Sequential Abruptly Changing Hidden States Estimation using Adaptive Particle Impoverishment Mitigation Scheme

Chanin Kuptametee, Nattapol Aunsri
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Abstract

Particle filtering (PF) is a sequential Monte Carlo (SMC) method that infers the states of the hidden parameters of interest from the posterior probability distribution functions (PDFs) given the noisy measurement data obtained from any non-linear systems. Each sample of parameter states (called particle) is randomly drawn, so their likelihoods can be different from each other and particle degeneracy may happen. Resampling eliminates the particles that have low likelihoods and replicates those having high likelihoods. Particle impoverishment is a side-effect where particle diversity is destroyed and all of these particles are likely to have low likelihoods, especially if the true state abnormally evolves. This paper proposes an adaptive scheme that relocates the particles when they are located far away from the maximum likelihood value to mitigate the particle impoverishment. The proposed scheme provides satisfactory effectiveness in discovering the abruptly changing hidden states and satisfactory estimation accuracy.
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基于自适应粒子贫困化缓解方案的序列突变隐藏状态估计
粒子滤波(PF)是一种序列蒙特卡罗(SMC)方法,它从给定非线性系统的噪声测量数据的后验概率分布函数(pdf)中推断出感兴趣的隐藏参数的状态。参数状态的每个样本(称为粒子)是随机抽取的,因此它们的似然可能彼此不同,并且可能发生粒子简并。重新采样消除可能性低的粒子,复制可能性高的粒子。粒子贫困化是粒子多样性被破坏的副作用,所有这些粒子的可能性都很低,尤其是在真实状态异常演变的情况下。本文提出了一种自适应方案,当粒子远离最大似然值时,对粒子进行重新定位,以减轻粒子的贫化。该方法在发现突变隐藏状态方面具有满意的有效性和估计精度。
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来源期刊
Transactions on Electrical Engineering, Electronics, and Communications
Transactions on Electrical Engineering, Electronics, and Communications Engineering-Electrical and Electronic Engineering
CiteScore
1.60
自引率
0.00%
发文量
45
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