Algorithm design for parallel implementation of the SMC-PHD filter

Tiancheng Li, Shudong Sun, Miodrag Bolić, Juan M. Corchado

科研成果: 期刊稿件文章同行评审

94 引用 (Scopus)

摘要

The sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter suffers from low computational efficiency since a large number of particles are often required, especially when there are a large number of targets and dense clutter. In order to speed up the computation, an algorithmic framework for parallel SMC-PHD filtering based on multiple processors is proposed. The algorithm makes full parallelization of all four steps of the SMC-PHD filter and the computational load is approximately equal among parallel processors, rendering a high parallelization benefit when there are multiple targets and dense clutter. The parallelization is theoretically unbiased as it provides the same result as the serial implementation, without introducing any approximation. Experiments on multi-core computers have demonstrated that our parallel implementation has gained considerable speedup compared to the serial implementation of the same algorithm.

源语言英语
页(从-至)115-127
页数13
期刊Signal Processing
119
DOI
出版状态已出版 - 28 2月 2016

指纹

探究 'Algorithm design for parallel implementation of the SMC-PHD filter' 的科研主题。它们共同构成独一无二的指纹。

引用此