Arie Sheinker

Magnetic Anomaly Detection Using a Three-Axis Magnetometer

Magnetic anomaly detection is a good method for detecting ferromagnetic objects, particularly hidden targets. In this work, we address the detection of a moving ferromagnetic target using a static three-axis referenced magnetometer. The analysis and the results are also applicable to the converse case of a static ferromagnetic target and a moving three-axis referenced magnetometer. We use the three magnetometer outputs to build a total magnetic field of the target. This signal is decomposed into a set of orthonormal basis functions, out of which the dominant basis function is chosen as the detector. The detector provides output responses to any target magnetic moment orientation. We support the analysis by a computer simulation and real-world experimental results. The high detection probability and the simple implementation of the proposed method make it attractive for real-time applications.

Localization and magnetic moment estimation of a ferromagnetic target by simulated annealing

In many applications, the detection of a visually obscured magnetic target is followed by the characterization of the target, i.e. localization and magnetic moment estimation. Effective target characterization may reduce the detection false alarm rate as well as direct the searcher toward the target. We address the characterization of a static magnetic target by a three-axis fluxgate magnetometer installed on a stabilized mobile platform. The magnetometer readings are contaminated by magnetic noise, which results in a low signal-to-noise ratio. We formulate the problem as an over-determined nonlinear equation set using a magnetic dipole model for the target and use simulated annealing (SA) in order to rapidly find a good approximation to the global optimum of this equation set. Computer simulations demonstrate high accuracy of the SA method in localizing the target and estimating its magnetic moment in the presence of high-level noise. The high accuracy of the SA method is also exemplified in tests employing real-world magnetic signals. In addition to its high accuracy, the SA method is very rapid, making it appropriate for real-time practical applications.

Magnetic Anomaly Detection Using High-Order Crossing Method

Magnetic anomaly detection (MAD) is a passive method used to detect visually obscured ferromagnetic objects by revealing the anomalies in the ambient Earth magnetic field. In this paper, we propose a method for MAD employing the high-order crossing (HOC) approach, which relies on the magnetic background nature. HOC is an alternative method for spectral analysis using zero-crossing count, also enabling signal discrimination. Tests with real-world recorded magnetic signals show high detection probability even for low signal-to-noise ratio. The high detection probability, together with a simple implementation and low power consumption, makes the HOC method attractive for real-time MAD applications such as intruder detection and for research on an earthquake magnetic precursor.

Localization in 2D Using Beacons of Low Frequency Magnetic Field

In this work we propose methods for object localization in 2D using beacons of low frequency quasi-static magnetic field. From a practical point of view, localization in 2D is sufficient for many applications, requiring much less calculations than in 3D, making it more robust and easier to implement in real-time low power applications. The low frequency magnetic field may penetrate foliage, soil, buildings, and many other types of media. This is an important advantage over traditional localization methods such as sonar or radar, where effective operation requires line-of-sight. Another advantage of the low frequency magnetic fields is that there is no direct influence by bad weather conditions and diurnal variations. Opposite to traditional electromagnetic methods, where operational range is usually more than a wavelength, low frequency induction approach results in a relatively limited localization range. Each beacon comprises a coil generating a magnetic field of a unique frequency in the ULF band. The generated magnetic fields are sensed by a search-coil magnetometer. The magnetometer readings are processed to estimate the magnitude and phase of the received beacons signals, which are used to localize the magnetometer. For a moving object, we propose to combine localization together with tracking algorithm using a data fusion approach. The proposed methods have been tested using numerous computer simulations, showing accurate localization results. A prototype was developed and used in field experiments, validating simulation results. The good accuracy together with a simple implementation makes the proposed methods attractive to many real-time low power field applications.

Aeromagnetic Search Using Genetic Algorithm

We propose the Genetic Algorithm (GA) approach for localization of an underwater magnetic dipole target by an airborne magnetometer. Airborne Magnetic Anomaly Detection (MAD) is used for decades to detect underwater targets, such as sunken ships. The target is assumed as a magnetic dipole, which produces an anomaly in the dominant Earth magnetic field. The aircraft follows a path, sampling the magnetic field and utilizing GA to estimate target location and magnetic moment. The method was simulated on a personal computer, obtaining promising results in the presence of noise. A scatter radius of about 60 m is achieved for SNR value of 0.2, which is acceptable for practical needs.

Magnetic anomaly detection (MAD) of ferromagnetic pipelines using principal component analysis (PCA)

The magnetic anomaly detection (MAD) method is used for detection of visually obscured ferromagnetic objects. The method exploits the magnetic field originating from the ferromagnetic object, which constitutes an anomaly in the ambient earths magnetic field. Traditionally, MAD is used to detect objects with a magnetic field of a dipole structure, where far from the object it can be considered as a point source. In the present work, we expand MAD to the case of a non-dipole source, i.e. a ferromagnetic pipeline. We use principal component analysis (PCA) to calculate the principal components, which are then employed to construct an effective detector. Experiments conducted in our lab with real-world data validate the above analysis. The simplicity, low computational complexity, and the high detection rate make the proposed detector attractive for real-time, low power applications.

Adaptive interference cancelation using a pair of magnetometers

We propose a method for adaptively canceling magnetic interference using a pair of magnetometers that is especially effective for a time-varying interference with an unknown signature. By canceling the interference, the proposed method enables the recovery of the ambient geomagnetic field. Computer simulations and experiments with manipulated real-world data support the theoretical analysis and in combination with other techniques (e.g., calibration), pave the way for magnetic measurements in orbit using boomless CubeSats.