Speedup and tracking accuracy evaluation of parallel particle filter algorithms implemented on a multicore architecture

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 i...

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Bibliographic Details
Main Authors: Rosén, Olov, Medvedev, Alexander, Ekman, Mats
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Atmospheric measurements
Automatic control
Computer science
Datavetenskap
Information technology
Informationsteknik
Multicore
Multicore processing
Parallel algorithms
Particle filter
Particle measurements
Reglerteknik
RNA
Target tracking
TECHNOLOGY
TEKNIKVETENSKAP
Tin
Tracking
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Summary: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.
ISSN:1085-1992
2576-3210
DOI:10.1109/CCA.2010.5611217