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Issue No.04 - April (2012 vol.11)
pp: 567-576
Xingbo Wang , Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China
Minyue Fu , Sch. of Electr. Eng. & Comput. Sci., Univ. of Newcastle, Callaghan, NSW, Australia
Huanshui Zhang , Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China
ABSTRACT
A common technical difficulty in target tracking in a wireless sensor network is that individual homogeneous sensors only measure their distances to the target whereas the state of the target composes of its position and velocity in the Cartesian coordinates. That is, the senor measurements are nonlinear in the target state. Extended Kalman filtering is a commonly used method to deal with the nonlinearity, but this often leads to unsatisfactory or even unstable tracking performances. In this paper, we present a new target tracking approach which avoids the instability problem and offers superior tracking performances. We first propose an improved noise model which incorporates both additive noises and multiplicative noises in distance sensing. We then use a maximum likelihood estimator for prelocalization to remove the sensing nonlinearity before applying a standard Kalman filter. The advantages of the proposed approach are demonstrated via experimental and simulation results.
INDEX TERMS
wireless sensor networks, distance measurement, Kalman filters, maximum likelihood estimation, radiofrequency measurement, target tracking, maximum likelihood estimator, wireless sensor networks, target tracking, MLE, KF, distance measurements, Cartesian coordinates, extended Kalman filtering, instability problem, improved noise model, multiplicative noises, additive noises, distance sensing, Sensors, Noise, Wireless sensor networks, Target tracking, Noise measurement, Kalman filters, Maximum likelihood estimation, extended Kalman filtering., Target tracking, wireless sensor networks, maximum likelihood estimation
CITATION
Xingbo Wang, Minyue Fu, Huanshui Zhang, "Target Tracking in Wireless Sensor Networks Based on the Combination of KF and MLE Using Distance Measurements", IEEE Transactions on Mobile Computing, vol.11, no. 4, pp. 567-576, April 2012, doi:10.1109/TMC.2011.59
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