CAJ | 학술논문

基于WLAN的室内定位技术对高性能算法的需求与日俱增，K近邻算法和最大似然概率算法是基于Wi-Fi位置指纹室内定位技术的两种常用算法。给出感知概率的定义，在感知概率基础上定义了感知欧氏距离和感知似然概率，并提出基于感知欧氏距离的确定型修正算法和基于感知似然概率的概率型修正算法。实验结果表明：基于感知概率的修正算法较原算法在相对复杂的室内环境中有更高的定位精度。
기우WLAN적실내정위기술대고성능산법적수구여일구증，K근린산법화최대사연개솔산법시기우Wi-Fi위치지문실내정위기술적량충상용산법。급출감지개솔적정의，재감지개솔기출상정의료감지구씨거리화감지사연개솔，병제출기우감지구씨거리적학정형수정산법화기우감지사연개솔적개솔형수정산법。실험결과표명：기우감지개솔적수정산법교원산법재상대복잡적실내배경중유경고적정위정도。
With the development of the WLAN based indoor positioning techniques ,the demand for algorithms of the WLAN based indoor positioning techniques with good perform-ance is increasing .The KNN and ML algorithms are two common algorithms for fingerprint-ing indoor positioning .This paper gave at first the definition of Sensing Probability and then defined the Sensing Euclidian Distance and the Sensing Likelihood .Some deterministic and probabilistic algorithms were proposed .The experimental results demonstrate the modified algorithms based on Sensing Probability have a higher degree of accuracy than the original al-gorithms in complex indoor environment .