IEEE Trans Cybern. 2019 May;49(5):1580-1591. doi: 10.1109/TCYB.2018.2805717. Epub 2018 Apr 24.
This paper is concerned with the target tracking problem over a filtering network with dynamic cluster and data fusion. A novel distributed consensus-based adaptive Kalman estimation is developed to track a linear moving target. Both optimal filtering gain and average disagreement of the estimates are considered in the filter design. In order to estimate the states of the target more precisely, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. An adaptive consensus factor is employed to adjust the optimal gain as well as to acquire a better filtering performance. In the filter's information exchange, dynamic cluster selection and two-stage hierarchical fusion structure are employed to get more accurate estimation. At the first stage, every sensor collects information from its neighbors and runs the Kalman estimation algorithm to obtain a local estimate of system states. At the second stage, each local sensor sends its estimate to the cluster head to get a fused estimation. Finally, an illustrative example is presented to validate the effectiveness of the proposed scheme.
本文研究了具有动态簇和数据融合的滤波网络中的目标跟踪问题。针对线性运动目标跟踪，提出了一种基于分布式一致性的自适应卡尔曼估计方法。在滤波器设计中，同时考虑了最优滤波增益和估计的平均分歧。为了更精确地估计目标的状态，通过最小化均方估计误差获得最优卡尔曼增益。自适应一致因子用于调整最优增益并获得更好的滤波性能。在滤波器的信息交换中，采用动态簇选择和两级分层融合结构来获得更准确的估计。在第一阶段，每个传感器从其邻居收集信息，并运行卡尔曼估计算法以获得系统状态的局部估计。在第二阶段，每个本地传感器将其估计发送到簇头以获得融合估计。最后，通过一个实例验证了所提出方案的有效性。
