Journal
IEEE SENSORS JOURNAL
Volume 22, Issue 21, Pages 20866-20876Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2022.3208114
Keywords
Gravity; Inertial navigation; Particle swarm optimization; Trajectory; Correlation; Sensors; Kalman filters; Gravity matching navigation; matching accuracy; particle swarm optimization (PSO); screening iterative
Funding
- National Key Research and Development Program of China [2017YFC0601601, 2016YFC0303006]
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This study improves the particle swarm optimization (PSO) algorithm for gravity-assisted inertial navigation. By using similarity of single-point matching, calculation of differences, and projection correction, the matching accuracy is significantly improved.
The promotion of matching precision is one of the key technical problems of gravity-assisted inertial navigation. In this study, first, the viability of the particle swarm optimization (PSO) algorithm applied to gravity matching inertial navigation is analyzed, and the original PSO algorithm is improved. The similarity of single-point matching is used to round off the matching result points, and the difference between the matching result coordinates and the corresponding inertial coordinates is calculated over a certain period to filter out the relatively poorer matching points. Finally, by calculating the coordinate distance of the first and last points between the consecutive valid matching segments and setting the limit difference, the coordinates of the invalid points between two adjacent valid matching points that meet the limit difference are projected to realize the correction of matching points. The test results show that the improved PSO-based screening iterative gravity matching algorithm can avoid the additional matching error caused by the setting of the search resolution compared with the PSO, new self-organizing hierarchical PSO (NHPSO), and adaptive PSO (APSO) matching algorithm and has significant improvement in the matching accuracy.
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