Indoor fingerprint positioning method based  on RSSI modified by GF-KF

Aiming at the problems that Wi-Fi signals are susceptible to external uncertainties such as noise, and the RSSI received by mobile terminals deviates from the true value, which results in low positioning accuracy, this paper proposes an indoor fingerprint positioning method based on RSSI modified by GF-KF. Because the collected RSSI is unstable, this method uses the characteristics of the RSSI like Gaussian distribution to perform a Gaussian fit on the RSSI data to obtain a relatively determined RSSI value. Based on this, a Kalman filter algorithm is introduced to correct the RSSI data after fitting, and the WKNN matching algorithm is used to locate. The experimental results show that the average positioning error of the method in this paper is 1.50 m, and the cumulative distribution probability of positioning errors within 2.0 m is 90.06％, and the positioning effect is better than similar methods.