Particle Filter with Differential Evolution for Trajectory Tracking

Lima, Leandro M. de; Krohling, Renato A.

doi:10.1007/978-3-642-20505-7_18

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Finally, the MIF2 algorithm itself should be studied to improve its speed and convergence. One core component of the MIF2 algorithm is the use of a symmetric perturbation distribution, which could be improved via integrating gradient information ala Hamiltonian MCMC (Neal, 1996) or by sharing information between particles ala a genetic optimization algorithm (de Lima & Krohling, 2011) . Improving the convergence of the MIF2 approach would relieve much of the burden on analysts to carefully tune the estimator, making these models more accessible to applied researchers.…”

Section: Discussionmentioning

confidence: 99%

Ordinal Outcome State-Space Models for Intensive Longitudinal Data

Henry,

Slipetz,

Falk

et al. 2024

Psychometrika

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Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day), over a period of time, allowing for the capture of the dynamics that underlie psychological and behavioral processes. One powerful framework for analyzing IL data is state-space modeling, where observed variables are considered measurements for underlying states (i.e., latent variables) that change together over time. However, state-space modeling has typically relied on continuous measurements, whereas psychological data often come in the form of ordinal measurements such as Likert scale items. In this manuscript, we develop a general estimation approach for state-space models with ordinal measurements, specifically focusing on a graded response model for Likert scale items. We evaluate the performance of our model and estimator against that of the commonly used “linear approximation” model, which treats ordinal measurements as though they are continuous. We find that our model resulted in unbiased estimates of the state dynamics, while the linear approximation resulted in strongly biased estimates of the state dynamics. Finally, we develop an approximate standard error, termed slice standard errors and show that these approximate standard errors are more liberal than true standard errors (i.e., smaller) at a consistent bias.

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Section: Discussionmentioning

confidence: 99%

Ordinal Outcome State-Space Models for Intensive Longitudinal Data

Henry,

Slipetz,

Falk

et al. 2024

Psychometrika

View full text Add to dashboard Cite

show abstract

“…The actual data is usually very complex, and contains many factors such as non-Gaussian, nonlinear, high dimensional and noise; in this case, the Kalman filter generally can't get the analytical solution. This is a common problem and appears in many different kinds of fields [2][3][4][5][6][7][8][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

The Data Processing of Single-yarn Strength Testing Based on the Particle Filter Algorithm

Li¹,

Liu²

2012

JDCTA

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Aiming at a nonlinear/non-Gaussian filter problem, the data processing of a single-yarn strength testing system, a filtering method based on the particle filter algorithm is put forward. This paper expounds the principle, and the working process and the procedures of particle filter. It discusses in detail the application of particle filter in the single-yarn strength testing, the modeling of testing system state equation, and the implementation of this algorithm in a project. It also indicates that particle filter algorithm used to process single-yarn strength test data achieves a good effect and satisfactory filtering precision by simulated tests. Finally, this paper predicts that particle filter will play an important role in general data processing and analyzing.

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A hybrid occlusion free object tracking method using particle filter and modified galaxy based search meta-heuristic algorithm

Sardari

Moghaddam

2017

Applied Soft Computing

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Particle Filter with Differential Evolution for Trajectory Tracking

Cited by 4 publications

References 8 publications

Ordinal Outcome State-Space Models for Intensive Longitudinal Data

Ordinal Outcome State-Space Models for Intensive Longitudinal Data

The Data Processing of Single-yarn Strength Testing Based on the Particle Filter Algorithm

A hybrid occlusion free object tracking method using particle filter and modified galaxy based search meta-heuristic algorithm

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