摘要
为降低RSSI指纹数据库中指纹数据量和AP数量对KNN算法的运算效率的影响,提出一种基于MD5-KNN的Wi-Fi室内定位算法,对大型场所构建的RSSI指纹数据库进行优化。在离线阶段,将RSSI指纹数据库中的每条指纹转换成包含32位16进制表示的MD5序列。在线上阶段,该算法完成定位所需时间与AP数量无关,且不随指纹数量的增加而线性增加,降低了定位所需时间和运算量。同时,该算法自适应地匹配出合适的K值,有效解决了RSSI-KNN算法需手动设定K值的问题。实验结果表明,该算法有效提高了基于Wi-Fi的室内定位技术的定位精度以及定位效率。
In order to reduce the influence of fingerprint data amount and AP number in the RSSI fingerprint database on the computational efficiency of the KNN algorithm,this paper proposed a Wi-Fi indoor positioning algorithm based on MD5-KNN,which optimized the fingerprint database of large place. In the offline phase,the algorithm converted each fingerprint into a MD5 sequence that included a 32 bit hexadecimal representation. In the online phase,the time it took for the algorithm to locate was linear with the number of APs and did not increase linearly with the number of fingerprints,which reduced the time and calculation amount required for positioning effectively. At the same time,this algorithm calculated the appropriate K adaptively,so as to effectively solve the problem that the RSSI-KNN algorithm needs to manually set the value of K. The experimental results show that the proposed algorithm can effectively improve the positioning accuracy and location efficiency of the indoor positioning technology based on Wi-Fi.
作者
苗云龙
陆彦辉
尹峰
杨守义
Miao Yunlong;Lu Yanhui;Yin Feng;Yang Shouyi(School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China;Shenzhen Institute of Big Data,The Chinese University of Hong Kong,Shenzhen Guangdong 518172,China)
出处
《计算机应用研究》
CSCD
北大核心
2019年第9期2746-2749,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61571401,61701426)
河南省技术创新引导专项资助项目(182106000027)
