基于主成分分析和卡方距离的信号强度差指纹定位算法

Signal strength difference fingerprint localization algorithm based on principal component analysis and chi-square distance

由于不同型号移动终端获取的接收信号强度(RSS)存在明显差异,传统的基于RSS位置指纹库的室内定位算法定位稳定性和精度不高,而现有的采用信号强度差(SSD)替代RSS构建位置指纹库的解决方案存在高数据维度、相关性冗余过高和K-近邻(KNN)算法本身定位精度不高的问题。针对上述问题,提出了一种基于主成分分析(PCA)和卡方距离(CSD)的SSD指纹定位算法,使用PCA算法进行SSD数据降维和相关性冗余消除,并使用CSD度量降维后特征量间的相对距离进行位置匹配。仿真实验中,使用所提算法的SSD位置指纹库定位误差累积概率曲线高于原有RSS和SSD指纹库;相比传统的KNN算法和基于余弦相似度改进的KNN算法(COS-KNN),所提算法的平均定位误差、定位误差方差均有明显减小,时间开销稍有增加。实验结果表明,所提算法可以有效提升原有SSD指纹定位方法的定位稳定性和定位精度,能够满足室内定位的实时性需要。

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.