Multisource sensors navigation methodology applied to ADAS testing platform vehicle

This study addresses sensor signal loss in Advanced Driver Assistance Systems (ADAS) testing when switching between open and tunnel test sites. Current methods require pausing and switching sensor devices, disrupting the testing process. This study proposes a multisource sensor integrated navigation algorithm based on the Interacting Multiple Model (IMM) framework, using GNSS/INS error state Kalman filter (ESKF) positioning in open areas and UWB/INS ESKF positioning in GNSS failure scenarios, like tunnels. An Interacting Multiple Model based on Adaptive Transition Probability Matrix (ATPM-IMM) algorithm is introduced, enabling adaptive Markov jump probability for target tracking. The method ensures positioning accuracy using low-cost UWB and IMU and achieves seamless sensor source switching. Compared with traditional GNSS/INS and UWB/INS methods, the proposed approach improves accuracy by 31.17% and 41.23%, respectively, providing a reference for future multisource sensor fusion positioning research.