Abstract: Aiming at the problems of large positioning fluctuation caused by excessive matching redundant information in indoor positioning fingerprint database and poor positioning timeliness caused by excessive sample number in database, This paper proposes a new indoor positioning method based on support vector machine (SVM) optimized by firefly algorithm (FA). Singular Spectrum Analysis (SSA) is used to remove noise during data preprocessing and the SVM parameters are optimized by FA, so as to establish the indoor positioning regression model. The experimental results show that, compared with the current indoor positioning method, FA-SVM algorithm has fast convergence speed and improves the indoor positioning accuracy and stability.

Key words: indoor positioning, denoising, firefly algorithm, support vector machine, positioning accuracy