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Improved Particle Swarm Optimization Screening Iterative Algorithm in Gravity Matching Navigation
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AI
DOI：10.1109/JSEN.2022.3208114.png)
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Abstract
En 中文
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.
Keywords:
Gravity
Inertial navigation
Particle swarm optimization
Trajectory
Correlation
Sensors
Kalman filters
Gravity matching navigation
matching accuracy
particle swarm optimization (PSO)
screening iterative
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4.5
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