Aiming at the problems of low location accuracy and poor adaptability in the traditional LANDMARC localization algorithm by the interference of the indoor environment, an improved LANDMARC indoor location algorithm based on CKF is proposed in this paper. Firstly, the algorithm obtains the state prediction value of the target to be targeted by the traditional LANDMARC algorithm. Then, with the purpose of improving the localization accuracy of the algorithm and reducing the fluctuation of the positioning result, the obtained state prediction value is taken as the observation and filtered by the Cubature Kalman Filter (CKF) algorithm. Finally, the results obtained by filtering are used instead of the estimated values obtained by LANDMARC as the state estimates of the targets to be targeted. The experimental results show that the proposed algorithm improves the localization accuracy and volatility compared with the traditional LANDMARC localization algorithm and the LANDMARC localization algorithm based on the Unscented Kalman Filter (UKF) algorithm, with the localization error of 60% in the tested label is less than 0.5m, it can get a realistic goal of moving trajectories used in indoor localization.