Adaptive Fusion Multi-IMU Confidence Level Location Algorithm in the Absence of Stars

Inertial navigation systems (INSs) often use inertial measurement units (IMUs) to produce precise results by combining them with GPS data. However, precise positioning is difficult in a GPS-denied environment. Most of the existing inertial-aided localization techniques rely on a single IMU, which can perform well when the GPS signal is good. When the GPS is not available, the IMU’s performance will rapidly deteriorate, leading to accumulating errors and positioning failure. In this article, we investigate the error distribution of the INS in the absence of GPS and present a pose estimation approach based on multiple IMU fusion using an adaptive extended Kalman filter (AEKF). Then, we propose a confidence level fusion technique that merges all IMUs into a virtual IMU to reduce redundancy and computational cost. To avoid unnecessary algorithm loss, we provide a confidence level judgment technique. According to the results, the latitude and altitude accuracy are improved by 23% and 17%, respectively, compared to the data from multiple IMUs. Compared to the method based on the law of weak majority that fuses multiple IMUs outputs, longitude and latitude are improved by 13% and 28%, respectively.