Aiming at the problem that the indoor moving target positioning algorithm is vulnerable to abnormal noise

a robust adaptive unscented particle filtering indoor moving target localization algorithm is proposed.According to the distribution characteristics of signal receiving intensity

the signal strength observations are corrected by polynomial fitting method. The robust estimation of the unscented particle filtering algorithm is evaluated. The Mahalanobis distance is used to construct the equivalent weight function to suppress the abnormal noise pair algorithm and increase the ability of the algorithm to control environmental disturbances. The simulation results show that compared with the traditional particle filter algorithm and the unscented particle filter algorithm

the algorithm has higher precision in indoor positioning and stronger algorithm robustness.