 Measurement-converted Kalman filter tracking with Gaussian intensity attenuation signal in wireless sensor networksSha Wen, Liqiang Xing, Xiaoqing Hu and Hui Zhang International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 4, 1550147717700896 Abstract: In this article, the target tracking problem in a wireless sensor network with nonlinear Gaussian signal intensity attenuation model is considered. A Bayesian filter tracking algorithm is presented to estimate the locations of moving source that has unknown central signal intensity. This approach adopts a measurement conversion method to remove the measurement nonlinearity by the maximum likelihood estimator, and a linear estimate of the target position and its associated noise statistics obtained by the Newton–Raphson iterative optimization steps are applied into the standard Kalman filter. The Monte Carlo simulations have been conducted in comparison with the commonly used extended Kalman filter with an augmented state that consists of both the original target state and the augmentative central signal intensity. It is observed that the proposed measurement-converted Kalman filter can yield higher accurate estimate and nicer convergence performance over existing methods. Keywords: Target tracking; wireless sensor networks; maximum likelihood estimation; extended Kalman filter; Gaussian attenuation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:4:p:1550147717700896 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.