Indoor location is a key technology in the application of pervasive computing

and aims at solving the problem of least square support vector machine parameters. An indoor localization algorithm based on the least square support vector machine is proposed. The principal component analysis is used to extract important features

and then

least square support vector machine is established for indoor localization model using particle swarm optimization algorithm. The simulation experiment is used to test location performance. The results show that the proposed algorithm improves the accuracy of indoor location

and the location time is less than other indoor location algorithm.