Research On Indoor Positioning Algorithm Based On The Fusion Of PDR And RSSI

In the current indoor positioning system,the signals received by the ranging positioning system based on the received signal strength Indication(RSSI) will have unpredictable random changes due to the uncertainty of the environment.Pedestrian dead reckoning(PDR) positioning system also has the problem of erroneously estimating sensor parameters and inconsistent movement of the left and right feet,resulting in cumulative errors.In view of the above-mentioned problems in the positioning system,the paper has done the following two research work:1.Aiming at the low reliability of the data of one foot in the PDR positioning system,the inconsistent movement of the left and right feet of the pedestrian,which leads to the large deviation of the pedestrian position estimation and the problem of accumulated step length,this paper proposes an optimization algorithm for step length and left and right foot fusion.First,the single parameter of the calculation step length is changed to a stable step length of multiple parameters,and then the asynchronous accuracy is improved by the method of fusion of the left and right foot coordinate data.Experiments show that this method reduces the cumulative positioning error of the step length and improves the positioning accuracy.2.Aiming at the cumulative error of incorrectly estimated sensor parameters in the PDR positioning system,this paper proposes a PDR correction algorithm and a positioning algorithm based on the fusion of improved PDR and RSSI.First,use the RSSI ranging and positioning results after the weighted K neighbor (WKNN) algorithm to correct the positioning information calculated by the PDR algorithm.Then according to the recursive characteristics of PDR,the parameters of the sensor are correctly estimated,and finally the two are fused through the extended Kalman filter (EKF).Experiments have proved that this method effectively reduces the cumulative positioning error,improves the positioning accuracy,and can obtain the best positioning result of the system.