Qingzhu LI, Zhining LI, Zhiyong SHI, Hongbo FAN. Multi-target magnetic positioning with the adaptive fuzzy c-means clustering and tensor invariants[J]. Optics and Precision Engineering, 2022, 30(20): 2523
- Optics and Precision Engineering
- Vol. 30, Issue 20, 2523 (2022)
Fig. 1. Dense point cloud formed by magnetic dipole positioning results in the recognition area
Fig. 2. Technical route of multi-target adaptive PR detection for magnetic dipoles
Fig. 3. Schematic diagram of multi-target magnetic dipoles
Fig. 4. Recognition results of MGT， MMA， NSS and CT in the 10 m×10 m survey area
Fig. 5. Magnetic dipole identification area delineated by improved tilt angles θ NSSTilt and θ CTTilt （contour threshold 0）
Fig. 6. Dense point cloud formed by the solution set of the initial position of the target in the recognition area
Fig. 7. Adaptive clustering results of AFCM algorithm for initial position point cloud
Fig. 8. Experiment 1： grid positioning of five magnets
Fig. 9. MGT， MMA， NSS and CT recognition results of five small magnets
Fig. 10. Recognition area of 5 magnet objects delineated by θ NSSTilt and θ CTTilt （contour threshold 0）
Fig. 11. Single-point positioning dense point cloud and AFCM clustering results in the recognition area
Fig. 12. Experiment 2： grid positioning of four magnets
Fig. 13. MGT， MMA， NSS and CT recognition results of four small magnets
Fig. 14. Recognition area of 4 magnet objects delineated by θ NSSTilt and θ CTTilt （contour threshold 0.3）
Fig. 15. AFCM clustering results of four magents in the recognition area
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Table 1. Preset and estimated physical parameters of the 20 magnetic dipoles
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Table 2. Real positions and estimated physical parameters of the nine magnets in the positioning experiment
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