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Aiming at the lack of indoor positioning accuracy caused by the complex indoor environment, a method of indoor navigation based on support vector regression (SVR) and particle filter is proposed. At the offline stage, the indoor received signal strength (RSS) is collected, and statistical learning methods are used to construct support vector regression model indoor mapping between received signal strength and physical location. At positioning stage, intelligent mobile equipment is used to capture acceleration, direction angle and environmental information perceived by WIFI module. Based on the particle filter, the motion data and the regression result are fused to calculate the motion trajectory of the mobile user. Experimental results show that the maximum location error of proposed method is 1.891 m, while the average error is 0.669 m, and indoor location and navigation accuracy can be effectively improved.