A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects

Alimi, Roger; Weiss, Eyal; Ram-Cohen, Tsuriel; Geron, Nir; Yogev, Idan

doi:10.3390/s150923788

Cited by 18 publications

(7 citation statements)

References 30 publications

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“…The solution can be calculated by the optimization method which minimizes the objective error. The common algorithms include genetic algorithm (GA), [26] particle swarm optimization (PSO) algorithm, [4,19,27] Levenberg-Marquardt (LM) algorithm, [12,27] etc.…”

Section: Target Localization Based On Arraymentioning

confidence: 99%

Baseline optimization for scalar magnetometer array and its application in magnetic target localization

Fan¹,

Zheng²,

Kang³

et al. 2018

Chinese Phys. B

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Generally, a magnetic target can be described with six parameters, three describing the position and three describing the magnetic moment. Due to a lack of sufficient components from one magnetometer, we need more than one magnetometer when locating the magnetic target. Thus, a magnetometer array should be designed. The baseline of the array is an important factor that affects the localization accuracy of the target. In this paper, we focus on the localization of a static target by using a scalar magnetometer array. We present the scalar magnetometer array with a cross-shaped structure. We propose a method of determining the optimal baseline according to the parameters of the magnetometer and detection requirements. In the method, we use the traditional signal-to-noise ratio (SNR) as a performance index, and obtain the optimal baseline of the array by using the Monte Carlo method. The proposed method of determining the optimal baseline is verified in simulation. The arrays with different baselines are used to locate a static magnetic target. The results show that the location performance is better when using the array with the optimal baseline determined by the proposed method.

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Section: Target Localization Based On Arraymentioning

confidence: 99%

Baseline optimization for scalar magnetometer array and its application in magnetic target localization

Fan¹,

Zheng²,

Kang³

et al. 2018

Chinese Phys. B

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show abstract

“…The Levenberg-Marquardt (L-M) algorithm can be regarded as a combination of the steepest descent method and the Newton-Gauss method. The main advantage of the L-M algorithm is its rapidity and its well-known and reliable implementation [20]. However, the L-M algorithm cannot guarantee convergence to a global minimum unless the initial parameter guesses are appropriate [21].…”

Section: Localization Algorithm Based On the Pso Algorithmmentioning

confidence: 99%

A Method to Remotely Track a Magnetic Target Using a Scalar Magnetometer Array

et al. 2017

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The orientation of a vector magnetic sensor can affect the measurement accuracy of magnetic anomaly, thereby increasing the localization error of a magnetic target. Compared with vector magnetic sensor, the measurement of the scalar magnetic sensor is almost not influenced by its orientation. Therefore, we present a method for tracking the magnetic target with a static scalar magnetometer array. In this study, the magnitude of the target’s magnetic moment is a key parameter. We isolate it and formulate an optimization problem based on it to estimate the position and magnetic parameters of the target. To calculate the solution of this optimization problem, a dedicated particle swarm optimization (PSO) algorithm is developed. Then, we define a quality index to evaluate the solution calculated by the optimization problem. The proposed method was validated by the simulation and the real data collected when an SUV car was passing by the array on a straight path. The results show that the tracked trajectory is very close to the true trajectory and the quality index can be used as a criterion to allow accepting or rejecting the localization of the target.

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“…In applications like surveillance systems DC jumps can compromise both detection and localization of relevant signals. They are well described in [Alimi 2009] and [Alimi 2015], see also the work of Kozick and Sadler in [Kozick 2008]. Another field of application is the magnetic localization of wireless capsule endoscopy where unwanted DC changes can induce large localization errors.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Detection Algorithms for Large Barkhausen Jumps in Cluttered Environment

et al. 2019

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Modern magnetic sensor arrays conventionally utilize state of the art low power magnetometers such as parallel and orthogonal fluxgates. Low power fluxgates tend to have large Barkhausen jumps that appear as a dc jump in the fluxgate output. This phenomenon deteriorates the signal fidelity and effectively increases the internal sensor noise. Even if sensors that are more prone to dc jumps can be screened during production, the conventional noise measurement does not always catch the dc jump because of its sparsity. Moreover, dc jumps persist in almost all the sensor cores although at a slower but still intolerable rate. Even if dc jumps can be easily detected in a shielded environment, when deployed in presence of natural noise and clutter, it can be hard to positively detect them. This work fills this gap and presents algorithms that distinguish dc jumps embedded in natural magnetic field data. To improve robustness to noise, we developed two machine learning algorithms that employ temporal and statistical physical-based features of a pre-acquired and well-known experimental data set. The first algorithm employs a support vector machine classifier, while the second is based on a neural network architecture. We compare these new approaches to a more classical kernel-based method. To that purpose, the receiver operating characteristic curve is generated, which allows diagnosis ability of the different classifiers by comparing their performances across various operation points. The accuracy of the machine learning-based algorithms over the classic method is highly emphasized. In addition, high generalization and robustness of the neural network can be concluded, based on the rapid convergence of the corresponding receiver operating characteristic curves.

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A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects

Cited by 18 publications

References 30 publications

Baseline optimization for scalar magnetometer array and its application in magnetic target localization

Baseline optimization for scalar magnetometer array and its application in magnetic target localization

A Method to Remotely Track a Magnetic Target Using a Scalar Magnetometer Array

Machine-Learning Detection Algorithms for Large Barkhausen Jumps in Cluttered Environment

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