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A mathematical model of extremely low frequency ocean induced electromagnetic noise

Journal Article · · AIP Conference Proceedings
DOI:https://doi.org/10.1063/1.4958451· OSTI ID:22608570
;  [1]
  1. Research & Development Engineer, Anyeshan Limited, Dhaka (Bangladesh)

Magnetic Anomaly Detection (MAD) system uses the principle that ferromagnetic objects disturb the magnetic lines of force of the earth. These lines of force are able to pass through both water and air in similar manners. A MAD system, usually mounted on an aerial vehicle, is thus often employed to confirm the detection and accomplish localization of large ferromagnetic objects submerged in a sea-water environment. However, the total magnetic signal encountered by a MAD system includes contributions from a myriad of low to Extremely Low Frequency (ELF) sources. The goal of the MAD system is to detect small anomaly signals in the midst of these low-frequency interfering signals. Both the Range of Detection (R{sub d}) and the Probability of Detection (P{sub d}) are limited by the ratio of anomaly signal strength to the interfering magnetic noise. In this paper, we report a generic mathematical model to estimate the signal-to-noise ratio or SNR. Since time-variant electro-magnetic signals are affected by conduction losses due to sea-water conductivity and the presence of air-water interface, we employ the general formulation of dipole induced electromagnetic field propagation in stratified media [1]. As a first step we employ a volumetric distribution of isolated elementary magnetic dipoles, each having its own dipole strength and orientation, to estimate the magnetic noise observed by a MAD system. Numerical results are presented for a few realizations out of an ensemble of possible realizations of elementary dipole source distributions.

OSTI ID:
22608570
Journal Information:
AIP Conference Proceedings, Journal Name: AIP Conference Proceedings Journal Issue: 1 Vol. 1754; ISSN APCPCS; ISSN 0094-243X
Country of Publication:
United States
Language:
English