文献类型：期刊文献

英文题名：Adaptive Consensus-Based Distributed Target Tracking With Dynamic Cluster in Sensor Networks

作者：Zhang, Hao[1];Zhou, Xue[1];Wang, Zhuping[1];Yan, Huaicheng[2,3];Sun, Jian[4]

机构：[1]Tongji Univ, Dept Control Sci & Engn, Shanghai 200092, Peoples R China;[2]Hubei Normal Univ, Coll Mechatron & Control Engn, Huangshi 435002, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[4]Tongji Univ, Dept Transportat Engn, Shanghai 200092, Peoples R China

年份：2019

卷号：49

期号：5

起止页码：1580

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：;EI(收录号：20181805116190);WOS:【SCI-EXPANDED(收录号:WOS:000460667400003)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant u1764261 and Grant 61773289, in part by the Projects of Shanghai International Cooperation under Grant 18510711100, and in part by the Fundamental Research Funds for the Central Universities. This paper was recommended by Associate Editor Y. Shi. (Corresponding author: Zhuping Wang.)

语种：英文

外文关键词：Adaptive filtering; data fusion; distributed Kalman filtering; dynamic cluster selection; sensor networks

摘要：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.