期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2017
卷号：13
期号：4
页码：1
DOI：10.1177/1550147717700896
出版社：Hindawi Publishing Corporation
摘要：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.
