PSOSVRPos: WiFi indoor positioning using SVR optimized by PSO

Wireless fidelity (WiFi) indoor positioning has attracted the attention of thousands of researchers. It faces many challenges, and the primary problem is the low positioning accuracy, which hinders its widespread applications. To improve the accuracy, we propose a WiFi indoor positioning algorithm based on support vector regression (SVR) optimized by particle swarm optimization (PSO), termed PSOSVRPos. SVR algorithm devotes itself to solving localization as a regression problem by building the mapping between signal features and spatial coordinates in high dimensional space. PSO algorithm concentrates on the global-optimal parameter estimation of the SVR model. The positioning experiment is conducted on an open dataset (1511 samples, 154 features). The PSOSVRPos algorithm could achieve positioning accuracy with a mean absolute error of 1.040 m, a root mean square error (RMSE) of 0.863 m and errors within 1 m of 59.8%. Experimental results indicate that the PSOSVRPos algorithm is a precise approach for WiFi indoor positioning as it reduces the RMSE (35%) and errors within 1 m (14%) compared with state-of-the-art algorithms such as convolutional neural network (CNN) based methods.