Previous magnetic dipole localization algorithms using gradient data attempt to find the position of the magnetic source at the measurement time only. Based on the direct inversion of the magnetic gradient tensor, these methods provide results that can be highly sensitive to temporal noise in data. To avoid a temporally scattered solution, a recursive approach is proposed that is promising for estimating the trajectory and the magnetic moment components of a target modeled as a magnetic dipole source using data collected with a gradiometer. In this study, the determination of target position, magnetic moment, and velocity is formulated as a Bayesian estimation problem for dynamic systems, which could be solved using a sequential Monte Carlo based approach known as the "particle filter." This filter represents the posterior distribution of the state variables by a system of particles which evolve and adapt recursively as new information becomes available. In addition to the conventional particle filter, the proposed tracking and classification algorithm uses the unscented Kalman filter (UKF) to generate the prior distribution of the unknown parameters. The proposed method is then demonstrated by applying it to real data collected when an automobile was passing by a gradiometer either on a straight or a curved track. The results indicate that the recursive method is less sensitive to noise than the direct inversion solution, even if not all the components of the gradient tensor were used.
-In this paper we present a recursive Bayesian solution to the problem of joint tracking and classification of a target modeled at a distance by an equivalent magnetic dipole. Tracking/classification of a magnetic dipole from noisy magnetic field measurements involves the modeling of a non-linear non-Gaussian system. This system allows for complications due to multiple directions of arrival and target maneuver. The determination of target position, velocity and magnetic moment is formulated as an optimal stochastic estimation problem, which could be solved using a sequential Monte Carlo based approach known as the particle filter. In addition to the conventional particle filter, the proposed tracking and classification algorithm uses the unscented Kalman filter (UKF) to generate the transition prior as the proposal distribution.
The roll-and pitch-induced eddy currents create a magnetic field that contributes to the total magnetic signature of naval vessels. The magnetic signature is of concern, as it exposes the ship to the threat of modern influence mines. It is estimated that the eddy current is the second most important source contributing to a ship's underwater magnetic field following the ferromagnetic effect. In the present paper, the finite element (FE) method is used to predict the eddy current signature of a real ship. The FE model is validated using the measurements of the Canadian research vessel CFAV QUEST at the Earth's Field Simulator (EFS) in Schirnau, Germany. Modeling and validation of the eddy current magnetic signature for a real ship represents a novelty in the field. It is shown that the characteristics of this signature depend on frequency. Based on these results, a ship's degaussing system could be improved to cancel both the ferromagnetic and the eddy current contribution to the magnetic signature simultaneously, reducing the susceptibility to sea mines.
The propagation of an electromagnetic signal in a marine environment cannot be modelled as a plane wave due to the high attenuation in seawater and the interactions with the ocean boundaries. Consequently, conventional beamforming techniques are not applicable for electromagnetic source localization. In this work, the Bayesian approach to matched-field processing is used to localize an electromagnetic source and estimate the environmental parameters. In this formulation, the solution to the inverse problem is given by the a posteriori probability distribution calculated here using the Gibbs sampling method. Bayesian inversion theory provides the formalism for estimating parameters, their uncertainties and verification of the estimates convergence. Two situations were investigated for the case where the single frequency measurements represent the magnitudes of two orthogonal horizontal electric field components: (1) all environmental parameters known and (2) unknown seabed conductivity. The objective function that relates the array data to the propagation model and environment parameters was chosen for the practical situation considered.
The electromagnetic (EM) fields radiating from a ship are widely recognized as important components of underwater detection. Galvanic currents flowing in the water around the hull and in the hull generate an underwater electric field. This field is responsible for the extremely low frequency (ELF) EM emission. The rotation of the shaft(s) modulates the galvanic current passing through the shaft-bearing-hull and thus an ELF EM signal is generated into the water. The ELF EM signal from ships was analyzed using the cross wavelet transform and wavelet coherence. The phase angle statistics demonstrated the presence of an additional ELF magnetic field component that was identified as being generated by a rotating vertical permanent magnet. The only vertical part of the ship rotating with the same frequency as the shaft is the nickel aluminum bronze propeller, which contains 3–5% iron. The paper presents the measurements and the investigation methodology that led to the separation of an additional ELF component, and to the calculation of the magnetic moment that caused it.
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