广东工业大学学报 ›› 2017, Vol. 34 ›› Issue (01): 60-64.doi: 10.12052/gdutxb.160012

• 综合研究 • 上一篇    下一篇

认知无线网络小型移动主用户的定位算法研究

吴用1, 万频1, 王永华1,2,3, 梁颋2, 卢强2   

  1. 1. 广东工业大学 自动化学院, 广东 广州 510006;
    2. 中国电子科技集团公司第七研究所, 广东 广州 510310;
    3. 中南民族大学 智能无线通信湖北省重点实验室, 湖北 武汉 430074
  • 收稿日期:2016-01-26 出版日期:2017-01-09 发布日期:2017-01-09
  • 作者简介:吴用(1991-),男,硕士研究生,主要研究方向为认知无线电.
  • 基金资助:

    国家自然科学基金资助项目（61471174）；中国博士后科学基金资助项目（2014M552529，2015T80920）；广东工业大学青年基金资助项目（13QNZD006）

Research on Location of Small-Scale Mobile Primary Users in Cognitive Radio Networks

Wu Yong1, Wan Pin1, Wang Yong-hua1,2,3, Liang Ting2, Lu Qiang2   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. China Electronics Technology Group Corporation No. 7 Research Institute, Guangzhou 510310, China;
    3. Hubei Key Laboratory of Intelligent Wireless Communications, South-Central University for Nationalities, Wuhan 430074, China
  • Received:2016-01-26 Online:2017-01-09 Published:2017-01-09

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摘要：

针对认知无线网络中，小型移动主用户可随意移动，难以准确定位的情况.本文提出一种基于加权质心算法与粒子滤波算法相结合的具有追踪调整机制的小型移动主用户定位算法.该算法的优势在于不仅可实时性地跟踪主用户，而且还能在跟踪出现偏差的情况启用调整机制，确保定位效果.仿真结果显示在100 m×100 m范围内，该算法的定位误差可以控制在10 m之内.

关键词: 小型移动主用户, 粒子滤波, 认知无线网络, 定位

Abstract:

In cognitive radio networks, it is very difficult to track the location of small-scale mobile primary users because of their mobile characteristic、the unpredictability of its spatial and temporal spectrum usage patterns and its weak transmission power. Based on these observations, a novel algorithm with self-adjustment mechanism is proposed based on weighted centroid localization and particle filter to track small-scale mobile primary users. The advantage of this method is that not only accurately and reliably track the location of small-scale mobile primary users, but also to preserve localization accuracy depend on adjustment mechanism in the case of localization error exceeds the threshold. Simulation results show that localization error of the proposed algorithm less than 10 m in the range of 100 m×100 m.

Key words: small-scale mobile primary users, particle filter, cognitive radio networks, location

中图分类号: 

  • TN925

[1] MIN A W, ZHANG X Y, SHIN K G. Detection of small-scale primary users in cognitive radio networks[J]. IEEE Journal on Selected Areas in Communications, 2011, 29(2):349-361.
[2] YUCEK T, ARSLAN H. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys and Tutorials, 2009, 11(1):116-130.
[3] MA Z, BEN L K, CHEN W, et al. A semi range-based iterative localization algorithm for cognitive radio networks[J]. IEEE Transactions on Vehicular Technology, 2009, 59(2):2214-2218.
[4] SHARMA P, CHANDRA K. Prediction of state transitions in rayleigh fading channels[J]. IEEE Transactions on Vehicular Technology, 2007, 56(2):416-425.
[5] VIANI F, LIZZI L, ROCCA P, et al. Object tracking through rssi measurements in wireless sensor networks[J]. Electronics Letters, 2008, 44(10):653-654.
[6] LIANG X, GREENSTEIN L J, MANDAYAM N B. Sensor-assisted localization in cellular systems[J]. IEEE Transactions on Wireless Communications, 2007, 6(12):4244-4248.
[7] WU Q, DING G, WANG J, et al. Spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks:two-dimensional sensing[J]. IEEE Transactions on Wireless Communications, 2013, 12(2):516-526.
[8] MIN A W, SHIN K G. Robust tracking of small-scale mobile primary user in cognitive radio networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2013, 24(4):778-788.
[9] WANG J, CHEN J, CABRIC D. Stansfield localization algorithm:theoretical analysis and distributed implementation[J]. Wireless Communications Letters IEEE, 2013, 2(3):327-330.
[10] WANG J, URRIZA P, HAN Y, et al. Weighted centroid localization algorithm:theoretical analysis and distributed implementation[J]. IEEE Transactions on Wireless Communications, 2011, 10(10):3403-3413.
[11] SCHIKORA M, GNING A, MIHAYLOVA L, et al. Box-particle probability hypothesis density filtering[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(3):1660-1672.
[12] YOO J, KIM W, KIM H J. Distributed estimation using online semi-supervised particle filter for mobile sensor networks[J]. Control Theory and Applications Iet, 2015, 9(3):418-427.
[13] 彭越, 吴多龙, Guillaume Andrieux, 等. 基于最小能量法的无线传感器网络节点能量有效性研究[J]. 广东工业大学学报, 2014, 31(1):86-89. PENG Y, WU D L, GUILLAUME A, et al. Research on energy-efficiency in wireless sensor networks based on minimum energy coding[J]. Journal of Guangdong University of Technology, 2014, 31(1):86-89.
[14] 李鹤, 刘广聪, 胡蝶. 无线传感器网络中基于能量比的簇首选择机制[J]. 广东工业大学学报, 2014, 31(3):83-87. LI H, LIU G C, HU D. Cluster-head selection mechanism based on energy ratio in wireless sensor networks[J]. Journal of Guangdong University of Technology, 2014, 31(3):83-87.
[15] CANDY J V. Bootstrap particle filtering[J]. IEEE Signal Processing Magazine, 2007, 24(4):73-85.
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