GPU-accelerated particle filter for multi-sensor multi-target tracking 다중 센서 다중 표적 추적을 위한 GPU 가속 파티클 필터

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 225
  • Download : 0
This paper addresses particle filtering for multi-sensor multi-target tracking implemented on a graphics processing unit (GPU) architecture. The GPU-accelerated particle filter is constructed as a distributed computation particle filter in which resampling is done in a distributed manner. The simulation result gives a comparison of the tracking accuracy and computation speed of the GPU-accelerated particle filtering to those of standard sequential CPU implementations. We analyze the effect of the resampling group size, which is the most important parameter in the distributed computation particle filter. While significant speedup is observed in GPU-accelerated particle filtering, the group size provides a trade-off between tracking accuracy and computation speed.
Publisher
Institute of Control, Robotics and Systems
Issue Date
2017-04
Language
English
Article Type
Article
Keywords

Bandpass filters; Clutter (information theory); Computer graphics; Computer graphics equipment; Distributed computer systems; Economic and social effects; Graphics processing unit; Monte Carlo methods; Particle accelerators; Particle size analysis; Program processors; Signal filtering and prediction; Tracking (position); Computation speed; Distributed computations; Distributed particle filter; GPU accelerations; Group size; Multi-target tracking; Particle Filtering; Tracking accuracy; Target tracking

Citation

Journal of Institute of Control, Robotics and Systems, v.23, no.3, pp.152 - 156

ISSN
1976-5622
DOI
10.5302/J.ICROS.2017.16.0193
URI
http://hdl.handle.net/10203/238188
Appears in Collection
AE-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