Classifying the weights of particle filters in nonlinear systems

Sharifian, M.; Rahimi, Abdolah; Pariz, Naser

doi:10.1016/j.cnsns.2015.05.021

Cited by 11 publications

(5 citation statements)

References 21 publications

(21 reference statements)

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“…The particle filter solves this problem; in fact, the particle filter has been developed to numerically implement the Bayesian state estimator. This filter estimates signals by sampling, these samples are called particles [32][33][34]. The sampling process is performed on the system's dynamic equation, and the samples are weighted using the measurement equation, then based on these samples and their weights, the optimal estimate of the stochastic signal is obtained.…”

Section: Standard Particle Filtermentioning

confidence: 99%

“…The PDF of the process noise (ω) and the measurement noise (υ) are not necessarily Gaussian, and just knowing the distribution is enough and the type of distribution is not important. With these assumptions, the particle filter algorithm estimates the states of the system as follows [33,34]. In these steps, note that p(..) means the probability distribution function, not the probability value.…”

Section: Standard Particle Filtermentioning

confidence: 99%

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Irradiation and Temperature Estimation with a New Extended Kalman Particle Filter for Maximum Power Point Tracking in Photovoltaic Systems

Hooshmand

Yaghobi

Jazaeri

2023

IJE

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In this paper, a new method, based on the estimation of irradiation and temperature values, was proposed for Maximum Power Point Tracking (MPPT) in photovoltaic systems. The proposed estimation method is based on a new Extended Kalman Particle Filter (EKPF). Given that the basis of the proposed method is a particle filter, firstly, the estimation is performed with high accuracy, although the target system has severe nonlinearity; secondly, there is no limitation for the probability density functions of the measurement and process noise. This method works for Gaussian and non-Gaussian noises. To show the estimation accuracy, the proposed method will be compared with the common method based on extended Kalman filter (EKF) and both methods will be evaluated due to the root means square error criterion. Due to the accurate estimation, MPPT is performed with good performance. For validation, the proposed MPPT method was compared with the EKF method and the conventional incremental conductance (InC) method. The simulations show that the efficiency is improved from 0.1% to 1% compared to the EKF, and from 0.8% to 8.65% compared to the InC method, which shows the performance of the proposed MPPT method in noisy environments.

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Section: Standard Particle Filtermentioning

confidence: 99%

Section: Standard Particle Filtermentioning

confidence: 99%

Irradiation and Temperature Estimation with a New Extended Kalman Particle Filter for Maximum Power Point Tracking in Photovoltaic Systems

Hooshmand

Yaghobi

Jazaeri

2023

IJE

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“…Scholars have made efforts to improve the PF algorithm by eliminating the effect of the sample impoverishment. In [22], the study attempts to overcome sample impoverishment by classifying the particles based on their weights. A clustering approach is utilized to implement the particles conversion.…”

Section: B Aga-pf Algorithmmentioning

confidence: 99%

Fault Diagnosis and RUL Prediction of Nonlinear Mechatronic System via Adaptive Genetic Algorithm-Particle Filter

Jiang

et al. 2019

IEEE Access

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This paper proposes a real-time model-based health monitoring method for a nonlinear mechatronic system with multiple faults in both parametric and nonparametric components. A nonlinear bond graph model incorporating the influence of Stribeck friction is established to capture the dynamic behavior of the monitored mechatronic system. Based on the model, fault diagnosis is carried out via the combinative fault signature matrix which is built from independent and dependent analytical redundancy relations to enhance the isolation ability of the monitored system under multiple-fault condition. After that, an adaptive genetic algorithm-particle filter (AGA-PF) is developed for fault parameter estimation and remaining useful life prediction. The AGA-PF can mitigate the sample impoverishment problem in generic particle filter through genetic operators with adaptive mutation probability according to the fitness of the particles. The effectiveness of the proposed method is verified through simulation and experiment investigations on the nonlinear mechatronic system. INDEX TERMS Fault diagnosis, genetic algorithm, particle filter, parameter estimation. I. INTRODUCTION

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“…Resampling aims to prevent the propagated particles' degeneracy by altering the random measure X t to X t and enhancing the state space examination at t + 1. While addressing degeneracy during resampling, it is also important for the random measure to approximate the original distribution as precisely as possible so that bias in the estimates can be prevented [40][41][42][43]. Although the X t approximation closely resembles that of X t , the set of X t particles have important variations from that of X t .…”

Section: Resamplingmentioning

confidence: 99%

Adaptive memory-based single distribution resampling for particle filter

et al. 2017

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IntroductionOver the past two decades, particle filtering has emerged as a procedure for sequential signal processing (Refs. [1][2][3][4] presents the review). Particle filter has also become popular because it is capable of processing observations signified by nonlinear state-space models. In such models, the noises can be non-Gaussian. Several fields have adopted this methodology including: finance [5][6][7][8], wireless communications [9][10][11][12], geophysical systems [13][14][15][16][17], navigation and tracking [18][19][20], control [21][22][23][24][25], and robotics [26][27][28][29][30][31]. Generally, this methodology can approximate state density p(x k ) using a range of random particles that have related nonnegative weights: AbstractThe restrictions that are related to using single distribution resampling for some specific computing devices' memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices' memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices' physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle's particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.

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Classifying the weights of particle filters in nonlinear systems

Cited by 11 publications

References 21 publications

Irradiation and Temperature Estimation with a New Extended Kalman Particle Filter for Maximum Power Point Tracking in Photovoltaic Systems

Irradiation and Temperature Estimation with a New Extended Kalman Particle Filter for Maximum Power Point Tracking in Photovoltaic Systems

Fault Diagnosis and RUL Prediction of Nonlinear Mechatronic System via Adaptive Genetic Algorithm-Particle Filter

Adaptive memory-based single distribution resampling for particle filter

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