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• W2016448024 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." @default.
• W2016448024 created "2016-06-24" @default.
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• W2016448024 date "2012-04-01" @default.
• W2016448024 modified "2023-10-18" @default.
• W2016448024 title "Target Tracking in Wireless Sensor Networks Based on the Combination of KF and MLE Using Distance Measurements" @default.
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• W2016448024 doi "https://doi.org/10.1109/tmc.2011.59" @default.
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