文章基本信息

标题：Model Segmentation in Single Particle Tracking ⁎
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作者：Boris I. Godoy ; Nicholas A. Vickers ; Sean B. Andersson 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：20
页码：340-345
DOI：10.1016/j.ifacol.2021.11.197
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, we implement and compare two different change detection techniques applied to determining the time points in Single Particle Tracking (SPT) data where the particle changes the dynamic model of motion. The goal is to use this change detection to segment the data in order to estimate the relevant parameters of such models. We consider two well-known statistics commonly used for change detection: the likelihood ratio test (LRT) and the Kullback-Leibler divergence (KLD). We assume that our time-varying system is subject to step-like changes in the parameters that drive the process. The techniques are then applied to experimental data acquired on a microscope under controlled settings to validate our results.
关键词：KeywordsModellingIdentificationSignal ProcessingStochastic SystemsEstimation