Particle filtering based on sign of innovation for distributed estimation in binary Wireless Sensor Networks

Abstract

Distributed estimation is a major feature in wireless sensor networks (WSNs). Recently, hard quantized observations based on sign of innovation (SOI) were used to perform optimal distributed filtering involving thus the SOI Kalman filter (KF)/extended KF (EKF) [1]. In this paper, a SOI-particle filter (SOIPF) is derived to enhance the performance of the distributed estimation procedure. On one hand, the use of the particle filter avoids the imperative linearization in the EKF and on the other hand it guarantees a part of optimality for nonlinear/non Gaussian state models. The SOIPF proposed in this paper is applied in the target tracking context. The experimental results obtained for different simulations demonstrate the good tracking ability of the SOIPF compared to the SOIEKF as well as the consistency of the so given trajectory estimate.