Seamless Indoor-Outdoor Switching Localization Algorithm Based on CKF

Abstract

To solve the problem of large positioning error and discontinuous positioning of special forces members when moving in cross-region indoors and outdoors, and to compensate for the linearization error and local convergence problem that may exist in the extended Kalman filter (EKF) in nonlinear systems, that is, the iterative results may be trapped in a local optimum situation, a seamless indoor-outdoor switching localization algorithm based on cubature Kalman filter (CKF) is proposed. CKF does not require the computation of Jacobian matrices, which can improve computational efficiency and filtering accuracy to a certain extent. In the system, an inertial measurement unit (IMU) is employed to correct the positioning errors of ultra-wideband (UWB) and BeiDou navigation satellite system (BDS). The positioning data from UWB and BeiDou in cross-region are weighted fused and then fused with the data from the IMU using CKF to obtain the final accurate positioning information. This study designs a scene-switching mechanism to achieve seamless switching between indoor and outdoor positioning scenes. By jointly analyzing the positioning accuracy of UWB and BeiDou, the positioning scene is determined, and appropriate counting thresholds are set to avoid frequent erroneous scene switches. Experimental results show that the proposed algorithm achieves a positioning accuracy of approximately 21.7 cm in cross-region, which can enable seamless integration of indoor and outdoor positioning, avoid positioning jumps, and enhance positioning accuracy.