| 基于采样滤波的信号矢量分解移动定位算法 |
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| 引用本文： | 张双,李晶,陈嘉兴,刘志华.基于采样滤波的信号矢量分解移动定位算法[J].软件学报,2014,25(S1):66-74. |
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| 作者姓名： | 张双 李晶 陈嘉兴 刘志华 |
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| 作者单位： | 河北师范大学 信息技术学院, 河北 石家庄 050024;河北师范大学 数学与信息科学学院, 河北 石家庄 050024;河北师范大学 数学与信息科学学院, 河北 石家庄 050024;河北师范大学 信息技术学院, 河北 石家庄 050024 |
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| 基金项目： | 国家自然科学基金(61271125);河北省自然科学基金(F2013205084);河北省教育厅基金项目(Q2012124) |
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| 摘 要： | 以接收信号强度(received signal strength,简称RSS)的测距技术为基础,借助移动传感器网络(MSN)中MCL类粒子滤波定位算法的采样、过滤方法,并融入物理中力的分解和合成的思想,提出了一种信号矢量分解的采样滤波移动节点定位算法.该算法通过建立直角坐标系,分解合成移动节点、样本点与信标节点间的信号矢量,利用误差圆环采样,比较移动节点与样本点的信号合矢量进行滤波,将合矢量模差绝对值最小的样本点坐标的均值作为移动节点的坐标.仿真结果表明,在同样的实验条件下,该算法的定位精度明显高于相比较的其他算法,且该算法不需要添加额外的硬件设备.
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| 关 键 词： | 移动传感器网络 定位算法 采样滤波 信号合矢量 |
| 收稿时间： | 2014/5/10 0:00:00 |
| 修稿时间： | 2014/8/26 0:00:00 |
Signal Strength Resolution for Dynamic Localization Based on Sampling and Filtering |
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| ZHANG Shuang,LI Jing,CHEN Jia-Xing and LIU Zhi-Hua.Signal Strength Resolution for Dynamic Localization Based on Sampling and Filtering[J].Journal of Software,2014,25(S1):66-74. |
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| Authors: | ZHANG Shuang LI Jing CHEN Jia-Xing and LIU Zhi-Hua |
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| Affiliation: | College of Information Technology, Hebei Normal University, Shijiazhuang 050024, China;Mathematics and Information Science College, Hebei Normal University, Shijiazhuang 050024, China;Mathematics and Information Science College, Hebei Normal University, Shijiazhuang 050024, China;College of Information Technology, Hebei Normal University, Shijiazhuang 050024, China |
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| Abstract: | Based on the received signal strength (RSS) range measurement technology, and by utilizing the sampling and filtering approach of MCL-kind particle filtering localization algorithms in mobile sensor networks (MSN), a kind of signal strength resolution for dynamic localization based on sampling and filtering (SSR-SF) which integrates with the principle of strength resolution and composition in physics is proposed. In the produced rectangular coordinates, SSR-SF resolves the resultants the signal vectors between mobile node, beacon nodes, and samples and beacon nodes respectively. It samples from an error annulus, compares the signal resultant vectors of the samples with that of the mobile node, and then picks out the final samples whose resultant vectors' mood are closest to that of the mobile node. SSR-SF takes the average value of those final samples' coordinates as the mobile node's location. Simulation results show that, under the same experiment conditions, the localization accuracy of SSR-SF is clearly higher than its counterparts and it needs no additional hardware. |
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| Keywords: | mobile sensor network (MSN) localization sample and filter signal resultant vector |
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