A Robust Indoor Localization Algorithm Based on Polynomial Fitting and Gaussian Mixed Model

Wireless sensor network (WSN) position-ing has a good effect on indoor positioning, so it has received extensive attention in the field of position-ing. Non-line-of sight (NLOS) is a primary challenge in indoor complex environment. In this paper, a ro-bust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error. Firstly, fitting polynomials are used to predict the measured values. The resid-uals of predicted and measured values are clustered by Gaussian mixture model (GMM). The LOS prob-ability and NLOS probability are calculated accord-ing to the clustering centers. The measured values are filtered by Kalman filter (KF), variable parame-ter unscented Kalman filter (VPUKF) and variable pa-rameter particle filter (VPPF) in turn. The distance value processed by KF and VPUKF and the distance value processed by KF, VPUKF and VPPF are com-bined according to probability. Finally, the maximum likelihood method is used to calculate the position co-ordinate estimation. Through simulation comparison, the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper. And it shows strong robustness in strong NLOS environ-ment.