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This paper deals with the problem of Distributed Estimation (DE) in binary Wireless Sensor Networks (WSN). Specifically, we propose to enhance the well-known Sign Of Innovation (SOI) based Extended Kalman Filter (SOI-EKF) [1] using the Unscented Transformation (UT) and iterative processing. The unscented transformation is especially used here to boost the quality of the predicted observation, already...
This paper deals with target tracking in a binary Wireless Sensor Network (WSN) context. In particular, we propose to study the performance of the Sign Of Innovation Particle Filter (SOI-PF) algorithm in a noisy context. This parallel algorithm based on the exchange of only one bit by instant between the sensors, has shown its efficiency for target tracking in a highly non linear and noiseless framework...
This paper deals with target tracking in a binary wireless sensor network. In this contribution, the target motion is represented by a non linear model based on a jump-Markovian direction. The observation of the trajectory based on the target signal strength is performed by binary sensors. In particular, only one sensor emits one bit by instant what increases the longevity of the wireless sensor network...
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,...
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