Multi-sensor distributed control strategy for multi-target tracking

被引：0
|
作者：
Chen H. [1 ]
Deng D.-M. [1 ]
Han C.-Z. [2 ]
机构：
[1] School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu
[2] Institute of Integrated Automation, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Distributed fusion; Information gain; Multi-sensor control; Multi-target tracking; Tactical significance assessment;
D O I：
10.7641/CTA.2019.80715
中图分类号：
学科分类号：
摘要：
Distributed sensor network technology plays an extremely important role in complex multi-target tracking system. Aiming at distributed sensor control problem in multi-sensor multi-target tracking, this paper proposes some multi-sensor control strategies information-based. First, a multi-sensor multi-Bernoulli filter is presented by using random finite set (RFS), and a multi-sensor multi-Bernoulli density is approximated by a set of parameterized multi-Bernoulli process. Further, through the sequential Monte Carlo implementation of the multi-Bernoulli filter, the sampling scheme is designed to sample the multi-Bernoulli density, and then the multi-target state space distribution is approximated by a set of weighted particles. Subsequently, the Bhattacharyya distance, as the reward function, is used for the decision making of independent and parallel multi-sensor control. As another important part, this paper proposes a multi-sensor control strategy based on multi-target tactical significance assessment, where the goal is to evaluate multi-target tactical significance and then track preferentially the maximum threat target. Finally, the simulations verify the effectiveness of the proposed algorithms. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：1585 / 1598
页数：13
相关论文
共 35 条
  • [1] Yin X.Y., Li Z.J., Zhang L.X., Et al., Distributed state estimation of sensor-network systems subject to Markovian channel switching with application to a chemical process, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48, 6, pp. 864-874, (2018)
  • [2] Yang R.N., Liu G.P., Shi P., Et al., Predictive output feedback control for networked control systems, IEEE Transactions on Industrial Electronics, 61, 1, pp. 512-520, (2014)
  • [3] Yang R.N., Shi P., Liu G.P., Filtering for discrete-time networked nonlinear systems with mixed random delays and packet dropouts, IEEE Transactions on Automatic Control, 56, 11, pp. 2655-2660, (2011)
  • [4] Leu J.S., Chiang T.H., Yu M.C., Et al., Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes, IEEE Communications Letters, 19, 2, pp. 259-262, (2015)
  • [5] Liu G.P., Predictive controller design of networked systems with communication delays and data loss, IEEE Transactions on Circuits and Systems-II Express Briefs, 57, 6, pp. 481-485, (2010)
  • [6] Krishnamurthy V., Algorithms for optimal scheduling and management of hidden Markov model sensors, IEEE Transactions on Signal Processing, 50, 6, pp. 1382-1397, (2002)
  • [7] Mahler R.P.S., Statistical Multi-source Multi-target Information Fusion, (2007)
  • [8] Mahler R.P.S., Advances in Statistical Multi-source Multi-target Information Fusion, (2014)
  • [9] Mahler R.P.S., Multitarget Bayes filtering via first-order multitarget moments, IEEE Transactions on Aerospace and Electronic Systems, 39, 4, pp. 1152-1178, (2003)
  • [10] Mahler R.P.S., PHD filters of higher order in target number, IEEE Transactions on Aerospace and Electronic Systems, 43, 4, pp. 1523-1543, (2007)
1234