Indoor Localization Algorithm of Terminal Based on RSS Feature Extension and Spectral Regression Kernel Discriminant Analysis

Aiming at the characteristics of large passenger flow and the complex and variable indoor environment in the airport terminal, an indoor localization algorithm based on Received Signal Strength feature extension and Spectral Regression Kernel Discriminant Analysis is proposed. In the offline phase, the Least Square-Support Vector Machine regression model is used to estimate the distance between the terminal and the Access Point, and the Received Signal Strength features are extended based on this. The Spectral Regression framework is introduced on the basis of Kernel Discriminant Analysis. The non-linear features of the Original Location Fingerprint were extracted by this algorithm to generate a new feature fingerprint dataset. During the online stage, Spectral Regression Kernel Discriminant Analysis was firstly used to process the extended Spectral Regression Kernel feature of the point to be positioned, and then use the weighted K nearest neighbor algorithm for position estimation. Experimental results show that the algorithm in this paper can effectively reduce the average error and improve the indoor localization accuracy in the complex terminal environment with large passenger flow and non-line-of-sight environment.