Research and improvement on indoor localization based on RSSI fingerprint database and K-nearest neighbor points
Research and improvement on indoor localization based on RSSI fingerprint database and K-nearest neighbor points
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DOI：
10.1109/icccas.2013.6765288
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发表时间：
2013-11
期刊：
影响因子：
--
通讯作者：
Guowei Zhang;Xu Zhan;Liu Dan
中科院分区：
文献类型：
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作者：
Guowei Zhang;Xu Zhan;Liu Dan
针对K近邻算法的不足，提出了一种基于K近邻参考点集的室内定位算法。该算法将测试点与参考点之间的单一关系扩展为测试点与参考点之间以及测试点的近邻点与其他参考点之间的网络关系。新算法利用了图像的深层信息，有效地降低了噪声点的影响。新算法通过参考点的出现对坐标估计公式进行优化。实验表明，与K近邻定位算法相比，新算法在定位精度和稳定性上都有提高。
Aiming at the shortcomings of K nearest neighbor algorithm, this paper put forward an indoor location algorithm based on K nearest neighbor collection of reference points. The new algorithm in this paper expand the single relationship between test points and reference points to net relationship between test points and reference points and between test points' close neighbor points and other reference points. The new algorithm uses the deeper information, and effectively reduces the influence of noise points. The new algorithm optimizes the formula of coordinate estimation through the occurrences of reference point. Experiments show that compared with K nearest neighbor localization algorithm, the new algorithm has improved on the positioning accuracy and stability.