| 引用本文: | 徐学永,黄河,黄刘生,徐宏力,张银东,刘刚.基于RSSI全向拟合经验图的节点自定位算法.软件学报,2011,22(zk1):73-82 |
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| 基于RSSI全向拟合经验图的节点自定位算法 |
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徐学永1,2,3, 黄河1,2, 黄刘生1,2, 徐宏力1,2, 张银东1,2, 刘刚1,2
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1.中国科学技术大学 计算机学院, 安徽 合肥 230027;2.中国科学技术大学 苏州研究院, 江苏 苏州 215123;3.北方自动控制技术研究所, 山西 太原 030006
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| 摘要: |
| 在无线传感器网络中,由于RSSI(radio signal strength indicator)信息不需要额外的代价就可以在信息的发 送接收过程中获得,所以RSSI被公认是一种非常具有吸引力的定位信息并被研究者大量应用于其定位系统设计中.已有的基于RSSI的定位研究分为基于经验值拟合和基于经验值匹配两类.但这两类方法要么对环境敏感要么扩展性很差,在实际应用中都存在很多限制.其原因在于,RSSI值在实际系统中由于多径反射、天线朝向等因素的影响而具有各向异性的特性,从而导致RSSI值与距离之间的对应关系并不明显.提出了一种基于RSSI拟合经验图的 定位算法.在该方法中,预先采集节点有限个不同方向及这些方向上的有限个不同距离对应的RSSI值.然后,在这些 有限个方向和距离的基础上采用拟合的方法得到一张包含任一方向与距离对应的RSSI值的全向经验图.待定位节点根据当前接收到的来自不同锚节点的RSSI值和该经验图可以求得一个最合适的位置坐标.该方法兼具经验值拟合和经验值匹配两种方法的优点,充分考虑到节点信号传输模型各向异性的影响,同时又具备较好的扩展性,具有很 强的实用价值.最后,通过搭建一个包含14个MICAz节点的实验床验证了该算法的实际应用效果.系统验证结果表明:与基于RSSI的W-Centroid算法相比,该方法在室内环境下的平均定位精度要高出26%左右,在室外环境下要高出大约42%. |
| 关键词: RSSI 定位 无线传感器网络 |
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| 基金项目:国家自然科学基金(60903055);国家重点基础研究发展计划(973)(2011CB302-905);国家科技重大专项(2011ZX03005-002);中央高校基本科研业务资助 |
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| Omni-Fitting RSSI Map Based Self-Localization Algorithm |
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XU Xue-Yong1,2,3, HUANG He1,2, HUANG Liu-Sheng1,2, XU Hong-Li1,2, ZHANG Yin-Dong1,2, LIU Gang1,2
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1.School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;2.Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou 215123, China;3.North Automatic Control Technology Insti
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| Abstract: |
| In wireless sensor networks,RSSI is considered as an appealing modality for localization in WSN as RSSI information can be obtained at almost no additional cost.To effectively utilize RSSI for localization,two directions have been investigated:RSSI fitting and RSSI profiling.Many state-of-art localization algorithms—falling in these two categories,however,work poorly in real environments because of imprecise mapping relationship between RSSI and the physical distance due to the impact from multi-path,environment noisy,et al. This paper proposes an Omni-Fitting RSSI Map Based Self-Localization Algorithm(ORM).In ORM,the study samples limited RSSI values in several different directions and distances in advance.Based on this information,the study can endue the global radio strength distribution map of the positioning area.According to this map,the unknown nodes can conduct localization to acquire their coordinates.ORM considers the anisotropic characteristics of radio transmission model carefully which makes it own the advantages of both good scalability and acceptable precision.In order to demonstrate the performance of the approach,the study builds up a testbed with 14 MICAz motes to run ORM.The results show that this method can outperform W-Centroid algorithm by about 26% in indoor environment and as much as 42% under outdoor circumstance. |
| Key words: RSSI localization wireless sensor networks |
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