Analytic Hierarchy Process (AHP)-Based Evaluation of Extremely-Low-Frequency Magnetic Field Contribution Rates
Source abstract
Abstract
The extremely-low-frequency (ELF) magnetic fields of submarines serve as key characteristics for target detection, with their formation mechanisms being complex and diverse. Effectively manipulating a submarine to reduce its magnetic signature is crucial for enhancing its magnetic stealth capabilities. However, current research on the impact of various causative factors is insufficient. This study proposes a contribution rate assessment method based on the Analytic Hierarchy Process (AHP) model, aiming to provide a theoretical basis for effective manipulation. Initially, a thorough analysis of the threat causes of a submarine’s ELF magnetic fields is conducted, and a corresponding hierarchical threat structure model is established. Subsequently, magnetic field signal characteristics generated by different causes are obtained through simulation, and threat matrices and characteristic matrices are constructed. Finally, the contribution rates of different causative magnetic fields to the total magnetic field are calculated, and the simulation results validate the effectiveness of the method. At the stern detection line, the contribution rate of the wake magnetic field is the highest, reaching 0.7649. Along the radial detection line, the contribution rate of the shaft frequency magnetic field is the highest and gradually decays, eventually falling below the wake magnetic field at 150 m and remaining at an approximately 0.5 contribution rate. This study calculates the contribution rates under different operational conditions and detection scenarios, laying a technical foundation for research on the comprehensive active control strategies of submarine ELF magnetic fields in different scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Magnetic Properties and Applications · Magnetic Field Sensors Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Intelligent Science, National University of Defense Technology, Changsha 410073, China
Naval Research Institute, Beijing 100161, China
Funding
National Natural Science Foundation of China
References (25)
Show source-provided references
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