Magnetometers for tracking metallic targets

Wahlström, Niklas; Callmer, Jonas; Gustafsson, Fredrik

doi:10.1109/icif.2010.5711900

Cited by 19 publications

(22 citation statements)

References 5 publications

(7 reference statements)

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“…• Work in [4]- [10] is based on a linear motion assumptions, which simplifies the modeling of the dipole moment m. However, for other motions where the target orientation changes over time, the dependency on m will be more complicated. One way of solving this is to marginalize the dipole moment as in [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Magnetometer Modeling and Validation for Tracking Metallic Targets

Wahlström

Gustafsson

2014

IEEE Trans. Signal Process.

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Abstract-With the electromagnetic theory as basis, we present a sensor model for three-axis magnetometers suitable for localization and tracking as required in intelligent transportation systems and security applications. The model depends on a physical magnetic dipole model of the target and its relative position to the sensor. Both point target and extended target models are provided as well as a heading angle dependent model. The suitability of magnetometers for tracking is analyzed in terms of local observability and the Cramér Rao lower bound as a function of the sensor positions in a two sensor scenario. The models are validated with real field test data taken from various road vehicles which indicate excellent localization as well as identification of the magnetic target model suitable for target classification. These sensor models can be combined with a standard motion model and a standard nonlinear filter to track metallic objects in a magnetometer network.

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Section: Introductionmentioning

confidence: 99%

Magnetometer Modeling and Validation for Tracking Metallic Targets

Wahlström

Gustafsson

2014

IEEE Trans. Signal Process.

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“…In Wahlström (2010); Wahlström et al (2011); , different models are investigated for localizing vehicles based on a sensor network of magnetometers. Both models for point targets and extended targets were proposed.…”

Section: Traffic Surveillancementioning

confidence: 99%

“…This approximation holds if the distance between the target and the sensor is large in comparison to the characteristic magnetic length of the target which is typically in the range of 1 m to 2 m for passenger cars and 4 m and more for larger vehicles such as busses and trucks. This gives raise to a magnetic dipole field h(t) expressed as where r(t) = r x (t) r y (t) r z (t) T is the position of the target relative to the sensor and m = m x m y m z T is the magnetic dipole moment, which can be considered as a target specific parameter (Wahlström et al, 2010). Two components of the magnetic field (1a) can then be measured with a 2-axis magnetometer…”

Section: Signal Modelmentioning

confidence: 99%

Modeling of Magnetic Fields and Extended Objects for Localization Applications

Wahlström¹

2015

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Cover illustration: The cover shows a magnetic dipole field (3.6), which is used as the sensor model in Paper A. The dipole is placed at the height of the front cover with position r 0 = [104 mm, 90 mm, 0 mm] relative to the bottom left corner of the front cover, with the magnetic dipole moment m = [0. 85, 0.53, 0] being orthogonal to its surface. A 3D cutout of the scalar potential for the magnetic dipole ϕ(r) = (r·m) r 3 is displayed on front, back and side cover, where r is the displacement relative to the position of the magnetic dipole. On the front cover, the field lines of the magnetic dipole field are overlaid.Linköping studies in science and technology. Dissertations.No. 1723 Modeling of Magnetic Fields and Extended Objects for Localization Applications AbstractThe level of automation in our society is ever increasing. Technologies like selfdriving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. These technologies depend upon autonomous localization and situation awareness where careful processing of sensory data is required. To increase efficiency, robustness and reliability, appropriate models for these data are needed. In this thesis, such models are analyzed within three different application areas, namely (1) magnetic localization, (2) extended target tracking, and (3) autonomous learning from raw pixel information. Magnetic localization is based on one or more magnetometers measuring the induced magnetic field from magnetic objects. In this thesis we present a model for determining the position and the orientation of small magnets with an accuracy of a few millimeters. This enables three-dimensional interaction with computer programs that cannot be handled with other localization techniques.Further, an additional model is proposed for detecting wrong-way drivers on highways based on sensor data from magnetometers deployed in the vicinity of traffic lanes. Models for mapping complex magnetic environments are also analyzed. Such magnetic maps can be used for indoor localization where other systems, such as gps, do not work.In the second application area, models for tracking objects from laser range sensor data are analyzed. The target shape is modeled with a Gaussian process and is estimated jointly with target position and orientation. The resulting algorithm is capable of tracking various objects with different shapes within the same surveillance region.In the third application area, autonomous learning based on high-dimensional sensor data is considered. In this thesis, we consider one instance of this challenge, the so-called pixels to torques problem, where an agent must learn a closedloop control policy from pixel information only. To solve this problem, highdimensional time series are described using a low-dimensional dynamical model. Techniques from machine learning together with standard tools from control theory are used to autonomously design a controller f...

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“…where r(t) = r x (t) r y (t) r z (t) T is the position of the target relative to the sensor and m = m x m y m z T is the magnetic dipole moment, which can be considered as a target specific parameter [22]. Two components of the magnetic field (1a) can then be measured with a 2-axis magnetometer…”

Section: Signal Modelmentioning

confidence: 99%

Classification of Driving Direction in Traffic Surveillance Using Magnetometers

Wahlström

Hostettler

Gustafsson

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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Abstract-Traffic monitoring using low-cost 2-axis magnetometers is considered. Though detection of metallic vehicles is rather easy, detecting the driving direction is more challenging. We propose a simple algorithm based on a nonlinear transformation of the measurements, which is simple to implement in embedded hardware. A theoretical justification is provided, and the statistical properties of the test statistic are presented in closed form. The method is compared to the standard likelihood ratio test on both simulated data and real data from field tests, where very high detection rates are reported, despite the presence of sensor saturation, measurement noise and near-field effects of the magnetic field.

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Magnetometers for tracking metallic targets

Cited by 19 publications

References 5 publications

Magnetometer Modeling and Validation for Tracking Metallic Targets

Magnetometer Modeling and Validation for Tracking Metallic Targets

Modeling of Magnetic Fields and Extended Objects for Localization Applications

Classification of Driving Direction in Traffic Surveillance Using Magnetometers

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