Optimization Of Extended Kalman Filtering Algorithm In Indoor Mobile Node Location

Wireless sensor networks(WSN)technology has been already applied widely,but when used in indoor positioning,the traditional WSN technology tends to be affected by multiple path interference.In order to solve this problem and optimize the indoor positioning effect,this paper analyzes the advantages and disadvantages of the indoor environment electromagnetic wave propagation model and the inertial measurement unit(IMU)in the positioning process through the in-depth study of the status quo of the Chinese and foreign indoor positioning technology.And the statistical characteristics of position information based on the received signal strength indicator(RSSI)are designed.A method of allocating extended Kalman filter(EKF)based on statistical characteristic is designed,especially the measurement noise covariance matrix of EKF is arranged.This paper proposes an optimized adaptive Kalman filter localization algorithm(LO-EKF)for indoor environment.In this algorithm,firstly,the velocity modulus and direction of the moving target node are measured by the inertial measurement unit,the position information of the moving target node is estimated by Least squares algorithm(LS),and the target state model is established by fusing the two measurements.Then,the electromagnetic noise channel model of the indoor wireless environment is analyzed,that is,the statistical characteristic of the RSSI value of the position measurement and the measurement noise covariance matrix of the EKF is adaptively arranged according to the signal strength power value in the indoor environment.At last,the statistical covariance matrix configuration of the extended Kalman filter on the state parameters is used to optimize the processing,so as to obtain the final target position.This paper compares the LO-EKF algorithm with the standard LS algorithm and the traditional EKF algorithm by simulation analysis.The simulation results show that the LO-EKF algorithm not only guarantees the short-term accuracy,but also improves the long-term accuracy in the indoor location,compared with the standard LS algorithm and the traditional EKF algorithm.In addition,to a certain extent,the algorithm can also improves the convergence rate of the positioning error.