A Sequential Estimation Algorithm of Particle Filters by Combination of Multiple Independent Features in Evidence
- Authors
- Kang, Hoon; Lee, Hyun Su; Kwon, Young-Bin; Park, Ye Hwan
- Issue Date
- Jun-2018
- Publisher
- INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
- Keywords
- Multiple features; particle filters; sensor fusion; sequential estimation; visual tracking
- Citation
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.16, no.3, pp 1263 - 1270
- Pages
- 8
- Journal Title
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
- Volume
- 16
- Number
- 3
- Start Page
- 1263
- End Page
- 1270
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/2126
- DOI
- 10.1007/s12555-016-0644-z
- ISSN
- 1598-6446
2005-4092
- Abstract
- We investigate a robust sequential estimation algorithm of particle filters, which combine multiple features of visual objects, in order to obtain reliable evidential information from independent sources of sensor data. Most of particle filter algorithms are based on conditional density propagation in Bayesian inference rules. In this paper, it is modified by the conjunctive rule of independent features. Therefore, the proposed algorithm is more reliable since it demonstrates the solution to both efficiency depletion and over-sampling in particle filters.
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- Appears in
Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
- College of Software > School of Computer Science and Engineering > 1. Journal Articles
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