A Robust and Efficient IMU Array/GNSS Data Fusion Algorithm
Tisheng Zhang, Man Yuan, Liqiang Wang, Hailiang Tang, Xiaoji Niu
Abstract
The inertial measurement unit (IMU) array, composed of multiple IMUs, has been proven to be able to effectively improve the navigation performance in inertial navigation system (INS)/global navigation satellite system (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 nonrigid 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 nonrigidly 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.22\times $ </tex-math></inline-formula> to that of the INS/GNSS algorithm for a single IMU; and the navigation performance was improved by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.51\times $ </tex-math></inline-formula>, as expected for such scale of array. For a nonrigid 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.