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In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and generate a safe substructure. Even today, maintenance personnel still have to manually locate the sleepers if they cannot be detected by computer vision systems or visually by the operator. Here we developed a first of its kind magnetic sleeper detector, even able to find sleepers, buried in gravel, undetectable by vision based systems. Our approach of magnetic detection is based on a DC magnetic field excitation and a detector moving with respect to the rail system, including the sleepers and fasteners for mounting the rails. Due to railway application constraints a large air gap between the sensor and the sleeper structure is required, which significantly complicates the magnetic sensing task for robust sleeper detection. The design and optimization of the magnetic circuit was based on extensive 3D simulation studies to ensure highest possible variation in magnetic flux density at the sensor locations for absence and presence of a sleeper. Furthermore, a low noise and high sensitivity electronic circuit has been realized to cope with sensor signal offsets from unknown or changing sensor orientations with respect to the earth's magnetic field, or magnetic interferences from other trains potentially passing by during active measurements. Since we only want to detect sleepers in close vicinity of the moving sensor system, digital signal processing of the acquired signals can easily compensate for disturbing slowly changing or static field components within real world application scenarios. We demonstrate that magnetic detection of even buried sleepers on railway tracks is possible for distances of up to 172 mm between the sensor and the sleeper. This enables an even higher level of railway maintenance automation previously impossible in certain scenarios. [ABSTRACT FROM AUTHOR]

In practical application, existing two-point magnetic gradient tensor (MGT) localization methods have a maximum detection distance of only 2.5 m, and the magnetic moment vectors of measured targets are all unknown. In order to realize remote, real-time localization, a new two-point magnetic localization method based on self-developed, ultra-sensitive superconducting quantum interference device (SQUID) magnetometers and MGT invariants is proposed. Both the magnetic moment vector and the relative position vector can be directly calculated based on the linear positioning model, and a quasi-Newton optimization algorithm is adopted to further improve the interference suppression capability. The simulation results show that the detection distance of the proposed method can reach 500 m when the superconducting MGT measurement system is used. Compared with Nara's single-point tensor (NSPT) method and Xu's two-point tensor (XTPT) method, the proposed method produces the smallest relative localization error (i.e., significantly less than 1% in the non-positioning blind area) without sacrificing real-time characteristics. The causes of and solutions to the positioning blind area are also analyzed. The equivalent experiments, which were conducted with a detection distance of 10 m, validate the effectiveness of the localization method, yielding a minimum relative localization error of 4.5229%. [ABSTRACT FROM AUTHOR]

Magnetic anomaly detection (MAD) technology based on the magnetic gradient tensor (MGT) has broad application prospects in fields such as unexploded ordnance detection and mineral exploration. The difference approximation method currently employed in the MGT measurement system introduces measurement errors. Designing reasonable geometric structures and configuring optimal structural parameters can effectively reduce measurement errors. Based on research into differential MGT measurement, this paper proposes three simplified planar MGT measurement structures and provides the differential measurement matrix. The factors that affect the design of the baseline distance of the MGT measurement system are also theoretically analyzed. Then, using the magnetic dipole model, the error analysis of the MGT measurement structures is carried out. The results demonstrate that the planar cross-shaped structure is optimal, with the smallest measurement error, only 3.15 × 10−10 T/m. Furthermore, employing the control variable method, the impact of sensor resolution constraints, noise level, target magnetic moment, and detection distance on the design of the optimal baseline distance of the MGT measurement system is simulated and verified. The results indicate that the smaller the target magnetic moment, the farther the detection distance, the lower the magnetometer resolution, the greater the noise, and the greater the baseline distance required. These conclusions provide reference and guidance for the construction of the MGT measurement system based on triaxial magnetometers. [ABSTRACT FROM AUTHOR]

The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants is seldom studied. In this work, unmanned aerial vehicle-based MAD field experiments are conducted on cargo vessels and NdFeB magnets as typical magnetic objects to give a set of visualized magnetic field flux density images. Isometric finite element models are established, calibrated and analyzed according to the experiment configuration. A maximum detectable distance map as a function of target size and measurement resolution is then obtained from parametric sweeping on an experimentally calibrated finite element analysis model. We find that the logarithm of detectable distance is positively proportional to the logarithm of object size while negatively proportional to the logarithm of resolution, within the ranges of 1 m~500 m and 1 pT~1 μT, respectively. A three-parameter empirical formula (namely distance-size-resolution logarithmic relationship) is firstly developed to determine the most economic sensor configuration for a given detection task, to estimate the maximum detection distance for a given magnetic sensor and object, or to evaluate minimum detectable object size at a given magnetic anomaly detection scenario. [ABSTRACT FROM AUTHOR]

This paper presents a portable device based on an Anisotropic Magnetoresistance (AMR) sensor for Steel Health Monitoring. The system operates by detecting magnetic anomalies in ferromagnetic materials caused by strain, corrosion, etc. This sensor can have various applications in the transportation, building, and aerospace fields for safety and maintenance monitoring of ferromagnetic materials. In this work, a low-cost device, that combines a high-sensitivity AMR sensor, a microcontroller, and supporting electronics has been designed and implemented. This sensor allows the contactless measurement of the magnetic flux density along three axes, when placed above the material under test, while the microcontroller and the required electronics enable real-time analysis and monitoring of measurements. In order to house and protect the sensor under various circumstances, a 3D-printed enclosure has also been created. This device can be used along with rehabilitation techniques for treatment of defective areas of an under-test material. Its versatility allows it to be employed in a variety of testing conditions for both single-point and scanning mode monitoring. The device's portability, ease of use and applicability to on-site measurements make it accessible to a wide range of users, requiring only a personal computer to display the measurements. Finally, measurements are presented to prove the device's accuracy for steel health monitoring. [ABSTRACT FROM AUTHOR]

Traditional methods of orthogonal basis function decomposition have been extensively used to detect magnetic anomaly signals. However, the determination of the relative velocity between the detection platform and the magnetic target remains elusive in practical detection. And, the non-ideal uniform motion of the magnetic target further complicates the process and adversely impacts the detector's performance. To address this challenge, this paper introduces an adaptive scale factor method based on orthogonal basis function decomposition. This new method can be used to adjust the relative velocity between the detection platform and the magnetic target and to refine the characteristic time in the basis functions. In this paper, a mathematical relationship between the scale factor and the relative velocity is established, which is subsequently fitted into a Gauss function curve. The optimal scale factor, denoted as β, is adaptively chosen from the fitting curve when the magnetic target moves at a non-ideal uniform velocity with an unknown motion state. The results of simulations indicate that the scale factor improves the signal-to-noise ratio of the magnetic anomaly signals in a non-ideal state. And, this method can improve the energy value of OBF decomposition by 17.7%. Simultaneously, this method ensures that the moment the magnetic target passes the CPA coincides with the energy peak of the orthogonal basis detection, which improves the accuracy by 54.1% compared with the traditional method. The effectiveness and precision of the proposed method are verified using simulations and practical experiments. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In practical application, existing two-point magnetic gradient tensor (MGT) localization methods have a maximum detection distance of only 2.5 m, and the magnetic moment vectors of measured targets are all unknown. In order to realize remote, real-time localization, a new two-point magnetic localization method based on self-developed, ultra-sensitive superconducting quantum interference device (SQUID) magnetometers and MGT invariants is proposed. Both the magnetic moment vector and the relative position vector can be directly calculated based on the linear positioning model, and a quasi-Newton optimization algorithm is adopted to further improve the interference suppression capability. The simulation results show that the detection distance of the proposed method can reach 500 m when the superconducting MGT measurement system is used. Compared with Nara’s single-point tensor (NSPT) method and Xu’s two-point tensor (XTPT) method, the proposed method produces the smallest relative localization error (i.e., significantly less than 1% in the non-positioning blind area) without sacrificing real-time characteristics. The causes of and solutions to the positioning blind area are also analyzed. The equivalent experiments, which were conducted with a detection distance of 10 m, validate the effectiveness of the localization method, yielding a minimum relative localization error of 4.5229%.

Due to the complexity of the combat environment, the traditional acoustic detection technologies of submarines are gradually limited, and the importance of non-acoustic detection technologies of submarines is increasingly shown. This article respectively introduced the magnetic anomaly detection technology, laser detection technology, wake detection technology, electric field detection technology, and other non-acoustic detection technologies of submarines, described the advantages and limitations of these technologies, and predicted their development trend, so as to provide a reference for the development of detection technologies of submarines.

In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads to difficulty in potential function regulation. In this paper, a new compound tri-stable stochastic resonance (CTSR) model is proposed to address this problem by combining a Gaussian Potential model and the mixed bi-stable model. The weak magnetic anomaly signal detection system consists of the CTSR system and judgment system based on statistical analysis. The system parameters are adjusted by using a quantum genetic algorithm (QGA) to optimize the output signal-to-noise ratio (SNR). The experimental results show that the CTSR system performs better than the traditional tri-stable stochastic resonance (TTSR) system and BSR system. When the input SNR is -8 dB, the detection probability of the CTSR system approaches 80%. Moreover, this detection system not only detects the magnetic anomaly signal but also retains information on the relative motion (heading) of the ferromagnetic target and the magnetic detection device. [ABSTRACT FROM AUTHOR]

Magnetic anomaly signal detection has broad application prospects in offshore defense, which is an urgent problem for realizing remote and long-term monitoring. In this paper, an underwater magnetic anomaly detection model is proposed, and wavelet domain analysis and orthogonal basis decomposition detection methods are introduced. Through the analysis of magnetic anomaly signal characteristics and wavelet decomposition frequency, adaptive layer determination method is proposed, and independent processing flow of denoising orthogonal basis decomposition energy detection was performed. To address the problem of wavelet domain denoising under different decomposition scales, different shrinkage coefficients are used to independently deal with detail coefficients to improve the effect of high-frequency noise suppression. The results show that compared with the traditional wavelet denoising, the signal-to-noise ratio of this method is improved by approximately 27%, and the false recognition rate is reduced by 39%.

To solve the problem of optimal course of anti-submarine aircraft in the process of searching submarine using magnetic detector, three indexes and a method for evaluating the ability of anti-submarine aircraft searching submarine by magnetic detector with different courses are proposed, on the basis of modeling and analysis of submarine magnetic field. Then the ability above is simulated and analyzed, and the result shows that the anti-submarine aircraft has the best detection effect along the north and south direction, and the worst detection effect along the east and west direction.

Magnetic anomaly detection (MAD) technology based on the magnetic gradient tensor (MGT) has broad application prospects in fields such as unexploded ordnance detection and mineral exploration. The difference approximation method currently employed in the MGT measurement system introduces measurement errors. Designing reasonable geometric structures and configuring optimal structural parameters can effectively reduce measurement errors. Based on research into differential MGT measurement, this paper proposes three simplified planar MGT measurement structures and provides the differential measurement matrix. The factors that affect the design of the baseline distance of the MGT measurement system are also theoretically analyzed. Then, using the magnetic dipole model, the error analysis of the MGT measurement structures is carried out. The results demonstrate that the planar cross-shaped structure is optimal, with the smallest measurement error, only 3.15 × 10−10 T/m. Furthermore, employing the control variable method, the impact of sensor resolution constraints, noise level, target magnetic moment, and detection distance on the design of the optimal baseline distance of the MGT measurement system is simulated and verified. The results indicate that the smaller the target magnetic moment, the farther the detection distance, the lower the magnetometer resolution, the greater the noise, and the greater the baseline distance required. These conclusions provide reference and guidance for the construction of the MGT measurement system based on triaxial magnetometers.

The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants is seldom studied. In this work, unmanned aerial vehicle-based MAD field experiments are conducted on cargo vessels and NdFeB magnets as typical magnetic objects to give a set of visualized magnetic field flux density images. Isometric finite element models are established, calibrated and analyzed according to the experiment configuration. A maximum detectable distance map as a function of target size and measurement resolution is then obtained from parametric sweeping on an experimentally calibrated finite element analysis model. We find that the logarithm of detectable distance is positively proportional to the logarithm of object size while negatively proportional to the logarithm of resolution, within the ranges of 1 m~500 m and 1 pT~1 μT, respectively. A three-parameter empirical formula (namely distance-size-resolution logarithmic relationship) is firstly developed to determine the most economic sensor configuration for a given detection task, to estimate the maximum detection distance for a given magnetic sensor and object, or to evaluate minimum detectable object size at a given magnetic anomaly detection scenario.

With the acceleration of the investigation and development of marine resources, the detection and location of submarine pipelines have become a necessary part of modern marine engineering. Submarine pipelines are a typical weak magnetic anomaly target, and their magnetic anomaly detection can only be realized within a certain distance. At present, a towfish or an autonomous underwater vehicle (AUV) is mainly used as the platform to equip magnetometers close to the submarine pipelines for magnetic anomaly detection. However, the mother ship directly affects the towfish, thus causing control interference. The AUV cannot detect in real time, which affects the magnetic anomaly detection and creates problems regarding detection efficiency. Meanwhile, a two-part towed platform has convenient control, thus reducing the interference of the towed mother ship and real-time detection. If the platform can maintain constant altitude sailing through the controller, the data accuracy in the actual magnetic anomaly detection can be guaranteed. On the basis of a two-part towed platform, a magnetic detection system with constant altitude sailing ability for submarine pipelines was constructed in this study. In addition, experimental verification was conducted. The experimental verification research shows that the constant altitude sailing experiment of the two-part towed platform verifies that the platform has good constant altitude sailing ability in both a hydrostatic environment and the actual marine environment. Meanwhile, the offshore magnetic anomaly detection experiment of submarine pipelines verifies the stable measurement function of the magnetic field and the function of the system to detect magnetic anomaly of submarine pipelines.

To solve the problem of optimal course of anti-submarine aircraft in the process of searching submarine using magnetic detector, three indexes and a method for evaluating the ability of anti-submarine aircraft searching submarine by magnetic detector with different courses are proposed, on the basis of modeling and analysis of submarine magnetic field. Then the ability above is simulated and analyzed, and the result shows that the anti-submarine aircraft has the best detection effect along the north and south direction, and the worst detection effect along the east and west direction. [ABSTRACT FROM AUTHOR]

Traditional methods of orthogonal basis function decomposition have been extensively used to detect magnetic anomaly signals. However, the determination of the relative velocity between the detection platform and the magnetic target remains elusive in practical detection. And, the non-ideal uniform motion of the magnetic target further complicates the process and adversely impacts the detector’s performance. To address this challenge, this paper introduces an adaptive scale factor method based on orthogonal basis function decomposition. This new method can be used to adjust the relative velocity between the detection platform and the magnetic target and to refine the characteristic time in the basis functions. In this paper, a mathematical relationship between the scale factor and the relative velocity is established, which is subsequently fitted into a Gauss function curve. The optimal scale factor, denoted as β, is adaptively chosen from the fitting curve when the magnetic target moves at a non-ideal uniform velocity with an unknown motion state. The results of simulations indicate that the scale factor improves the signal-to-noise ratio of the magnetic anomaly signals in a non-ideal state. And, this method can improve the energy value of OBF decomposition by 17.7%. Simultaneously, this method ensures that the moment the magnetic target passes the CPA coincides with the energy peak of the orthogonal basis detection, which improves the accuracy by 54.1% compared with the traditional method. The effectiveness and precision of the proposed method are verified using simulations and practical experiments.

Practical remote vessel detection schemes are switching from the active lidar and sonar based methods to modern passive schemes such as analyzing the geomagnetic anomaly initiated from a moving remote vessel. The detection of a remote vessel and its heading direction has been fully studied in our recently published work through analyzing the two-dimensional frequency spectrum image of the magnetic wake captured by an array of magnetic sensors positioned under the sea surface. Instead of employing the fixed-positioned multi-sensor detection scheme, in this work, we study the heading detection of a remote vessel traveling in shallow/deep water through analyzing the measured magnetic anomaly captured by a single airborne magnetic sensor. After representing mathematical expressions of the captured magnetic wake initiated from the remote vessel, we manage to formulate the Fourier transform of the magnetic wake, and show that the detection of a remote vessel as well as its heading direction can be estimated through detecting the frequency corresponding to the peak amplitude of the spectrum. We show through theoretical and numerical results that the lower the cross-angle between the drone and vessel traveling directions, the more accurate the estimation of the heading angle. [ABSTRACT FROM AUTHOR]

Aiming at the problem of magnetic anomaly detection under low signal-to-noise ratio, a full magnetic gradient detection via minimum entropy and gradient orthonormal basis function is proposed in this paper. Firstly, the mathematical model of magnetic dipole gradient tensor and the experimental method of collecting magnetic gradient tensor signals are introduced. Secondly, a new minimum entropy detector is designed, the basic functions which are processed by new minimum entropy detector can be orthogonalized easily. Thirdly, a group of new standard orthogonal basis functions are constructed according to the characteristics of magnetic gradient anomaly signals. Finally, the detector and orthogonal basis functions are used to detect the magnetic anomaly. Both numerical simulation signals and experiment signals are used to prove the effectiveness of the methods. • A new magnetic gradient minimum entropy detector is designed on the orthonormal basic function. • A group of new standard orthogonal basis functions are constructed on the characteristics of magnetic gradient anomaly signals. • The detector and orthogonal basis functions are used to detect the magnetic anomaly. [ABSTRACT FROM AUTHOR]

In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads to difficulty in potential function regulation. In this paper, a new compound tri-stable stochastic resonance (CTSR) model is proposed to address this problem by combining a Gaussian Potential model and the mixed bi-stable model. The weak magnetic anomaly signal detection system consists of the CTSR system and judgment system based on statistical analysis. The system parameters are adjusted by using a quantum genetic algorithm (QGA) to optimize the output signal-to-noise ratio (SNR). The experimental results show that the CTSR system performs better than the traditional tri-stable stochastic resonance (TTSR) system and BSR system. When the input SNR is -8 dB, the detection probability of the CTSR system approaches 80%. Moreover, this detection system not only detects the magnetic anomaly signal but also retains information on the relative motion (heading) of the ferromagnetic target and the magnetic detection device.

Dipole approximation is a common simplified method in magnetic field research, which is widely used in magnetic anomaly detection, control of magnetic device and magnetic field experiment. The accuracy of magnetic dipole approximation will determine the upper limit of related applications. Previous studies have shown that the dipole approximation of a typical magnetic dipole simulator (e.g., a solenoid coil) can lead to a positioning error of up to 50% in localization of magnetic object in near field. In order to solve this problem, this paper aims to design a coil structure that fits well with the standard magnetic dipole. Firstly, the structure of cylindrical solenoid with planar symmetry and rotational symmetry is optimized and analyzed. On this basis, a multi-layer solenoid structure with rotational symmetry and plane symmetry is proposed. Then, the coil structure is optimized by gradient descent method, taking the inner and outer radius, height, layer spacing and quantity of the solenoid as variables. Compared with the traditional coil, this coil structure can greatly reduce the error of dipole approximation, and has the advantages of large magnetic moment and small geometric size. Simulation results show that the positioning error percentage can be reduced to less than 0.2% when the new-type coil is tested in the same area.

In order to improve the weak magnetic detection ability under the background of Gaussian colored magnetic environment noise, a magnetic anomaly detection method based on feature fusion and isolation forest (IForest) algorithm is proposed in this paper. The method uses different feature algorithms to extract the statistical features, time-frequency features and fractal features of the signal, reduces the dimensionality of the features by principal component analysis (PCA) and generates feature fusion tensors. Finally the IForest algorithm is used to achieve target detection. The simulation and experimental results show that the method has a higher detection rate under different SNR of Gaussian color noise, which is approximately 5%-18% higher than that of the traditional feature detection algorithm. This method can train an effective detection model with only a small number of negative samples. Compared with the fully connected neural network (FCN) model trained with unbalanced samples, the detection rate increases by approximately 5%-12%, and it takes less time.

The existing magnetic target localization methods are greatly affected by the geomagnetic field and exist approximation errors. In this paper, a two-point magnetic gradient tensor localization model is established by using the spatial relation between the magnetic target and the observation points derived from magnetic gradient tensor and tensor invariants. Based on the model, the equations relating to the position vector of magnetic target are constructed. Solving the equations, a new magnetic target localization method using only a two-point magnetic gradient tensor and no approximation errors is achieved. To accurately evaluate the localization accuracy of the method, a circular trajectory that varies in all three directions is proposed. Simulation results show that the proposed method is almost error-free in the absence of noise. After adding noise, the maximum relative error percentage is reduced by 28.4% and 2.21% compared with the single-point method and the other two-point method, respectively. Furthermore, the proposed method is not affected by the variation in the distance between two observation points. At a detection distance of 20 m, the maximum localization error is 1.86 m. In addition, the experiments also verify that the new method can avoid the influence of the geomagnetic field and the variation in the distance, and achieve high localization accuracy. The average relative error percentage in the y-direction is as low as 3.78%. [ABSTRACT FROM AUTHOR]

Magnetic anomaly signal detection (MASD) is a passive method for the detection of visually obscured ferromagnetic objects. This paper proposes a new method for MASD based on the ambient field of power frequency magnetic (power transmission line system). Moreover, a new information extraction technique is extended by employing Synchrosqueezed Wavelet Transform (SSWT) to improve the accuracy of the MASD method. With the extended information extraction technique, the time-frequency information of the anomalies can be efficiently distinguished from the power frequency magnetic anomaly signal. The multi-component of the time-frequency information is separated by extracting the time-frequency ridges in the spectrogram. The complexity of time-frequency information is evaluated using Rayleigh entropy. Compared with the continuous wavelet transform and short-time Fourier transform, the Rayleigh entropy of our method is reduced by 4.1886 and 4.3623, respectively. Finally, the efficiency of the new method is verified by the outfield experiments. [ABSTRACT FROM AUTHOR]

In the background of strong interference noise, it is one of the crucial technologies of magnetic anomaly detection system to effectively detect and identify weak magnetic anomaly signals (MAS) gene- rated by targets. The detection and recognition of target signal under very low signal-to-noise ratio(SNR)below −10dB have not been achieved in the existing magnetic anomaly detection technology. To tackle these challenges, this paper proposes a magnetic anomaly recognition method based on fourth-order aperiodic stochastic resonance with the aid of sto- chastic resonance theory. The main nature of new algorithm is a four-layer fusion of single potential well stochastic resonance and has real-time recognition ability for MAS without prior information. When simulated magnetic anomaly signal of single object and mutil-targets were assumed in different condition, effect of parameters of algorithm was analyzed and range of optimal parameters was obtained. Through simulation and experimental verification of targets data, the new algorithm could realize target signal recognition in complex interference environment with SNR lower than −15dB,which demonstrates the efficacy of our proposed method and offer some useful design insights to practical MAD system. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm to localize a magnetic dipole using a limited number of noisy measurements from magnetic field sensors. The algorithm is based on the theory of compressed sensing, and exploits the sparseness of the magnetic dipole in space. Beforehand, a basis consisting of magnetic dipole fields belonging to individual dipoles in an evenly spaced 3D grid within a specified search domain is constructed. In the algorithm, a number of sensors is chosen which measure all three magnetic field components. The sensors are chosen optimally using QR pivoting. Using the pre-constructed basis and the obtained field measurements, a sparse representation in the location domain is computed using $\ell _{{1}}$ optimization. Based on the resulting sparse representation, the location and magnetic moment of the magnetic dipole are estimated. An extension to an iterative method is implemented, where the basis and chosen sensors improve after every location estimate. Numerical simulations have been performed to verify the algorithm, and experiments have been done for validation. The proposed algorithm is shown to be effective in localizing magnetic dipoles. [ABSTRACT FROM AUTHOR]

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Innovative "Orientation-Attitude" model analyzes blind spot distribution patterns in ferromagnetic target localization methods. • Improved two-point localization method based on magnetic gradient tensor reduces blind spots. • Improved method's accuracy matches the Scalar Triangulation and Ranging method and outperforms the Gradient Higher-Order Tensor method. • Improved method offers the greatest effective locating distance for targets at specific orientations and attitudes. Magnetic gradient tensor-based detection and localization techniques, renowned for their safety, convenience, and high resolution, have found widespread applications in various fields such as medical assistance, military defense, and geological exploration. However, some localization methods based on magnetic gradient tensors require precise isolation of the target-induced magnetic anomaly from the background magnetic field, limiting their applicability. Therefore, this study explores a localization method based on the two-point magnetic gradient tensor (TPM) that obviates the need for background field removal. Its localization blind spots are intuitively demonstrated by using the Orientation-Attitude analysis model. The criterion for pre-judging the accuracy of the localization and an improved method (TPM-im) that eliminates partial blind spots are proposed on the basis of this analysis. The theoretical analysis contrasts the localization accuracy, blind spots, and maximum detection range of TPM-im using a novel 6-sensor measurement array with two conventional localization methods that do not necessitate background magnetic field removal. The results illustrate that, at a distance of 10 m and with a magnetic moment of 100 A·m2, TPM-im demonstrates an average localization error of 0.41%. Moreover, TPM boasts a maximum detection range of 33.35 m, further highlighting its superior capabilities. [ABSTRACT FROM AUTHOR]

Target detection technology based on magnetic anomalies, due to its wide detection range, high localization accuracy, all weather conditions and other characteristics, plays an important role in geological prospecting, medical monitoring, disaster rescue and other fields. Some nonmagnetic or weakly magnetic metal targets are difficult to observe with passive magnetic anomaly detection (MAD) methods. The active MAD method based on the eddy current magnetic effect can locate nonmagnetic metal targets. However, traditional detection methods based on magnetic dipoles require knowledge of the target magnetic moment in advance and are not suitable for near-field detection. To solve these problems, this paper establishes a detection model based on a nonmagnetic metal shell target and derives the eddy current magnetic field (ECMF) expression of the nonmagnetic metal shell target. An ECMF extraction method that separates the ECMF from the excitation magnetic field and background interference from the perspective of the signal phase is further proposed. Then, the particle swarm optimization (PSO) algorithm is used to locate the nonmagnetic metal target. When the metal target is located within the range of 1.2 m*1.2 m, the localization error is approximately 2.5%. This method is important and valuable in the fields of medical pill tracking, drug dose conversion monitoring, trapped miner locating, indoor autonomous robot navigation, unexploded object detection and other fields. [ABSTRACT FROM AUTHOR]

Based on the development of numerous commercial cheap or low noise magnetometers, some publications feature systems of magnetic anomaly detection. They are meanly based on matrix inversion gradients and are built with multi-magnetometer single head in order to derive spatially the sensed magnetic field. Meanwhile, these examples often suffer from a lack of true performance analysis, based on a “systemic approach.” As an example, they take account partially of the main sensor, mechanical or conditioning electronic characteristics. In that way, we propose a theoretical modeling of 1D and 2D magnetic head detection capabilities that could be extended to 3D. We show that the distance of detection could be quantified with the help of three main parameters: the baseline, the sensor mismatch in terms of sensitivity (V/T) or misalignment, and a defined signal-to-noise ratio of the measurement ($\textit {SNR}_{m}$). With a reasonable built system (sensitivity disparity of 0.6 %, misalignment lower than some (°) and $\textit {SNR}_{m}$ of 120 dB), a magnetic dipole could be localized and quantified, from $\times1$ up to $\times20$ gradiometer base line with a relative error lower than 30 %, and from $\times3$ up to $\times50$ with a $\textit {SNR}_{m}$ of 160 dB, as an example. Notice that the highest distance of detection is mainly bounded by the $\textit {SNR}_{m}$ value and the intrinsic head spatial transfer function. At a close distance, we have enlarged significantly the surface of the source localization, by using optimization algorithms when classical gradiometric method appears non-functional. To exemplify this systemic approach, we have built and characterized a setup. It is based on the IoT principles, where acquired data are pushed over-the-air through a gateway. Finally, they are post-processed in quasi-real time with a laptop. [ABSTRACT FROM AUTHOR]

The application of high-performance magnetic sensors and the parameter estimation of ferromagnetic target from magnetic anomaly data are two important parts in magnetic anomaly detection (MAD) technique. In this work, we proposed a detection approach using a self-designed high-sensitivity three-axis magnetoelectric (ME) magnetic sensor based on a hybrid algorithm of particle swarm optimization (PSO) and simulated annealed (SA) algorithm. The magnetic dipole model is utilized as a universal model in the parameter estimation. Combining the strong global exploration ability and fast convergence of PSO algorithm with the probabilistic acceptance criterion in SA algorithm, the hybridization entitled PSO-SA algorithm for magnetic anomaly detection is expounded in detail. The ME sensor was applied to construct a test platform with an equivalent magnetic noise (EMN) lower than 26.08pT $/ \surd $ Hz at 1Hz. The proposed method was evaluated by three controlled experiments performed on the test platform using the ME sensor. Based on the observed results for the designed experiments, a high fitting level has been demonstrated by the goodness indexes of the proposed algorithm as high as 0.9182, 0.9287 and 0.9320 for the three experimental data, respectively. Moreover, the characteristics of the estimated parameters are in good agreement with the experimental design, which proves the reliability of the proposed algorithm and reveals its universality in magnetic anomaly detection as well. [ABSTRACT FROM AUTHOR]

Aeromagnetic surveys play an important role in many fields, for example, archaeology, anti-submarine warfare, and geophysical exploration. Being in the geomagnetic field, the aircraft generates a great deal of magnetic interference, resulting in bad performance during detection surveys. Thus, it is necessary and important to perform aeromagnetic compensation in advance. Conventional aeromagnetic compensation methods consider that the geomagnetic gradient is approximately zero after bandpass filtering, bringing about the inaccuracy of compensation coefficients. To address this issue, an improved aeromagnetic compensation method robust to geomagnetic gradient is proposed. In this study, the International Geomagnetic Reference Field (IGRF) model was employed to model the geomagnetic gradient. Then, the estimated geomagnetic gradient was subtracted from the measured data, which improved the accuracy of the compensation equations. Field experiments were conducted to verify the effectiveness of the proposed method. The experimental results show that compared to the traditional method, the compensation performance of the proposed method was improved by 152% to 329%. For the level flight, the standard deviation of residual noise after compensation can be as low as 3.3pT. The results indicate that the proposed method can significantly improve the compensation effect, showing great benefits for weak magnetic anomaly detection. [ABSTRACT FROM AUTHOR]

To aid the magnetic anomaly detection (MAD) of underground ferromagnetic pipelines, this paper proposes a geometric modeling method based on the magnetic dipole reconstruction method (MDRM). First, the numerical modeling of basic pipe components such as straight sections, bends and elbows, and tee joints are discussed and the relevant mathematical formulations for these components are derived. Next, after analyzing the function of MDRM and various element division strategies, the sectional division and blocked division methods are introduced and applied to the appropriate pipeline components to determine the volume and center coordinates of each element, establishing the general models for the three typical pipeline components considered. The resulting volume and center coordinates of each component are the fundamental parameters for determining the MAD forwarding of underground ferromagnetic pipelines using the MDRM. Finally, based on the combination and transformation of the basic pipeline components considered, the visualized geometric models of typical pipeline layouts including parallel pipelines, pipelines with elbows, and a pipeline with a tee joint are constructed. The results demonstrate the feasibility of the proposed method of geometric modeling for the MDRM, which can be further applied to the finite element modeling of these and other components when analyzing MAD data. Furthermore, the models with output parameters proposed in this paper establish a foundation for the inversion of MAD.

In this paper we introduce an end-to-end deep learning (DL) framework for magnetic anomaly detection (MAD) and denoising. This framework consists of two neural networks: a binary classification network for magnetic anomaly detection and a regression network for geomagnetic noise suppression. The two networks work in a cascade mode: the magnetic field measurement is first sent to the detection network to check the existence of the anomaly signal, and then to the denoising network for extracting the signal from the geomagnetic noise if the detection result is positive. The core idea of our proposed method is that the characteristics of both the magnetic anomaly signal and the geomagnetic noise can be learned from massive training data. The experimental results show that: (1) under the same false alarm rate constraint, the probability of detection of our proposed method is above 80% when the signal-to-noise ratio (SNR) equals -6 dB, while the orthogonal basis function (OBF) method fails when the SNR is below 0 dB; (2) for geomagnetic noise suppression, an improvement of 10 to 15 dB is achieved for data with input SNRs between -5 and 15 dB. Our results paved the way for data-driven magnetic anomaly detection and denoising.

The nonlinear stochastic resonance (SR) system possesses the ability of taking advantage of noise to enhance the weak signal when the SR system, signal and noise reach to the matching relation. It provides an effective approach to detect the weak magnetic anomaly signal in low signal-to-noise ratio. However, in practical applications, the measured magnetic anomaly signal may be a peak signal, a trough signal, or a combination of the two due to the uncertainty of magnetic target orientation. Hence it is difficult to maintain a good detection performance with single SR system because the SR system output is directly influenced by signal waveforms. Aiming to this, a new strategy using the parallel monostable SR (PMSR) system is proposed, which can ensure the good detection performance regardless of a peak signal, a trough signal, or a combination of the two. Besides, we take the kurtosis index as the criterion and search the optimal system parameters in SR system. The simulation and experiment results indicate its availability, validity and that it can achieve a good detection performance in different waveforms. It can be expected to be widely used in the field of magnetic anomaly detection with PMSR system.

The existing magnetic target localization methods are greatly affected by the geomagnetic field and exist approximation errors. In this paper, a two-point magnetic gradient tensor localization model is established by using the spatial relation between the magnetic target and the observation points derived from magnetic gradient tensor and tensor invariants. Based on the model, the equations relating to the position vector of magnetic target are constructed. Solving the equations, a new magnetic target localization method using only a two-point magnetic gradient tensor and no approximation errors is achieved. To accurately evaluate the localization accuracy of the method, a circular trajectory that varies in all three directions is proposed. Simulation results show that the proposed method is almost error-free in the absence of noise. After adding noise, the maximum relative error percentage is reduced by 28.4% and 2.21% compared with the single-point method and the other two-point method, respectively. Furthermore, the proposed method is not affected by the variation in the distance between two observation points. At a detection distance of 20 m, the maximum localization error is 1.86 m. In addition, the experiments also verify that the new method can avoid the influence of the geomagnetic field and the variation in the distance, and achieve high localization accuracy. The average relative error percentage in the y-direction is as low as 3.78%.

Magnetic anomaly signal detection (MASD) is a passive method for the detection of visually obscured ferromagnetic objects. This paper proposes a new method for MASD based on the ambient field of power frequency magnetic (power transmission line system). Moreover, a new information extraction technique is extended by employing Synchrosqueezed Wavelet Transform (SSWT) to improve the accuracy of the MASD method. With the extended information extraction technique, the time-frequency information of the anomalies can be efficiently distinguished from the power frequency magnetic anomaly signal. The multi-component of the time-frequency information is separated by extracting the time-frequency ridges in the spectrogram. The complexity of time-frequency information is evaluated using Rayleigh entropy. Compared with the continuous wavelet transform and short-time Fourier transform, the Rayleigh entropy of our method is reduced by 4.1886 and 4.3623, respectively. Finally, the efficiency of the new method is verified by the outfield experiments.

[Display omitted] • Innovative portable device for on-site steel health monitoring, based on an AMR sensor. • A magnetic yoke ensures accurate detection of defects on the ferromagnetic material. • A microcontroller enables real-time data analysis and wired or wireless transmission. • The results have shown successful detection of the defects of different steel samples. • The device's low energy consumption and high sensitivity enables its use in various applications for precise, fast and contactless measurements of ferromagnetic materials. Electromagnetic sensors are widely used for safety and maintenance inspection purposes of ferromagnetic materials used in aerospace, construction and transport industry. In this paper, a portable device based on an Anisotropic Magnetoresistance (AMR) sensor for steel health monitoring is presented. The principle of operation of this device is the detection of magnetic anomalies of ferromagnetic materials, such as steel, due to stresses, corrosion, or other kind of defections. For this purpose, a cost-effective, low-consumption and high-sensitivity device has been developed, consisting of a sensor, a magnetic yoke and a microcontroller. The AMR sensor, is placed above the material under test, giving the ability of contactless measurement of the magnetic flux density on 3 axes. Additionally, a yoke, consisting of two iron poles and a permanent magnet between them, is used in this implementation, in order to create a closed magnetic circuit with the steel sample, enhancing the measurements. Then, a microcontroller with the appropriate electronic components receives the acquired data, enabling their real-time analysis and visualization of the measurements through a personal computer, smartphone, etc. Moreover, a 3D-printed enclosure has been designed and constructed, which both protects the sensor and maintains the desired position and alignment with the samples. The device can be portable due to an integrated Li-Po battery and its Wi-Fi capabilities. It can be used to determine the location of the defective spots or zones of the material under inspection, facilitating its treatment through rehabilitation methods that can be applied on the affected area. Both single-point and scanning mode monitoring are possible, making it suitable for many testing scenarios. Furthermore, its ease of use and portability render it accessible to a wide range of users, and appropriate for on-site measurements. The initial measurements using the device on test samples, demonstrate its reliability and accuracy in steel health monitoring. [ABSTRACT FROM AUTHOR]

• design and conduct of a measurement campaign in sea conditions. • measured data transferring to probability distributions. • stationary and nonstationary GPS error analysis. • Monte Carlo Markov Chain simulation. • analysis of GPS error influence on the quality of ship magnetic signature reproduction. In previous studies, the authors performed the magnetic signature reconstruction of the marine ship Zodiak as part of the measurement campaign focused on recording magnetic data and the relative position of a ship during its passage over a magnetometer immersed on the testing ground. A high degree of representation of the magnetic signature was obtained. However, the recorded measurement data revealed new patterns of the multi-dipole model behavior that were not observed in the synthetic data based analyzes. It was assumed that the main factor influencing the abovementioned behavior of the model is the error in determining the geographical position of the ship in relation to the magnetometer. Therefore, another research was carried out to determine the relative position of the ship and the measurement device in sea conditions, in the area of the test site used in the previous study. For this purpose, two different classes of GPS receivers were used. The first receiver was the same as that used to determine the position of Z odiak in the previous measurement campaign, while the second receiver, treated as a reference, was a top-class geodetic receiver. The difference in indications between these two receivers gave a picture of the scale of errors in the data recorded during the previous measurement campaign. These errors are used in the article to analyze the effect of inaccuracies in determining the ship position on the quality of magnetic signature reproduction. Two types of signature reproduction error were introduced – the error based only on the data collected from the ship's paths, and the error in the entire area of magnetic anomaly. The model of Zodiak was used to determine the value of the magnetic flux outside the paths. Profiles of differences in indications of GPS receivers at sea which were obtained from actual measurements were used to analyze the errors in determining the ship position. A measurable result of the work reported in the article is the map of the loss of quality of magnetic signature reproduction as a function of the ship position determination error, which can indicate the range of applicability of the model and the described method. [ABSTRACT FROM AUTHOR]

Magnetic anomaly detection (MAD) has been widely used for detecting some hidden ferromagnetic objects. Orthonormal basis function (OBFs) detector is one of the most popular methods of MAD. The OBFs detector works effectively under white Gaussian noise. However, the practical geomagnetic noise is colored noise with a power spectral density of 1/fα (f is frequency and α is noise exponent), and the signal-to-noise ratio (SNR) is usually very low. In order to improve magnetic anomaly detection performance in the case of colored noise and low SNR, a novel detection method by using full connected neural network (FCN) is proposed in the paper. Firstly, the detector based on FCN is designed and two kinds of features that include the signal's statistical property and the magnetic moment's characteristics of the target are extracted and used as the input of neural network; Then, the optimal network structure with proper number of layers and nodes is obtained; Finally, the detection performance of the detector under different SNRs and orientations of target's magnetic moment is evaluated. Simulation results show that the proposed method has better performance and achieves an incremental detection probability of about 5% to 40% under colored Gaussian noise with different noise exponent than traditional method. In the end, experiments under real geomagnetic noise also verify the effectiveness of the proposed method.

Aeromagnetic surveys play an important role in many fields, for example, archaeology, anti-submarine warfare, and geophysical exploration. Being in the geomagnetic field, the aircraft generates a great deal of magnetic interference, resulting in bad performance during detection surveys. Thus, it is necessary and important to perform aeromagnetic compensation in advance. Conventional aeromagnetic compensation methods consider that the geomagnetic gradient is approximately zero after bandpass filtering, bringing about the inaccuracy of compensation coefficients. To address this issue, an improved aeromagnetic compensation method robust to geomagnetic gradient is proposed. In this study, the International Geomagnetic Reference Field (IGRF) model was employed to model the geomagnetic gradient. Then, the estimated geomagnetic gradient was subtracted from the measured data, which improved the accuracy of the compensation equations. Field experiments were conducted to verify the effectiveness of the proposed method. The experimental results show that compared to the traditional method, the compensation performance of the proposed method was improved by 152% to 329%. For the level flight, the standard deviation of residual noise after compensation can be as low as 3.3pT. The results indicate that the proposed method can significantly improve the compensation effect, showing great benefits for weak magnetic anomaly detection.

Magnetic anomaly detection is an effective method for detecting ferromagnetic targets. The practical application of target detection by measuring the magnetic gradient has been confirmed in many studies, and its suppression of background noise has been widely recognized. However, the variation of the unidirectional magnetic field gradient is very small in a short distance, and the amplitude of the signal is also affected by the direction of the target’s magnetic moment and the shaking of the sensor platform itself. These make it difficult to extract the signal from complex background interference. In order to solve these problems, this paper is devoted to use the variation of three axial magnetic gradients in the geomagnetic coordinate system caused by the presence of magnetic target (we call it full magnetic gradient) to construct the magnetic anomaly characteristic of the target. A set of orthonormal basis functions is further derived to linearly describe this characteristic, which improves the signal-to-noise ratio (SNR) of the signal from the perspective of signal energy. The present work main contribution is to propose a full magnetic gradient orthonormal basis function (FMG-OBF) method to effectively characterize magnetic anomaly of magnetic targets. Compared with the traditional one-direction magnetic gradient orthonormal basis function method, the SNR of this method is greatly improved, and it is not affected by a change in the target magnetic moment’s direction nor by the shaking of the sensor carrier platform. Therefore, it has great value in the practical application of aeromagnetic detection. [ABSTRACT FROM AUTHOR]

We are concerned with the inverse problem of identifying magnetic anomalies with varying parameters beneath the Earth using geomagnetic monitoring. Observations of the change in Earth's magnetic field–the secular variation–provide information about the anomalies as well as their variations. In this paper, we rigorously establish the unique recovery results for this magnetic anomaly detection problem. We show that one can uniquely recover the locations, the variation parameters including the growth or decaying rates as well as their material parameters of the anomalies. This paper extends the existing results in [9] by two of the authors to the more practical and challenging scenario with varying anomalies. [ABSTRACT FROM AUTHOR]

For an airborne sensor, there is a pressing need to be able to detect/track submerged submarines, shipwrecks, sea mines, unexploded explosive ordnance, and buried drums during maritime surveillance. Traditional usage is the magnetic anomaly detection (MAD), where the small changes in the earth's magnetic field caused by the ferrous components of the targets are measured. The primary means of long-range detection and classification of targets are with passive and active acoustic sensors, and MAD is used for accurate final localization. MAD could also be used for land-based targets but this is not common. Knowing the relationship between the magnetic signature and the kinematic parameters, the tracking problem can be formulated under a Bayesian framework. In this article, multiple nonlinear filters are used for a real single surface-target tracking problem in maritime surveillance using an airborne total-field sensor. The posterior Cramér–Rao lower bound for MAD is derived. Given the total-field measurements, these filters can estimate the kinematic states as well as the permanent moments and induced moments effectively. Results demonstrate the effectiveness of the proposed nonlinear filters as well as the impact of using MAD as part of airborne surveillance. [ABSTRACT FROM AUTHOR]

Satellite-based systems are the most widespread solution for outdoor localization. However, they present well-known limitations in multipath environments and non-line-of-sight satellite conditions, e.g. tunnels, underground, urban canyons, and multilevel roads, being frequently combined with dead reckoning techniques. Inertial sensors present cumulative errors, and geomagnetic-field information is often distorted by strong local magnetic fields caused by road infrastructure. We turn this magnetic weakness into strength by proposing MagLand, an approach with detection and matching steps to leverage these anomalies as signatures for localization purposes. For anomaly detection, we adopt a window-based technique and apply supervised binary classification, choosing a random forest. We select one nearest centroid algorithm with dynamic time warping to match input data streams to reference signatures, providing guidelines to define and collect them. Real data experiments with off-the-shelf devices in challenging road scenarios show MagLand's feasibility with anomaly detection accuracy of 91% and matching with lane distinction of up to 93%. Magnetic landmarks can be extremely useful to address limitations of current localization systems and improve their performance, e.g. by providing an alternative in GPS limited areas, anchors for integrity monitoring, or resetting dead reckoning cumulative errors. [ABSTRACT FROM AUTHOR]

A superconducting quantum interference device (SQUID) planar gradiometer is an extremely sensitive sensor for magnetic gradient measurements. It has been shown to have potential applications for aeromagnetic detection. The major challenge when operating an aeromagnetic SQUID system in actual environment is the motion noise, including the inherent response resulting from gradiometer imbalance and from magnetic interferences. Three orthogonal reference magnetometers are usually adopted to improve the gradiometer balance. However, magnetic interference coming from the system itself also needs to be compensated. In this article, a mathematical model of the motion noise picked up by the gradiometer is derived from the traditional magnetic total field compensation method. Based on the model, the signals from the triaxial magnetometer can also be used to compensate the eddy current contribution to the magnetic interference. To verify the compensation method, a SQUID planar gradiometer system was set up and used for flight trials. The gradient field noise and imbalance of our homemade gradiometer were measured to be 100 fT/m/rt(Hz) and 2E-4 in the lab. In flight, the motion-induced peak-to-peak output of the gradiometer was reduced from 170 to about 0.1 nT/m, so that a magnetic anomaly signal of about 2 nT/m from a cargo ship was clearly recognized. [ABSTRACT FROM AUTHOR]

Magnetic target tracking is a low-cost, portable, and passive method for tracking materials wherein magnets are physically attached or embedded without the need for line of sight. Traditional magnet tracking techniques use optimization algorithms to determine the positions and orientations of permanent magnets from magnetic field measurements. However, such techniques are constrained by high latencies, primarily due to the numerical calculation of the gradient. In this study, we derive the analytic gradient for multiple-magnet tracking and show a dramatic reduction in tracking latency. We design a physical system comprising an array of magnetometers and one or more spherical magnets. To validate the performance of our tracking algorithm, we compare the magnet tracking estimates with state-of-the-art motion capture measurements for each of four distinct magnet sizes. We find comparable position and orientation errors to state-of-the-art magnet tracking, but demonstrate increased maximum bandwidths of 336%, 525%, 635%, and 773% for the simultaneous tracking of 1, 2, 3, and 4 magnets, respectively. We further show that it is possible to extend the analytic gradient to account for disturbance fields, and we demonstrate the simultaneous tracking of 1 to 4 magnets with disturbance compensation. These findings extend the use of magnetic target tracking to high-speed, real-time applications requiring the tracking of one or more targets without the constraint of a fixed magnetometer array. This advancement enables applications such as low-latency augmented and virtual reality interaction, volitional or reflexive control of prostheses and exoskeletons, and simplified multi-degree-of-freedom magnetic levitation. [ABSTRACT FROM AUTHOR]

• Wavelet width and sensing range angle are used to characterize the time-varying magnetic signature. • The signature peak strength, magnetometer's sensing range d and angle θ , are all independent of velocity. • The signature waveform width T is used as a cue, which has been found to be inversely proportional to the speed by d = vT. • The sensing range angle θ retains to be nearly θ y = 90° and θ x = 130° for y-axis and x-axis respectively, originating from constant d/CPA factors. A detection procedure by employing the induced magnetic signature characteristics for magnetic targets detection and identification is proposed in this paper. The highly sensitive tunneling magnetoresistive (TMR) sensor with low self-noise is adopted, while the magnetic target is traveling along a straight line at various speed and CPA (closest path approach) for magnetic anomaly detection (MAD). The triple-axis TMR sensor is characterized with a high sensitivity around 100 mV/V/Oe in a linear range of ±1 Oe and a self-noise of 170 pT/√Hz at 1 Hz. The signature waveform width and sensing range angle are used to analyze the signatures. It is found that the sensing range angle is independent of changing velocity and CPA. The proposed signature interpretation strategy is demonstrated by both experimental and simulated data. [ABSTRACT FROM AUTHOR]

A magnetic anomaly detection (MAD) scheme detects magnetic changes generated from the ferromagnetic target in the geomagnetic field. The three-axis magnetic vector sensor is considered as a technique to extend the detection range for the MAD scheme. However, in the conventional MAD scheme, the scheme detects only the magnitude signal calculated from the three-axis signal, and thus, the sensitivity of the three-axis signal which has been changed by the target cannot be directly reflected. In addition, when the axis of the magnetic vector sensor is physically rotated during the installation, the detection probability of the MAD scheme using the three-axis signal is deteriorated because the strength and pattern of the three-axis signal are changed by the rotated axis. In this paper, we propose a continuous angle alignment-based MAD (CAA-MAD) to extend the detection range of the target and to reduce the false alarm rate. Experimental results show that the proposed CAA-MAD can extend the detection range significantly compared with the conventional magnitude-based MAD. [ABSTRACT FROM AUTHOR]