Environmental-adaptive RSSI-based localization in intelligent space

被引：0
|
作者：
Song, Baoye [1 ,2 ]
Tian, Guohui [1 ]
Zhou, Fengyu [1 ]
机构：
[1] School of Control Science and Engineering, Shandong University, Jinan 250061, China
[2] College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, China
来源：
Gaojishu Tongxin/Chinese High Technology Letters | 2012年 / 22卷 / 10期
关键词：
Sensor nodes - Kalman filters;
D O I：
10.3772/j.issn.1002-0470.2012.10.013
中图分类号：
学科分类号：
摘要：
To overcome the influence of dynamic environmental parameters on the accuracy of the object localization in intelligent space using the received signal strength indication (RSSI) method, an environmental-adaptive RSSI-based localization algorithm is proposed, and the reason why changes of environmental parameters are the main factors influencing the distance measurements and localization error is expounded. When using the proposed algorithm, firstly the measured data are preprocessed through singular value removing and average filtering, and then the adaptive RSSI distance estimating algorithm is utilized to estimate the distance between nodes. The location of unknown node is calculated using the maximum likelihood estimation. Finally, the Kalman filter is exploited to estimate the trajectory of moving targets. The experimental results show that the localization error of the proposed algorithm is less than one meter, meeting the requirements of moving-targets localization in intelligent space.
引用
收藏
页码：1083 / 1089
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