Target Tracking in Wireless Sensor Networks Based on the Combination of KF and MLE Using Distance Measurements
Target Tracking in Wireless Sensor Networks Based on the Combination of KF and MLE Using Distance Measurements
复制标题
DOI：
10.1109/tmc.2011.59
复制
发表时间：
2012-04
影响因子：
7.9
通讯作者：
Xingbo Wang;M. Fu;Huanshui Zhang
中科院分区：
文献类型：
--
作者：
Xingbo Wang;M. Fu;Huanshui Zhang
无线传感器网络中目标跟踪的一个常见技术难点是，单个同构传感器只测量它们到目标的距离，而目标的状态由其在笛卡尔坐标系中的位置和速度组成。也就是说，传感器测量在目标状态下是非线性的。扩展卡尔曼滤波是一种常用的处理非线性的方法，但这往往会导致不满意甚至不稳定的跟踪性能。在本文中，我们提出了一种新的目标跟踪方法，避免了不稳定的问题，并提供了上级跟踪性能。首先，我们提出了一种改进的噪声模型，它结合了加性噪声和乘性噪声的距离感知。然后，我们使用一个最大似然估计的预定位，以消除传感器的非线性应用标准的卡尔曼滤波器之前。通过实验和仿真结果证明了所提出的方法的优点。
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.