.png){kind=icon}
Return
Sequential Particle-Based Sum-Product Algorithm for Distributed Inference in Wireless Sensor Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2012.2221484.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Graphical models have been widely applied in solving distributed inference problems in wireless sensor networks (WSNs). In this paper, the factor graph (FG) is employed to model a distributed inference problem. Using particle filtering methods, a sequential particle-based sum-product algorithm (SPSPA) is proposed for distributed inference in FGs with continuous variables and nonlinear local functions. Importance sampling methods are used to sample from message products, and the computational complexity of SPSPA is thus linear in the number of particles. The SPSPA is applied to a distributed tracking problem, and its performance is evaluated based on the number of particles and the measurement noise.
Keywords:
Distributed inference
factor graph (FG)
particle filtering
sum-product algorithm (SPA)
target tracking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
View more