A Robust and Efficient IMU Array/GNSS Data Fusion Algorithm

The IMU array, composed of multiple IMUs, has been proven to be able to effectively improve the navigation performance in INS/GNSS integrated applications. The conventional IMU-level fusion algorithm, using IMU raw measurements, is straightforward and highly efficient but yields poor robustness when the IMU array is not rigidly installed. On the contrary, the classic INS-level fusion algorithm, using navigation results from each IMU, is immune to the non-rigid installation of the IMU array but suffers a heavy computation load. Here we propose a robust and efficient INS-level fusion algorithm for IMU array/GNSS (eNav-Fusion). Each IMU in the array shares the common state covariance (P matrix) and Kalman gain (K matrix), and the navigation solutions of all IMUs are eventually fused to produce a more accurate solution. The proposed eNav-Fusion was fully evaluated with rigidly and non-rigidly installed IMU arrays. For a rigid 16-IMU array, the processing time of eNav-Fusion was close to that of the IMU-level fusion and only 1.22 times to that of the INS/GNSS algorithm for a single IMU; and the navigation performance was improved by 2.51 times, as expected for such scale of array. For a non-rigid 6-IMU array, in which case the traditional IMU-level fusion does not work, eNav-Fusion still maintained the same accuracy as the classic INS-level fusion algorithm, while the computation load is still close to that of the IMU-level fusion. In conclusion, the proposed eNav-Fusion achieves the same robustness as the INS-level fusion, while only consuming comparable computational complexity to the IMU-level fusion.