Performance Analysis of Resampling Algorithms of Parallel/Distributed Particle Filters

Particle filters have been widely used in various fields due to their advantages in dealing with non-linear and/or non-Gaussian systems. A large number of particles are needed to guarantee the convergence of particle filters for the state estimation, especially for large-scale complex systems. There...

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Veröffentlicht in:	IEEE access Jg. 9; S. 4711 - 4725
Hauptverfasser:	Zhang, Xudong, Zhao, Liang, Zhong, Wei, Gu, Feng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Complex systems Complexity Complexity theory Computer science Kalman filters Monte Carlo methods Parallel processing Parallel/distributed particle filters performance analysis Performance enhancement Resampling Signal processing algorithms State estimation state size system complexity Target tracking
ISSN:	2169-3536, 2169-3536
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Abstract	Particle filters have been widely used in various fields due to their advantages in dealing with non-linear and/or non-Gaussian systems. A large number of particles are needed to guarantee the convergence of particle filters for the state estimation, especially for large-scale complex systems. Therefore, parallel/distributed particle filters were adopted to improve the performance, in which different paradigms of resampling were proposed, including the centralized resampling, the decentralized resampling, and the hybrid resampling. To ease their adoptions, we analyze time consumptions and speedup factors of parallel/distributed particle filters with various resampling algorithms, state sizes, system complexities, numbers of processing units, and model dimensions in this study. The experimental results indicate that the decentralized resampling achieves the highest speedup factors due to the local transfer of particles, the centralized resampling always has the lowest speedup factors because of the global transfer of particles, and the hybrid resampling attains the speedup factors between. Moreover, we define the complexity-state ratio, as the ratio between the system complexity and the system state size to study how it impacts the speedup factor. The experiments show that the higher complexity-state ratio results in the increase of the speedup factors. This is one of the earliest attempts to analyze and compare the performance of parallel/distributed particle filters with different resampling algorithms. The analysis can provide potential solutions for further performance improvements and guide the appropriate selection of the resampling algorithm for parallel/distributed particle filters.
AbstractList	Particle filters have been widely used in various fields due to their advantages in dealing with non-linear and/or non-Gaussian systems. A large number of particles are needed to guarantee the convergence of particle filters for the state estimation, especially for large-scale complex systems. Therefore, parallel/distributed particle filters were adopted to improve the performance, in which different paradigms of resampling were proposed, including the centralized resampling, the decentralized resampling, and the hybrid resampling. To ease their adoptions, we analyze time consumptions and speedup factors of parallel/distributed particle filters with various resampling algorithms, state sizes, system complexities, numbers of processing units, and model dimensions in this study. The experimental results indicate that the decentralized resampling achieves the highest speedup factors due to the local transfer of particles, the centralized resampling always has the lowest speedup factors because of the global transfer of particles, and the hybrid resampling attains the speedup factors between. Moreover, we define the complexity-state ratio, as the ratio between the system complexity and the system state size to study how it impacts the speedup factor. The experiments show that the higher complexity-state ratio results in the increase of the speedup factors. This is one of the earliest attempts to analyze and compare the performance of parallel/distributed particle filters with different resampling algorithms. The analysis can provide potential solutions for further performance improvements and guide the appropriate selection of the resampling algorithm for parallel/distributed particle filters.
Author	Zhong, Wei Gu, Feng Zhang, Xudong Zhao, Liang
Author_xml	– sequence: 1 givenname: Xudong orcidid: 0000-0002-0078-8067 surname: Zhang fullname: Zhang, Xudong organization: Department of Computer Science, Graduate Center of The City University of New York, New York, NY, USA – sequence: 2 givenname: Liang surname: Zhao fullname: Zhao, Liang organization: Department of Computer Science, Lehman College, Bronx, NY, USA – sequence: 3 givenname: Wei orcidid: 0000-0002-6199-1376 surname: Zhong fullname: Zhong, Wei organization: Division of Math and Computer Science, University of South Carolina Upstate, Spartanburg, SC, USA – sequence: 4 givenname: Feng surname: Gu fullname: Gu, Feng email: feng.gu@csi.cuny.edu organization: Department of Computer Science, College of Staten Island, Staten Island, NY, USA
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SubjectTerms	Algorithms Complex systems Complexity Complexity theory Computer science Kalman filters Monte Carlo methods Parallel processing Parallel/distributed particle filters performance analysis Performance enhancement Resampling Signal processing algorithms State estimation state size system complexity Target tracking
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Title	Performance Analysis of Resampling Algorithms of Parallel/Distributed Particle Filters
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