Indoor Localization Algorithm Based on SRD and Adaptive Kalman Filter

Ultra-wideband (UWB), as an emerging technology in recent years, has the characteristics of high-precision distance measurement capability, resistance to multi-path fading and multi-base station positioning, and has become the mainstream in the field of industrial positioning. However, due to the complexity of the environment and the presence of noise, a single positioning algorithm often produces large errors. Therefore, this paper proposes an ultra-wideband indoor hybrid positioning algorithm based on the squared difference (SRD) algorithm and adaptive Kalman filtering. Using the position estimate obtained by the SRD algorithm, the non-line-of-sight error (NLOS) is identified by comparing the set threshold with the residual size of the TDOA measurement obtained by the ultra-wideband module. The Kalman filter parameters are dynamically adjusted based on the identification results to improve the automatic It adapts to the Kalman filter iterative process, resists noise interference and reduces the influence of abnormal measurements. Finally, the motion trajectory and its residual value are obtained through experiments. The results show that the algorithm in this paper can significantly improve the accuracy of positioning, and the maximum positioning error is reduced from 1.22m. to 0.42m, the root mean square error of the positioning result is 0.11m.