基于谱回归核判别分析的候机楼室内快速定位算法

针对机场候机楼客流量大、室内环境复杂多变的特点,提出了一种基于谱回归核判别分析(SRKDA)的室内定位算法。在离线阶段,采集已知位置的接收信号强度(RSS)数据,使用SRKDA算法提取原始位置指纹(OLF)的非线性特征生成新的特征指纹库;在线阶段,先使用SRKDA对待定位点的RSS数据进行处理,进而使用加权K最近邻(WKNN)算法进行位置估计。定位仿真实验中,在两个不同的定位场景中,所提算法在1. 5 m定位精度下的误差累积分布函数(CDF)和定位准确率分别达到91. 2%和88. 25%,相对于核主成分分析法(KPCA)+WKNN模型分别提高了16. 7个百分点和18. 64个百分点,相对于KDA+WKNN模型分别提高了3. 5个百分点和9. 07个百分点;在大量离线样本(大于1 100条)的情况下,该算法数据处理时间远小于KPCA和KDA。实验结果表明,所提算法能够提高室内定位精度,同时节省了数据处理时间,提高了定位效率。

Fast indoor positioning algorithm of airport terminal based on spectral regression kernel discriminant analysis

Aiming at the characteristics of large passenger flow, complex and variable indoor environment in airport terminals, an indoor positioning algorithm based on Spectral Regression Kernel Discriminant Analysis (SRKDA) was proposed. In the offline phase, the Received Signal Strength (RSS) data of known location was collected, and the non-linear features of the Original Location Fingerprint (OLF) were extracted by SRKDA algorithm to generate a new feature fingerprint database. In the online phase, SRKDA was firstly used to process the RSS data of the point to be positioned, and then Weighted K-Nearest Neighbor (WKNN) algorithm was used to estimate the position. In positioning simulation experiments, the Cumulative Distribution Function (CDF) and positioning accuracies of the proposed algorithm under 1.5 m positioning accuracy are 91.2% and 88.25% respectively in two different localization scenarios, which are 16.7 percentage points and 18.64 percentage points higher than those of the Kernel Principal Component Analysis (KPCA)+WKNN model, 3.5 percentage points and and 9.07 percentage points higher than those of the KDA+WKNN model. In the case of a large number of offline samples (more than 1100), the data processing time of the proposed algorithm is much shorter than that of KPCA and KDA. The experimental results show that, the proposed algorithm can effectively improve the indoor positioning accuracy, save data processing time and enhance the positioning efficiency.