.png){kind=icon}
返回
Wrapped Particle Filtering for Angular Data
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3200478.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Particle filtering is probably the most widely accepted methodology for general nonlinear filtering applications. The performance of a particle filter critically depends on the choice of proposal distribution. In this paper, we propose using a wrapped normal distribution as a proposal distribution for angular data, i.e. data within finite range (-pi, pi] . We then use the same method to derive the proposal density for a particle filter, in place of a standard assumed Gaussian density filter such as the unscented Kalman filter. The numerical integrals with respect to wrapped normal distribution are evaluated using Rogers-Szego quadrature. Compared to using the unscented filter and similar approximate Gaussian filters to produce proposal densities, we show through examples that wrapped normal distribution gives a far better filtering performance when working with angular data. In addition, we demonstrate the trade-off involved in particle filters with local sampling and global sampling (i.e. by running a bank of approximate Gaussian filters vs running a single approximate Gaussian filter) with the former yielding a better filtering performance than the latter at the cost of increased computational load.
Keyword:
Nonlinear dynamical systems
angular data
particle filtering
wrapped normal distribution
Rogers-Szego quadrature rule
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Substrate specificity characterization of a cdc2-like protein kinase purified from bovine brain.
OAAI
OAAI分享收藏Activity and expression pattern of cyclin-dependent kinase 5 in the embryonic mouse nervous system.png)
Development
DevelopmentIF0
PREAI
分享收藏
查看更多内容