基于AP加权的WKNN室内定位算法

Indoor Positioning Algorithm of WKNN Based on AP Weighting

科技的发展带动了日常生活质量的提升,给予了室内定位广阔的发展前景。由于定位过程中不同AP存在差异,造成室内定位精度差、波动大等问题,文章在原有指纹数据库匹配算法的基础上,提出一种基于AP加权的K近邻算法,利用方差加权AP结合欧氏距离进行相似度匹配。实验结果表明,该方法较经典室内定位算法,存在更高的定位精度及稳定性。

The development of science and technology promotes the improvement of the quality of daily life,which gives a broad development prospect for indoor positioning.Due to differences between different APs in the positioning process,problems such as poor indoor positioning accuracy and large fluctuations are caused.Based on the original fingerprint database matching algorithm,this paper proposes a K-nearest neighbor algorithm based on AP weighting,which uses variance weighted AP combined with Euclidean distance for similarity matching.Experimental results show that this method has higher positioning accuracy and stability than the classical indoor positioning algorithm.