New Grid for Particle Filtering of Multivariable Nonlinear Objects

Kozierski, Piotr; Michalski, Jacek; Sadalla, Talar; Giernacki, Woiciech; Ziętkiewicz, Joanna; Drgas, Szymon

doi:10.15439/2018f308

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…The plant contains 4 state variables (is composed of 4 nodes). This system was proposed and described as the 4-th version of system presented in [9].…”

Section: Simulation Resultsmentioning

confidence: 99%

Double hybrid Kalman filtering for state estimation of dynamical systems

2019

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In this paper authors present a new approaches to the hybrid Kalman filtering and modified hybrid Kalman filtering, with the changed order of methods inside (Unscented Kalman Filter and Extended Kalman Filter). For these algorithms, the modification based on double use of Hybrid Kalman Filters (Excented and Unscented) has been proposed. This new modification has been checked for Hybrid Kalman Particle Filters too, for the variable number of particles. Based on the obtained results, one can see that duplication of hybrid filters can improve the estimation quality.

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“…The plant contains 4 state variables (is composed of 4 nodes). This system was proposed and described as the 4-th version of system presented in [9].…”

Section: Simulation Resultsmentioning

confidence: 99%

Double hybrid Kalman filtering for state estimation of dynamical systems

2019

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“…Ob3 has been proposed (in each of the following 5 versions) in [44]. This object has been created in a network structure; the precursor for it is a power system grid.…”

Section: Examined Objectsmentioning

confidence: 99%

MultiPDF particle filtering in state estimation of nonlinear objects

et al. 2021

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This paper presents a new particle filter algorithm (MultiPDF) for state estimation of nonlinear systems. The proposed method is a modification of the standard particle filter approach. Due to the strong need for the acceleration of calculations and an improvement in the estimation quality of state estimation, the authors propose a method which enables one to divide the main particle filter into smaller sub-filters with an accordingly smaller number of particles for each one of them. The algorithm has been implemented for various numbers of particles and subordinate parallel filters. Estimation quality has been checked for nine nonlinear objects (both one- and multidimensional) and evaluated through the quality index, average root-mean-squared error. The computation time of the particle filter algorithm for several hardware configurations has been compared. Based on the obtained results, it can be concluded that, besides the computation acceleration, the parallelization of the particle filter’s operation also improves the estimation quality.

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“…This paper is a greatly extended version of the article presented during the 13th Federated Conference on Computer Science and Information Systems (FedCSIS 2018) [20].…”

Section: Introductionmentioning

confidence: 99%

“…All nodal measurements proposed in the new network (P i , Q i , R i , S i , T i ) are described by (20)- (24), and examples of the designations on the scheme are presented in Table 5; whereas all proposed branch measurements are written by (25)- (29), and the exemplary designations are presented in Table 6.…”

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confidence: 99%

A New Network for Particle Filtering of Multivariable Nonlinear Objects †

et al. 2020

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In this paper, a new object in the form of a theoretical network is presented, which is useful as a benchmark for particle filtering algorithms designed for multivariable nonlinear systems (potentially linear, nonlinear, and even semi-Markovian jump system). The main goal of the paper is to propose an object that potentially can have similar to the power system grid properties, but with the number of state variables reduced twice (only one state variable for each node, while there are two in the case of power systems). Transition and measurement functions are proposed in the paper, and two types of transition functions are considered: dependent on one or many state variables. In addition, 10 types of measurements are proposed both for branch and nodal cases. The experiments are performed for 14 different, four-dimensional systems. Plants are both linear and highly nonlinear. The results include information about the state estimation quality (based on the mean squared error indicator) and the values of the effective sample size. It is observed how the higher effective sample size resulted in the better estimation quality in subsequent cases. It is also concluded that the very low number of significant particles is the main problem in particle filtering of multivariable systems, and this should be countered. A few potential solutions for the latter are also presented.

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New Grid for Particle Filtering of Multivariable Nonlinear Objects

Cited by 3 publications

References 12 publications

Double hybrid Kalman filtering for state estimation of dynamical systems

Double hybrid Kalman filtering for state estimation of dynamical systems

MultiPDF particle filtering in state estimation of nonlinear objects

A New Network for Particle Filtering of Multivariable Nonlinear Objects †

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