%0 Journal Article %A XIA Pengcheng %A ZHOU Fei %T Signal strength difference fingerprint localization algorithm based on principal component analysis and chi-square distance %D 2019 %R 10.11772/j.issn.1001-9081.2018102143 %J Journal of Computer Applications %P 1405-1410 %V 39 %N 5 %X Due to the significant difference in Received Signal Strength (RSS) acquired by different types of mobile terminals, the traditional indoor localization algorithm based on RSS location fingerprint database has low localization stability and accuracy, existing solutions using Signal Strength Difference (SSD) instead of RSS to construct location fingerprint database has problems such as high data dimension, and high correlation redundancy, and K-Nearest Neighbors (KNN) algorithm has low positioning accuracy. Aiming at the above problems, an SSD fingerprint localization algorithm based on Principal Component Analysis (PCA) and Chi-Square Distance (CSD) was proposed. PCA algorithm was used to reduce the dimension of SSD data and eliminate correlation redundancy, and CSD was used to measure the relative distance between the feature quantities after dimension reduction to match the position. In the simulation experiments, the positioning error cumulative probability curve of the SSD location fingerprint database using the proposed algorithm is higher than that of the original RSS and SSD fingerprint database. Compared with the traditional KNN and the improved KNN algorithm based on Cosine Similarity (COS-KNN), the average positioning error and the positioning error variance of the proposed algorithm are both significantly reduced while time cost is slightly increased. The experimental results show that the proposed algorithm can further improve the positioning stability and positioning accuracy of the original SSD fingerprint localization algorithm effectively, and meets the real-time needs of indoor localization. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2018102143