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The accuracy of particle filtering estimation for the tracking system is prone to be influenced by the high measurement noise (or errors) in wireless sensor networks (WSNs). We first analyze the impact of instantaneous measurement noise which is introduced into the likelihood function and biases the particle filtering estimation. Based on our analysis, we propose a likelihood adaptation method considering...
In this paper, we propose a robust and efficient particle filtering framework for indoor localization in wireless sensor network (WSN) which searches effective anchors and constructs particle filter dynamically. Within this framework, three algorithms are integrated into the dynamic particle filter: anchor selection algorithm, location constraint resampling and SIR particle filter. The proposed scheme...
Sensor devices suffer severe interference due to multi path effect, non line of sight (NLOS) and variations of the wireless propagation environment in indoor positioning systems. These interferences lead to measurement error during sensor measurement. Kalman filter (KF) and extended Kalman filter (EKF) have been widely used in tracking systems to reduce measurement noise. However, KF and EKF assume...
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