Abstract:

Due to the raising complexity of the indoor environment, the influence of the non-line-of-sight error is gradually increasing in indoor positioning.How to reduce the non-line-of-sight error in the indoor positioning environment is particularly significant.The UWB indoor positioning technology was selected, Kalman filter has been used widely in reducing the positioning error in indoor non-line-of-sight situations.However, in the process of switching between line of sight and non-line-of-sight scenes, the Kalman filter will produce a new error.To solve this problem, an improved Kalman filter indoor positioning algorithm based on CHAN was proposed.The ranging results of five or more base stations were used to construct a positioning solution equation set.Then the CHAN algorithm that was sensitive to positioning errors under non-line-of-sight conditions was selected to calculate the result.Finally, residual processing was performed with the results of each base station and different confidence regions were judged in line with the residual processing.For different confidence regions, the Kalman filter preset gain factor K was used to improve the stability of the positioning result and reduce the positioning error in the conversion process.In the simulation environment, the error can reach about 80 cm.In real scene, the algorithm can reduce the positioning error in non-line-of-sight scenes to 60 cm, which can improve accuracy 60% higher than normal complex indoor positioning.

Key words: non-line-of-sight, CHAN, residual error judgement, Kalman filter