Show simple item record

dc.contributor.authorTong, Xianqiaoen_US
dc.contributor.authorFurukawa, Tomonarien_US
dc.contributor.authorDurrant-Whyte, Hughen_US
dc.identifier.citationJournal of Uncertainty Analysis and Applications. 2013 Dec 16;1(1):15en_US
dc.description.abstractThis paper presents the performance modeling of the real-time grid-based recursive Bayesian estimation (RBE), particularly the parallel computation using graphics processing unit (GPU). The proposed modeling formulates data transmission between the central processing unit (CPU) and the GPU as well as floating point operations to be carried out in each CPU and GPU necessary for one iteration of the real-time grid-based RBE. Given the specifications of the computer hardware, the proposed modeling can thus estimate the total amount of time cost for performing the grid-based RBE in a real-time environment. A new prediction formulation, which adopted separable convolution, is proposed to further accelerate the real-time grid-based RBE. The performance of the proposed modeling was investigated, and parametric studies have first demonstrated its validity in various conditions by showing that the average error of estimation in computational performance stays below 6% to 7%. Utilizing the prediction with separable convolution, the grid-based RBE has also been found to perform within 1 ms, although the size of the problem was relatively large.en_US
dc.rightsCreative Commons Attribution 4.0 International*
dc.titleComputational modeling for parallel grid-based recursive Bayesian estimation: parallel computation using graphics processing uniten_US
dc.typeArticle - Refereed
dc.description.versionPeer Reviewed
dc.rights.holderXianqiao Tong et al.; licensee BioMed Central Ltd.en_US
dc.title.serialJournal of Uncertainty Analysis and Applications

Files in this item


This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International