Sequential Particle-Based Sum-Product Algorithm for Distributed Inference in Wireless Sensor Networks

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.