Non-myopic scheduling algorithm for multi-sensor collaborative detection and tracking

被引：0
|
作者：
Qiao C.-L. [1 ]
Shan G.-L. [1 ]
Wang Y.-C. [2 ]
Liu H. [3 ]
机构：
[1] Department of Electronic and Optical Engineering, Shijiazhuang Campus of Army Engineering University, Shijiazhuang
[2] College of Joint Service, National Defense University, Beijing
[3] Unit 63870 of PLA, Huayin
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 04期
关键词：
Branch and bound; Collaborative detection and tracking; PCRLB; POMDP; Sensor scheduling;
D O I：
10.13195/j.kzyjc.2018.0835
中图分类号：
学科分类号：
摘要：
In consideration of the radiation control for target detection and tracking, a non-myopic scheduling algorithm for multi-sensor collaborative dectection and tracking is proposed. Firstly, the model of target tracking and radiation control is formulated as a partially observable Markov decision process (POMDP). Then, the probability of detecting new targets is calculated by the randomly distributed particles, the non-myopic tracking accuracy is predicted by the posterior carmér-rao lower bound (PCRLB), and the non-myopic radiation cost is derived by the hidden Markov model (HMM) filter. Finally, the non-myopic optimization function of radiation control is set up with the constraints of the new target detection probability and the existing target tracking accuracy. And the optimal scheduling sequence is obtained by the branch and bound algorithm based on greedy search. Simulation results show the effectiveness of the proposed algorithm. © 2020, Editorial Office of Control and Decision. All right reserved.
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页码：799 / 806
页数：7
相关论文
共 17 条
  • [1] Wu W.H., Jiang J., Gao L., Tracking maneuvering target in clutter with passive sensor aided by airborne radar, Control and Decision, 30, 2, pp. 277-282, (2015)
  • [2] Dai C.L., Shi C.G., Zhou J.J., Adaptive radiation control algorithm with passive sensor cooperation in airborne radar system, Journal of Data Acquisition and Processing, 31, 4, pp. 746-753, (2016)
  • [3] Wu W., Wang G.H., Shuang W., Multi-airborne-platform multi-target tracking and radiation control technology, Systems Engineering and Electronics, 34, 3, pp. 495-501, (2012)
  • [4] Zhang Z., Shan G., UTS-based foresight optimization of sensor scheduling for low interception risk tracking, International Journal of Adaptive Control and Signal Processing, 28, 10, pp. 921-931, (2014)
  • [5] Zhang Z., Shan G., Non-myopic sensor scheduling to track multiple reactive targets, IET Signal Processing, 9, 1, pp. 37-47, (2015)
  • [6] Qiao C.L., Shan G.L., Duan X.S., Scheduling algorithm of active sensors for tracking task requirement, Systems Engineering and Electronics, 39, 11, pp. 2515-2521, (2017)
  • [7] Krishnamurthy V., Emission management for low probability intercept sensors in network centric warfare, IEEE Transactions on Aerospace and Electronic Systems, 41, 1, pp. 133-151, (2005)
  • [8] Tharmarasa R., Kirubarajan T., Hernandez M., PCRLB-based multisensory array management for multitarget tracking, IEEE Transactions on Aerospace and Electronic Systems, 43, 2, pp. 539-555, (2007)
  • [9] Tharmarasa R., Kirubarajan T., Sinha A., Decentralized sensor selection for large-scale multisensory-multitarget tracking, IEEE Transactions on Aerospace & Electronic Systems, 47, 2, pp. 1307-1324, (2011)
  • [10] Liu Q., Liu Z., Xie R., Collaborative detection and tracking of stealthy target by netted radar, Journal of Electronics & Information Technology, 35, 3, pp. 601-607, (2013)
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