| Autor: |
Ge, Jian, Wang, Shuqiao, Dong, Haobin, Liu, Huan, Zhou, Dan, Wu, Shuang, Luo, Wang, Zhu, Jun, Yuan, Zhiwen, Zhang, Haiyang |
| Zdroj: |
IEEE Sensors Journal; 9/15/2020, Vol. 20 Issue 18, p10717-10723, 7p |
| Abstrakt: |
Using a prior data to solve for the characteristic parameters of a moving magnetic target is crucial in its detection. For this purpose, we developed a real-time detection method using distributed scalar sensor networks based on a hybrid algorithm combining particle swarm optimization and the Gauss-Newton method. The magnetic anomaly fitting model of the target is established using the magnetic-dipole-moment principle. The PSO’s insensitivity to the initial solution is used to obtain a rough solution of the coefficients of the fitting, and then a more accurate solution is developed using the Gauss-Newton method, exploiting its advantage in high precision. Furthermore, to evaluate the proposed method, we built a test platform. The detection results show that the goodness-of-fit reaches 0.9565 and only requires 3.4 s; in the 16 m horizontal distance, the calculated trajectory overlaps significantly the actual trajectory, and errors in position and speed are around 10% and 4.25%, respectively. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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