.png){kind=icon}
返回
A Robust and Efficient IMU Array/GNSS Data Fusion Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2024.3418383.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 1.22x to that of the INS/GNSS algorithm for a single IMU; and the navigation performance was improved by 2.51 x , 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.
Keyword:
Navigation
Arrays
Data integration
Global navigation satellite system
Classification algorithms
Computational complexity
Sensors
Data fusion
inertial measurement unit (IMU) array
inertial navigation system (INS)/global navigation satellite system (GNSS)
micro-electromechanical system (MEMS) IMU
期刊
IF:
4.5
论文数:
2.1W
被引数:
7.3W
机构
引用论文
OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer.png)
IEEE SENSORS JOURNAL
OAAI
IEEE SENSORS JOURNALIF4.5
OAAI分享收藏A Performance Enhancement Method for Redundant IMU Based on Neural Network and Geometric Constraint
PREAI
分享收藏
分享收藏
查看更多内容