Name
Title | ||
|---|---|---|
Efficient Parallel Implementation of State Estimation Algorithms on Multicore Platforms. |
Abstract | ||
|---|---|---|
For many applications in signal processing and control it is crucial that estimates of the state vector in a dynamic system can be obtained in real time. This poses the problem of producing algorithms that are fast enough to enable online execution. In this article, it is investigated how two of the most popular and powerful state estimation algorithms, the Kalman filter and the particle filter, can be efficiently implemented in parallel on a multicore architecture. The proposed parallel implementations are analyzed in terms of hardware requirements, such as memory bandwidth and available cache memory, to provide the desired speedup. The algorithms are exemplified by and evaluated in an adaptive filtering and a bearings-only tracking application. In the cases when original algorithms have been modified for parallelization, the accuracy of the estimates obtained is evaluated in comparison with that of the sequential algorithm. It is found that linear speedup, in the number of cores used, can indeed be achieved without loss of accuracy, for both state estimation algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TCST.2011.2176735 | IEEE Trans. Contr. Sys. Techn. |
Keywords | Field | DocType |
Multicore processing,Random access memory,Cache memory,Signal processing algorithms,Algorithm design and analysis,Mathematical model | Analysis of parallel algorithms,State vector,Memory bandwidth,CPU cache,Computer science,Parallel computing,Algorithm,Kalman filter,Adaptive filter,Sequential algorithm,Speedup | Journal |
Volume | Issue | ISSN |
21 | 1 | 1063-6536 |
Citations | PageRank | References |
5 | 0.56 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olov Rosen | 1 | 23 | 4.10 |
| Alexander Medvedev | 2 | 72 | 22.43 |