Abstract
Based on deeply analyzing the characteristic of the battlefield ferromagnetic targets, according to the problems of magnetic detection system, for instance single detection pattern, low detection resolution and poor anti-interference performance, the Giant Magneto-Impedance(GMI) micro-magnetic sensor in combination with the technology of fuzzy neural networks(FNN) were carried as the core of the magnetic detection system. Take advantage of GMI sensor and FNN to realize accurate recognition of the target in the range of nan-otesla magnetic field. In this paper, equable magnetization rotation ellipsoid is used to simulate the tank and military truck, taking the triaxial magnetic moments and semi-focal length, that is M x , M y , M z , c, as recognition characte-ristic quantity , and the FNN is used to recognize the tank and military truck including the categories and motion directions. The method reaches good recognition effect through experimental verification, and it has significance to improve detection range and recognition accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Panina, L., Mohri, K.: Magneto-impedance effect in amorphous wires. Appl. Phys. Lett. 65, 1189–1191 (1994)
Mohri, K., Uchiyama, T., Panina, L.: Recent advances of micro-magnetic sensors and sensing application. Sensors & Actuators A: Physical 59, 1–8 (1997)
Kentaro, T., Yichi, H., Masayoshi, E.: Three-axis magneto-impedance effect sensor system for detecting position and orientation of catheter tip. Sensors & Actuators A: Physical 111, 304–309 (2004)
Sugeno, M., Yasukawa, T.: A fuzzy logical based approach to qualitative modeling. IEEE Trans. on Fuzzy Systems 1(1), 7–31 (1993)
Hairong, S., Pu, H., Lihui, Z.: A New Method to construct Fuzzy Systems Based on Rule Selecting. In: ICMLC 2004, vol. 3, pp. 1855–1858 (2004)
Takagi, T., Sugeno, M.: A robust stabilization problem of fuzzy control system and its application to backing up control of truck-trailer. IEEE Trans. on Fuzzy System. 2(2), 119–134 (1994)
Chunsheng, L., Qian, X., Shenguang, G.: A modeling algorithm for detection of moving on-water magnetic objects. Journal of China Ordnance 2(26), 192–195 (2005)
Pingxian, Y., Shenguang, G.: The physical field of warship, pp. 25–60. Weapon Industry Press (1992)
Zhenyuan, J., Ying, Z., Yingdong, S., Wenyan, T.: The application of NN in recognition of vehicles. Journal of Natural Science of Heilongjiang University 1(26), 39–42 (2009)
Tao, Z., Tingjin, L., Xuehai, Z.: The recognition method of small underground objective based on fuzzy neural network. Journal of Projectiles, Rockets, Missiles and Guidance 4(27), 316–319 (2007)
Hitoshi, L., Masafumi, H.: Adaptive fuzzy inference neural network. Pattern Recognition 10(37), 2049–2057 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Wu, C., Deng, J., Yang, Y. (2010). A Research of Fuzzy Neural Network in Ferromagnetic Target Recognition. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-12990-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
Online ISBN: 978-3-642-12990-2
eBook Packages: EngineeringEngineering (R0)