【摘要】 提出了一种信息驱动的节点选择机制,应用于无线传感器网络中的目标估值.其以传感器节点的测量值与目标状态的估计分布之间的互信息作为信息效用函数,度量节点的测量值对目标状态估计的信息贡献,选择信息贡献值大的节点参与卡尔曼滤波过程进行迭代;应用基于地理位置信息的路由算法顺序访问选中的节点,并建立与Sink节点之间的路由,路径上的节点依次进行卡尔曼迭代以修正估计的状态值.仿真结果表明,该机制涉及的节点数目较少,总的通信距离较短,但目标估值的性能很好.更多还原

【Abstract】 An information-driven sensor selection algorithm is proposed to select sensors to participate in Kalman filtering for target state estimation in sensor networks.The mutual information between the measurements of sensors and the estimated distribution of the target state is considered as the information utility function to evaluate the information contributions of sensors.Only those sensors with larger mutual information are selected to participate in Kalman filtering iterations.Then the geographic rou-(ting) mechanism is utilized to visit these selected sensors sequentially and to set up a path to transport the state estimation information to the sink node.Simulation results show that the information-driven sensor selection algorithm has excellent estimation performance.更多还原