pyParticleest : A Python framework for particle-based estimation methods
(2017) In Journal of Statistical Software 78.- Abstract
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... (More)
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.
(Less)
- author
- Nordh, Jerker LU
- organization
- publishing date
- 2017
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Expectation-maximization, Particle filter, Particle smoother, Python, Rao-Blackwellized, System identification
- in
- Journal of Statistical Software
- volume
- 78
- pages
- 25 pages
- publisher
- University of California at Los Angeles
- external identifiers
-
- wos:000405335300001
- scopus:85020435054
- ISSN
- 1548-7660
- DOI
- 10.18637/jss.v078.i03
- language
- English
- LU publication?
- yes
- id
- 34c7e088-faa0-4b1c-abb8-30809d0ab772
- date added to LUP
- 2017-06-29 11:24:53
- date last changed
- 2024-07-21 23:49:22
@article{34c7e088-faa0-4b1c-abb8-30809d0ab772, abstract = {{<p>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.</p>}}, author = {{Nordh, Jerker}}, issn = {{1548-7660}}, keywords = {{Expectation-maximization; Particle filter; Particle smoother; Python; Rao-Blackwellized; System identification}}, language = {{eng}}, publisher = {{University of California at Los Angeles}}, series = {{Journal of Statistical Software}}, title = {{pyParticleest : A Python framework for particle-based estimation methods}}, url = {{http://dx.doi.org/10.18637/jss.v078.i03}}, doi = {{10.18637/jss.v078.i03}}, volume = {{78}}, year = {{2017}}, }