Magnetic Field Analysis for 3-D Positioning Applications

Pasku, Valter; Angelis, Alessio De; Angelis, Guido De; Moschitta, Antonio; Carbone, Paolo

doi:10.1109/tim.2017.2682738

Cited by 42 publications

(20 citation statements)

References 34 publications

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“…Magnetic field sensing is an important task for many applications ranging, e.g., from positioning and navigation to electronic stability programs, electrical current sensors to biomagnetic field detection 1 – 3 . Naturally, the requirements differ and depend on the specific application.…”

Section: Introductionmentioning

confidence: 99%

Wide Band Low Noise Love Wave Magnetic Field Sensor System

Kittmann

Durdaut

Zabel

et al. 2018

Sci Rep

102

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We present a comprehensive study of a magnetic sensor system that benefits from a new technique to substantially increase the magnetoelastic coupling of surface acoustic waves (SAW). The device uses shear horizontal acoustic surface waves that are guided by a fused silica layer with an amorphous magnetostrictive FeCoSiB thin film on top. The velocity of these so-called Love waves follows the magnetoelastically-induced changes of the shear modulus according to the magnetic field present. The SAW sensor is operated in a delay line configuration at approximately 150 MHz and translates the magnetic field to a time delay and a related phase shift. The fundamentals of this sensor concept are motivated by magnetic and mechanical simulations. They are experimentally verified using customized low-noise readout electronics. With an extremely low magnetic noise level of ≈100 pT/, a bandwidth of 50 kHz and a dynamic range of 120 dB, this magnetic field sensor system shows outstanding characteristics. A range of additional measures to further increase the sensitivity are investigated with simulations.

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Section: Introductionmentioning

confidence: 99%

Wide Band Low Noise Love Wave Magnetic Field Sensor System

Kittmann

Durdaut

Zabel

et al. 2018

Sci Rep

102

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“…The sensor model estimation of sensor η is executed by recording N k measurement points with different known TX poses. Here, random TX poses to support the assumption of uncorrelated Gaussian noise, as in (31). The field sensors can be calibrated independently of each other, as in (35), the corresponding errors are summed up.…”

Section: Sensor Model Estimationmentioning

confidence: 99%

“…Various ac field-based systems with different system topologies and algorithms were proposed in previous works, see [3] for an overview of existing systems and their performance. Generally, the systems either utilize one-axis coils [23]- [27] or three-axis coils [21], [22], [28]- [31] to detect the magnetic field. On the one hand, the use of three-axis sensors maximizes the information generated at each measurement point by measuring each dimension of the magnetic field.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, a variety of imperfections such as coupling, coil misalignment, field sensor rotation, amplitude, and phase errors caused by the analog circuits and signal processing, and unsynchronized sampling instances, degrade the localization performance. These imperfections represent the main challenge of current magnetic localization systems, see [31]. Hence, a corresponding calibration and localization scheme is necessary in order to improve the performance and increase the reliability of magnetic positioning systems.…”

Section: Introductionmentioning

confidence: 99%

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High-Accuracy Localization and Calibration for 5-DoF Indoor Magnetic Positioning Systems

Hehn

Sippel

Carlowitz

et al. 2019

IEEE Trans. Instrum. Meas.

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Magnetic local positioning systems are a well-suited candidate for reliable indoor positioning systems, as they are robust against blocking by dielectric materials like walls or people. The system presented in this paper is implemented with a one-axis magnetic transmitter and several three-axis field sensors connected to a complete sensor network. Unfortunately, the performance of the system is severely impaired by field sensor nonidealities such as magnetic coupling of the sensor coils, coil misalignment, field sensor rotation, and unsynchronized sampling. In this paper, the overall field sensor impairments and an additive Gaussian noise model superposing the magnetic field are mathematically described. Then, a novel calibration scheme for the overall field sensor nonidealities is presented. Furthermore, a statistically optimal localization procedure coping with the field sensor nonidealities is developed. The proposed novel localization and calibration algorithms are demonstrated in a common office environment with a size of 7 m × 5 m × 3 m. Thereby, the calibration impressively reduces the position rootmean-square error (RMSE) from 46.8 to 10.6 cm and the angle RMSE from 24.8 • to 6.1 • .

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“…The Earth’s magnetic field has been considered for indoor positioning in many studies [1,24,25,26,27]. However, several excellent reviews have commented that the magnitude of magnetism is ambiguous in large indoor spaces or non-steel structure buildings [28,29,30,31]. …”

Section: Introductionmentioning

confidence: 99%

An Infrastructure-Free Indoor Localization Algorithm for Smartphones

Wang

Luo

Men

et al. 2018

Sensors

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Accurate indoor positioning technology provides location-based service for a variety of applications. However, most existing indoor localization approaches (e.g., Wi-Fi and Bluetooth-based methods) rely heavily on positioning infrastructure, which prevents their large-scale deployment and limits the range at which they are applicable. Here, we proposed an infrastructure-free indoor positioning and tracking approach, termed LiMag, which used ubiquitous magnetic field and ambient lights (e.g., fluorescent, incandescent, and light-emitting diodes (LEDs)) without containing modulated information. We conducted an in-depth study on both the advantages and the challenges in leveraging magnetic field and ambient light intensity for indoor localization. Based on the insights from this study, we established a hybrid observation model that took full advantage of both the magnetic field and ambient light signals. To address the low discernibility of the hybrid observation model, LiMag first generated a single-step fingerprint model by vectorizing consecutive hybrid observations within each step. In order to accurately track users, a lightweight single-step tracking algorithm based on the single-step fingerprints and the particle filter framework was designed. LiMag leveraged the walking information of users and several single-step fingerprints to generate long trajectory fingerprints that exhibited much higher location differentiation ability than the single-step fingerprint. To accelerate particle convergence and eliminate the accumulative error of single-step tracking algorithm, a long trajectory calibration scheme based on long trajectory fingerprints was also introduced. An undirected weighted graph model was constructed to decrease the computational overhead resulting from this long trajectory matching. In addition to typical indoor scenarios including offices, shopping malls and parking lots, we also conducted experiments in more challenging scenarios, including large open-plan areas as well as environments characterized by strong sunlight. Our proposed algorithm achieved a 75th percentile localization accuracy of 1.8 m and 2.2 m, respectively, in the office and shopping mall tested. In conclusion, our LiMag algorithm provided location-based service of infrastructure-free with significantly improved localization accuracy and coverage, as well as satisfactory robustness inside complex indoor environments.

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Magnetic Field Analysis for 3-D Positioning Applications

Cited by 42 publications

References 34 publications

Wide Band Low Noise Love Wave Magnetic Field Sensor System

Wide Band Low Noise Love Wave Magnetic Field Sensor System

High-Accuracy Localization and Calibration for 5-DoF Indoor Magnetic Positioning Systems

An Infrastructure-Free Indoor Localization Algorithm for Smartphones

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