Constrained MEMS-Based INS/UWB Tightly Coupled System for Accurate UGVs Navigation

To enhance the navigation performance and robustness of navigation system combining ultrawideband (UWB) and inertial navigation systems (INS) under complex indoor environments, an improved navigation method-Allan variance (AV) to assist a modified adaptive extended Kalman Filter based on the dynamic weight function (DWF-MAEFF)-is proposed. Firstly, AV is used to improved INS error dynamics by modeling the stochastic noise of an inertial sensor; which can compensate for inertial sensor error caused by stochastic noise during integrated navigation. Secondly, the MAEKF is developed by designing the weight function to adjust the weight of measurement noise reasonably and dynamically, which can further improve the robustness of the AEKF algorithm. Field tests were conducted to verify the effectiveness of the proposed navigation method. The result indicated that an improvement of up to 60% over the existing integrated navigation method based on EKF and AEKF can be obtained by the proposed method.

Automated summary

In order to improve the navigation performance and robustness of a navigation system combining ultrawideband (UWB) and inertial navigation systems (INS) in complex indoor environments, an improved navigation method called Allan variance (AV) is proposed. This method utilizes AV to model the stochastic noise of an inertial sensor and compensates for inertial sensor error caused by stochastic noise. In addition, a modified adaptive extended Kalman Filter based on the dynamic weight function (DWF-MAEFF) is developed to further enhance the robustness of the system. Field tests have shown that this proposed method can achieve up to a 60% improvement compared to existing integrated navigation methods based on EKF and AEKF.