Indoor location algorithm based on improved Kalman filter

Abstract

Aiming at the problems of low accuracy and poor anti-noise performance of UWB sensor in time difference of arrival (TDOA) positioning mode in complex indoor environment, an improved Kalman (KF) filtering indoor positioning algorithm (TSK) based on Taylor series is proposed. The main idea of the algorithm is to first linearize the UWB nonlinear positioning equation based on TDOA with first-order Taylor, and combine the error compensation function, then optimize the noise reduction through linear filter KF, and finally iteratively calculate the position coordinates of the labels to be measured according to the set threshold. Finally, the results of MATLAB simulation show that the proposed algorithm is better than the comparison algorithm in the complex occlusion and noise environment.