重简并化的问题。本文提出了一种自适应加权粒子滤波（AWPF）理论，该理论采用自适应加权方法选择样本。它使样品具有高重量值而发生裂变。该方法在不降低计算速度的情况下提高了估计精度。

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来源
《2011 Seventh International Conference on Natural Computation》|2011年|p.677-680|共4页
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Kong Xiangsheng; Sun Jing;
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正文语种
中图分类理论、方法;
关键词
adaptive weight particle filtering; non-linear filtering; sample degeneration; signal process; weight degeneracy;

机译：自适应权重粒子滤波;非线性滤波;样本退化;信号处理;权重退化;

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3. Signal adaptive filter for noisy images based on fuzzy S- and T-norm operators [J] . Hasanzadeh R.P.R., Reza A.M. Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic . 2013,第7期

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4. Adaptive weight particle filter for nor-linear noisy signals [C] . Kong Xiangsheng, Sun Jing International Conference on Natural Computation . 2011

机译：用于NOR-LINEAR嘈杂信号的自适应重量粒子滤波器
5. IDENTIFICATION AND RESTORATION OF NOISY AND BLURRED IMAGES (ARMA MODEL, SPECTRAL FACTORIZATION, MAXIMUM LIKELIHOOD, KALMAN FILTERING, ADAPTIVE). [D] . TEKALP, AHMET MURAT. 1984

机译：识别和恢复嘈杂和模糊的图像（ARMA模型，光谱分解，最大相似度，卡尔曼滤波，自适应）。
6. Wavelets filtering for classification of very noisy electron microscopic single particles images- application on structure determination of VP5-VP19C recombinant [O] . Ali Samir Saad 2003

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7. Sliding Mode Tracking Differentiator With Adaptive Gains for Filtering and Derivative Estimation of Noisy Signals [O] . Jingdong Yu, Shanhai Jin 2021

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Adaptive weight particle filter for nor-linear noisy signals

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