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- Nordh, JerkerLund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH (author)
pyParticleest : A Python framework for particle-based estimation methods
- Article/chapterEnglish2017
Publisher, publication year, extent ...
- 2017
- Foundation for Open Access Statistic,2017
- 25 s.
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- LIBRIS-ID:oai:lup.lub.lu.se:34c7e088-faa0-4b1c-abb8-30809d0ab772
- https://lup.lub.lu.se/record/34c7e088-faa0-4b1c-abb8-30809d0ab772URI
- https://doi.org/10.18637/jss.v078.i03DOI
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- Language:English
- Summary in:English
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- Particle methods such as the particle filter and particle smoothers have proven very useful for solving challenging nonlinear estimation problems in a wide variety of fields during the last decade. However, there are still very few existing tools available to support and assist researchers and engineers in applying the vast number of methods in this field to their own problems. This paper identifies the common operations between the methods and describes a software framework utilizing this information to provide a flexible and extensible foundation which can be used to solve a large variety of problems in this domain, thereby allowing code reuse to reduce the implementation burden and lowering the barrier of entry for applying this exciting field of methods. The software implementation presented in this paper is freely available and permissively licensed under the GNU Lesser General Public License, and runs on a large number of hardware and software platforms, making it usable for a large variety of scenarios.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Programvaruteknik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Software Engineering hsv//eng
- Expectation-maximization
- Particle filter
- Particle smoother
- Python
- Rao-Blackwellized
- System identification
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- Institutionen för reglerteknikInstitutioner vid LTH (creator_code:org_t)
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- In:Journal of Statistical Software: Foundation for Open Access Statistic781548-7660
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