Unscented Particle Filter for Tracking a Magnetic Dipole Target

Birsan, Marius

doi:10.1109/oceans.2005.1639993

Cited by 24 publications

(29 citation statements)

References 2 publications

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“…the heading Ψ k will also be constant as well as the magnetic dipole moment m k according to (15). We could also consider another parametrization of the target motion.…”

Section: A Sensor Model For Constant Velocitymentioning

confidence: 99%

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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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“…the heading Ψ k will also be constant as well as the magnetic dipole moment m k according to (15). We could also consider another parametrization of the target motion.…”

Section: A Sensor Model For Constant Velocitymentioning

confidence: 99%

“…In order to validate (15), predictions of the magnetic dipole moment m for new Ψ can be made by usingm 0 andD…”

Section: E Validation Of Direction Dependent Target Modelmentioning

confidence: 99%

Magnetometer Modeling and Validation for Tracking Metallic Targets

Wahlström

Gustafsson

2014

IEEE Trans. Signal Process.

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“…One way of doing this is by estimating the unknowns r(t) and m from the measurement of y k and extract the direction information from the estimated trajectoryr(t). This can either be done in a batch approach where a whole data batch is used at once or through object tracking using, for example, a Kalman or particle filter as it has been done in [23], [24] and [25]. However, this is a nonlinear problem and convergence to a global optimum is not guaranteed.…”

Section: Signal Modelmentioning

confidence: 99%

“…Notice that the classification is performed in a distributed manner by first computing the ratios p j in each sensor according to (20), and then, these values are fused according to (23).…”

Section: Sensor Fusionmentioning

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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“…The idea of using a permanent magnet for localization and tracking was proposed by Birsan [22]. Using a permanent magnet as a source is beneficial compared to an active source as it can be kept small, works passively and is therefore more suitable for attachment on various body parts.…”

Section: Introductionmentioning

confidence: 99%

Hand Pose Estimation by Fusion of Inertial and Magnetic Sensing Aided by a Permanent Magnet

Kortier¹,

Antonsson²,

Schepers³

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Tracking human body motions using inertial sensors has become a well-accepted method in ambulatory applications since the subject is not confined to a lab-bounded volume. However, a major drawback is the inability to estimate relative body positions over time because inertial sensor information only allows position tracking through strapdown integration, but doesn't provide any information about relative positions. In addition, strapdown integration inherently results in drift of the estimated position over time. We propose a novel method in which a permanent magnet combined with 3D magnetometers and 3D inertial sensors are used to estimate the global trunk orientation and relative pose of the hand with respect to the trunk. An Extended Kalman Filter is presented to fuse estimates obtained from inertial sensors with magnetic updates such that the position and orientation between the human hand and trunk as well as the global trunk orientation can be estimated robustly. This has been demonstrated in multiple experiments in which various hand tasks were performed. The most complex task in which simultaneous movements of both trunk and hand were performed resulted in an average rms position difference with an optical reference system of 19.7 ± 2.2 mm whereas the relative trunk-hand and global trunk orientation error was 2.3 ± 0.9 and 8.6 ± 8.7 deg respectively.

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Unscented Particle Filter for Tracking a Magnetic Dipole Target

Cited by 24 publications

References 2 publications

Magnetometer Modeling and Validation for Tracking Metallic Targets

Magnetometer Modeling and Validation for Tracking Metallic Targets

Classification of Driving Direction in Traffic Surveillance Using Magnetometers

Hand Pose Estimation by Fusion of Inertial and Magnetic Sensing Aided by a Permanent Magnet

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