Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks

Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks

Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few work...

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Bibliographic Details
Main Authors: Xiaofan Li, Yubin Zhao, Sha Zhang, Xiaopeng Fan
Format: Article
Language:English
Published: MDPI AG 2016-05-01
Series:Sensors
Subjects:
particle filter
nonparametric estimation
indoor positioning
wireless sensor networks
Kullback–Leibler divergence
dynamic Gaussian model
Online Access:http://www.mdpi.com/1424-8220/16/6/786

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http://www.mdpi.com/1424-8220/16/6/786

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