Full text
Preview |
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (6MB) | Preview |
Rodríguez, Alfonso and Moreno González, Félix Antonio (2015). Evolutionary computing and particle filtering: a hardware-based motion estimation system. "IEEE Transactions on Computers", v. 64 (n. 11); pp. 3140-3152. ISSN 0018-9340. https://doi.org/10.1109/TC.2015.2401015.
Title: | Evolutionary computing and particle filtering: a hardware-based motion estimation system |
---|---|
Author/s: |
|
Item Type: | Article |
Título de Revista/Publicación: | IEEE Transactions on Computers |
Date: | 2015 |
ISSN: | 0018-9340 |
Volume: | 64 |
Subjects: | |
Freetext Keywords: | Embedded systems, evolutionary computing, FPGAs, particle filtering |
Faculty: | Centro de Electrónica Industrial (CEI) (UPM) |
Department: | Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
Preview |
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (6MB) | Preview |
Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. Evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed Evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm.
Item ID: | 41052 |
---|---|
DC Identifier: | https://oa.upm.es/41052/ |
OAI Identifier: | oai:oa.upm.es:41052 |
DOI: | 10.1109/TC.2015.2401015 |
Official URL: | http://ieeexplore.ieee.org/document/7035029/ |
Deposited by: | Memoria Investigacion |
Deposited on: | 03 Apr 2017 18:01 |
Last Modified: | 03 Apr 2017 18:01 |