Advanced Search
Volume 41 Issue 12
Dec.  2019
Turn off MathJax
Article Contents
Shanchao YANG, Kangsheng TIAN, Changfei WU. Target Assignment Method for Phased Array Radar Network Based on Quality of Service[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2844-2851. doi: 10.11999/JEIT181133
Citation: Shanchao YANG, Kangsheng TIAN, Changfei WU. Target Assignment Method for Phased Array Radar Network Based on Quality of Service[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2844-2851. doi: 10.11999/JEIT181133

Target Assignment Method for Phased Array Radar Network Based on Quality of Service

doi: 10.11999/JEIT181133
Funds:  The National Natural Science Foundation of China (61601510)
  • Received Date: 2018-12-07
  • Rev Recd Date: 2019-06-18
  • Available Online: 2019-07-04
  • Publish Date: 2019-12-01
  • The constraint conditions of target assignment model for phased array radar network are unreasonable and the performance of model solving algorithms are not good enough. To solve these problems, a target assignment model for radar network based on Quality of Service (QoS) is constructed in this paper, and a model solving algorithm based on strong concave function approximation is proposed. Through the establishment of resource space and environment space in QoS model, radar resource constraints as well as the visibility constraints between radars and targets are described accurately. Then, sufficient conditions for the optimal solution of QoS model are derived by Karush-Kuhn-Tucker(KKT) condition, and a two-dimensional fast traversal method is used to approximate the strong concave function curve. Finally, the optimal assignment scheme is obtained by the stepwise iteration of operation setting points on the strong concave curve of each target. The simulation results show that the proposed model can effectively accomplish the target assignment of radar network, and model solving algorithm has better performance than the typical intelligent search algorithms.
  • loading
  • BIL R and HOLPP W. Modern phased array radar systems in Germany[C]. IEEE International Symposium on Phased Array Systems and Technology, Waltham, USA, 2016: 11–17.
    MALLICK M, KRISHNAMURTHY V, and VO B N. Integrated Tracking, Classification, and Sensor Management: Theory and Applications[M]. Hoboken, USA: John Wiley & Sons, Inc., 2014: 447–520.
    MOO P W and DING Zhen. Coordinated radar resource management for networked phased array radars[J]. IET Radar, Sonar & Navigation, 2015, 9(8): 1009–1020. doi: 10.1049/iet-rsn.2013.0368
    KALANDROS M. Covariance control for sensor management in cluttered tracking environments[J]. Journal of Guidance, Control, and Dynamics, 2004, 27(3): 493–496. doi: 10.2514/1.10339
    AUGHENBAUGH J M and LA COUR B R. Metric selection for information theoretic sensor management[C]. The 11th International Conference on Information Fusion, Cologne, Germany, 2008: 1–8.
    周林. 基于信息论的传感器管理算法研究[D]. [硕士论文], 河南大学, 2005: 34–42.

    ZHOU Lin. The algorithm of sensor management based on information theory[D]. [Master dissertation], Henan University, 2005: 34–42.
    WANG Xiaoying, HOSEINNEZHAD R, GOSTAR A K, et al. Multi-sensor control for multi-object bayes filters[J]. Signal Processing, 2018, 142: 260–270. doi: 10.1016/j.sigpro.2017.07.031
    SEOK J, ZHAO Jinxin, SELVAKUMAR J, et al. Radar resource management: Dynamic programming and dynamic finite state machines[C]. 2013 European Control Conference, Zurich, Switzerland, 2013: 4100–4105.
    DELIGIANNIS A and LAMBOTHARAN S. A Bayesian game theoretic framework for resource allocation in multistatic radar networks[C]. 2017 IEEE Radar Conference, Seattle, USA, 2017: 546–551. doi: 10.1109/RADAR.2017.7944263.
    方德亮, 冉晓旻, 李鸥. 一种能量有效的分布式传感器管理算法[J]. 西安电子科技大学学报: 自然科学版, 2017, 44(2): 171–177. doi: 10.3969/j.issn.1001-2400.2017.02.029

    FANG Deliang, RAN Xiaomin, and LI Ou. Energy efficient distributed sensor management algorithm[J]. Journal of Xidian University:, 2017, 44(2): 171–177. doi: 10.3969/j.issn.1001-2400.2017.02.029
    RUSU C, THOMPSON J, and ROBERTSON N M. Sensor scheduling with time, energy, and communication constraints[J]. IEEE Transactions on Signal Processing, 2018, 66(2): 528–539. doi: 10.1109/TSP.2017.2773429
    JAIN N K, NANGIA U, and JAIN J. A Review of particle swarm optimization[J]. Journal of the Institution of Engineers (India) : Series B, 2018, 99(4): 407–411. doi: 10.1007/s40031-018-0323-y
    郭广颂, 文振华, 郝国生. 基于群体决策的多用户协同交互式遗传算法[J]. 电子与信息学报, 2018, 40(9): 2165–2172. doi: 10.11999/JEIT171234

    GUO Guangsong, WEN Zhenhua, and HAO Guosheng. Interactive genetic algorithm based on collective decision making with multi-user collaboration[J]. Journal of Electronics &Information Technology, 2018, 40(9): 2165–2172. doi: 10.11999/JEIT171234
    NADJIASNGAR R and CHARLISH A. A performance model for target tracking with a radar network[C]. Proceedings of IEEE Radar Conference, Johannesburg, South Africa, 2015: 117–123. doi: 10.1109/RadarConf.2015.7411887.
    HANSEN J, RAJKUMAR R, LEHOCZKY J, et al. Resource management for radar tracking[C]. 2006 IEEE Conference on Radar, Verona, USA, 2006: 358–363. doi: 10.1109/RADAR.2006.1631788.
    BOYD S and VANDENBERGHE L. Convex Optimization[M]. London, England: Cambridge University Press, 2004: 99–103.
    FRANCU M, KERMAN R, and SINNAMON G. A new algorithm for approximating the least concave majorant[J]. Czechoslovak Mathematical Journal, 2017, 67(4): 1071–1093. doi: 10.21136/CMJ.2017.0408-16
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(3)

    Article Metrics

    Article views (2166) PDF downloads(73) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return