Automatic Clustering for Precision Reconnaissance and Surveillance 정밀 감시정찰을 위한 자동 군집화 기법 개발

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 379
  • Download : 0
In this paper, we propose a coarse-to-fine approach-based automatic clustering algorithm for precision reconnaissance and surveillance. In order to improve the strengths and compensate for the weaknesses of the basic sequential algorithm and K-means clustering algorithm (the first one is simple to implement but may generate significant noise, whereas the second one returns precise results although the number of clusters needs to be known in advance), the proposed algorithm was developed by fusing them. It was validated and verified in a MATLAB simulation and embedded processing. As a result, it can be used to shrink the data effectively in a large dataset.
Publisher
Institute of Control, Robotics and Systems
Issue Date
2017-02
Language
Korean
Article Type
Article
Keywords

MATLAB; Unmanned aerial vehicles (UAV); Automatic clustering; Automatic clustering algorithm; Clustering; Embedded processing; K-Means clustering algorithm; Matlab simulations; Pointcloud; Sequential algorithm; Clustering algorithms

Citation

Journal of Institute of Control, Robotics and Systems, v.23, no.2, pp.89 - 95

ISSN
1976-5622
DOI
10.5302/J.ICROS.2017.16.0202
URI
http://hdl.handle.net/10203/225351
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0