北京航空航天大学学报 ›› 2008, Vol. 34 ›› Issue (03): 257-261.

• 论文 • 上一篇    下一篇

无线传感器网中的加权距离节点选择法

吴海1, 赵巍1, 田斌2   

  1. 1. 北京航空航天大学 电子信息工程学院, 北京 100083;
    2. 第二炮兵装备研究院, 北京 100085
  • 收稿日期:2007-06-29 出版日期:2008-03-31 发布日期:2010-09-17
  • 作者简介:吴 海(1983- ),男,安徽池州人,硕士生,wuhai@ee.buaa.edu.cn.
  • 基金资助:

    国家自然科学基金资助项目(60502019)

Sensor selection algorithm based on weighted distance in wireless sensor networks tracking

Wu Hai1, Zhao Wei1, Tian Bin2   

  1. 1. School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. The Armament Academy of the Second Artillery, Beijing 100085, Chin
  • Received:2007-06-29 Online:2008-03-31 Published:2010-09-17

摘要: 节点选择存在于无线传感器网的目标跟踪问题中,主要任务是从多个传感器中选取合适节点跟踪当前目标,满足跟踪精度、算法计算量的要求.提出一种适用于测角传感器节点的加权距离选择法,该算法利用目标状态预测的分布及节点的探测模型,通过计算节点距目标几何距离及加权系数,选择具有最小加权距离的传感器点进行探测,避开了贝叶斯滤波,在减少计算量的同时具有很好的选择精度.仿真结果表明,本算法大幅度减少了计算量,同时可达到和熵值法相当的跟踪定位效果.

Abstract: A new sensor selection methods for bearings-only sensors, weighted-distance node selection (WNS) was proposed. Based on probability distribution function (PDF) of target estimates and sensing-model, the sensors with the minimum weighted-distance were activated in the next snapshot. Since the existing mahalanobis distance measure, though yields a good precision with low computational complexity, fits only range sensors and the algorithms based on the PDF, such as entropy method, are computationally burdensome, WNS avoids the Bayes filtering process and achieves alluring precision. Simulation results show that the computation complexity is indeed reduced while a comparable selection precision is achieved as entropy method.

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