A covariance shaping filtering method for tightly-coupled MIMU/GNSS of UAV. Issue 10 (4th November 2019) - Liu, Bin; Xu, Jiangtao; Fu, Bangsheng; Hao, Yong; An, Tianyu
Abstract : Purpose: Regarding the important roles of accuracy and robustness of tightly-coupled micro inertial measurement unit (MIMU)/global navigation satellite system (GNSS) for unmanned aerial vehicle (UAV). This study aims to explore the efficient method to improve the real-time performance of the sensors. Design/methodology/approach: A covariance shaping adaptive Kalman filtering method is developed. For optimal performance of multiple gyros and accelerometers, a distribution coefficient of precision is defined and the data fusion least square method is applied with fault detection and identification using the singular value decomposition. A dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed. Findings: Hardware-in-the-loop numerical simulation was adopted, the results indicate that the gain of the covariance shaping adaptive filter is self-tuning by changing covariance weighting factor, which is calculated by minimizing the cost function of Frobenius norm. With the improved method, the positioning accuracy with tightly-coupled MIMU/GNSS of the adaptive Kalman filter is increased obviously. Practical implications: The method of covariance shaping adaptive Kalman filtering is efficient to improve the accuracy and robustness of tightly-coupled MIMU/GNSS for UAV in complex and dynamic environments and has great value for engineering applications. Originality/value: A covariance shaping adaptive Kalman filtering method is presented and a novel dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed, to improve the real-time performance in complex and dynamic environments.