Abstract: To address the problems of degraded ultra-wideband (UWB)positioning accuracy in non-line of sight (NLOS)environments and the divergence of long-term Inertial Navigation System (INS)positioning results, a UWB/INS integrated positioning method based on adaptive unscented Kalman filter (AUKF)and graph optimization is proposed.First, based on an improved IGGIII function, M-estimation is performed by assigning different weights to the deviations between UWB-measured pseudoranges and INS-positioning pseudoranges, achieving NLOS identification and reconstruction.Second, AUKF is employed to fuse observations for UWB/INS integrated positioning estimation.By introducing an adaptive factor that adjusts the Kalman gain according to the innovation variation, the accuracy of the integrated positioning estimate is enhanced.Finally, a graph optimization method constrained by INS increments and line-of-sight (LOS)UWB pseudoranges is adopted, further suppressing the NLOS error in the integrated positioning and improving the accuracy of the positioning estimate.Positioning experiments demonstrate that the proposed algorithm achieves an average positioning accuracy of 0.14 m, representing an improvement of approximately 22%compared to traditional integrated positioning methods, and effectively ensures positioning performance in complex indoor scenarios.

Key words: non-line of sight, adaptive Kalman filter, M-estimation, graph optimization, combined positioning