Paper Info
Title
Speedup and tracking accuracy evaluation of parallel particle filter algorithms implemented on a multicore architecture
Abstract
Four different parallel particle filters such as globally distributed particle filter (GDPF), resampling with proportional allocation filter (RPA), resampling with non-proportional allocation filter (RNA) and the Gaussian particle filter (GPF), are studied in terms of speedup and tracking accuracy in a bearings-only tracking problem. The filters are implemented on a shared memory multicore computer, where the speedup is measured using up to eight cores. The tracking accuracy is studied in a simulated BOT application where the GPF exhibits best tracking accuracy, and RNA, RPA and GDPF give tracking accuracy comparable to the sequential particle filter. Both GPF and RNA appear to be capable of achieving linear speedup in the number of cores used, while RPA shows somewhat less encouraging speedup and GDPF is found to have a speedup limited to about 3 times.
Year
DOI
Venue
2010
10.1109/CCA.2010.5611217
Control Applications
Keywords
Field
DocType
particle filtering (numerical methods),shared memory systems,Gaussian particle filter,bearings-only tracking problem,globally distributed particle filter,multicore architecture,parallel particle filter algorithms,resampling with nonproportional allocation filter,resampling with proportional allocation filter,shared memory multicore computer,simulated BOT application,Multicore,Parallel algorithms,Particle filter,Tracking
Shared memory,Computer science,Parallel algorithm,Gaussian particle filter,Parallel computing,Particle filter,Algorithm,Multicore architecture,Resampling,Multi-core processor,Speedup
Conference
ISSN
ISBN
Citations 
1085-1992
978-1-4244-5363-4
7
PageRank 
References 
Authors
0.71
4
3
Name
Order
Citations
PageRank
Olov Rosen1234.10
Alexander Medvedev27222.43
Mats Ekman3162.49