Comprehensive Features Matching Algorithm for Gravity Aided Navigation - Liu, Hui; Wu, Lin; Bao, Lifeng; Li, Qianqian; Zhang, Panpan; Xi, Menghan; Wang, Yong (IEEE geoscience and remote sensing letters, 2022)
The gravity navigation performance is challenged by gravity measurement accuracy and heading precision which is determined by navigation equipment and measurement environment. In this letter, a new gravity matching algorithm based on comprehensive features matching (CFM) is proposed to evaluate the similarity correlation between gravity measurement sequences and reference map. Different from classical matching algorithms based on the single gravity statistical values characteristics, this method could combine gravity roughness comparison correlation, gravity contrast comparison correlation, and regression fitting degree of two gravity sequences, respectively, which helps to incorporate the gravity numerical error sensitivity and structural similarity of gravity sequences in matching navigation process. Simulation and measured data experiments in the South China Sea were carried out, and a better than 1.5 n miles matching navigation accuracy was maintained by CFM method regardless of an increase in gravity measurement error and heading error. Experimental results demonstrated that the proposed method had less dependence on gravity measurement accuracy or vehicle heading precision for higher gravity matching navigation performance, which could provide a new option for future underwater passive autonomous navigation.