Magnetic Induction-Based Positioning in Distorted Environments

Kypris, Orfeas; Abrudan, Traian E.; Markham, Andrew

doi:10.1109/tgrs.2016.2546461

Cited by 14 publications

(17 citation statements)

References 32 publications

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“…6 shows the results obtained with the same simulated priors as in Fig. 2 but with experimental MI data and using As described in [23], this may also be a consequence of distortions. Likewise, in similarity with the results presented in [3] and [15], the ML orientation estimates are better for the yaw angle than for the roll and pitch angles.…”

Section: A Position and Orientation Estimationmentioning

confidence: 97%

Sensor Fusion for Magneto-Inductive Navigation

et al. 2020

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Magneto-inductive navigation is an inexpensive and easily deployable solution to many of today's navigation problems. By utilizing very low frequency magnetic fields, magnetoinductive technology circumvents the problems with attenuation and multipath that often plague competing modalities. Using triaxial transmitter and receiver coils, it is possible to compute position and orientation estimates in three dimensions. However, in many situations, additional information is available that constrains the set of possible solutions. For example, the receiver may be known to be coplanar with the transmitter, or orientation information may be available from inertial sensors. We employ a maximum a posteriori estimator to fuse magnetoinductive signals with such complementary information. Further, we derive the Cramér-Rao bound for the position estimates and investigate the problem of detecting distortions caused by ferrous material. The performance of the estimator is compared to the Cramér-Rao bound and a state-of-the-art estimator using both simulations and real-world data. By fusing magneto-inductive signals with accelerometer measurements, the median position error is reduced almost by a factor of two.

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Section: A Position and Orientation Estimationmentioning

confidence: 97%

Sensor Fusion for Magneto-Inductive Navigation

et al. 2020

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“…Wherever indicated, random initialization refers to uniform sampling of an initial position within the room (and uniformly sampled orientation in the case of ML 5D ). 5 The true agent deployment is sampled the same way. Figures 6a and 6b show the error statistics and convergence speed, respectively, of the algorithms with a single initialization.…”

Section: Evaluation Of Estimation Performancementioning

confidence: 99%

“…Therefore, the near-field has been considered as physical layer for localization on the 10 m-scale in harsh propagation environments, e.g., underground [2] and indoor [3]- [6]. Most existing work considers tri-axial coil arrays [2]- [5] whose form factor and hardware complexity are however undesired for many applications. In contrast, we assume an unobtrusive setup consisting of planar coils, allowing for an integrated printed coil at the agent and anchor coils which can be flush-mounted on walls without obstructing any activities in the room.…”

Section: Introductionmentioning

confidence: 99%

Robust near-field 3D localization of an unaligned single-coil agent using unobtrusive anchors

Dumphart

Slottke

Wittneben

2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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The magnetic near-field provides a suitable means for indoor localization, due to its insensitivity to the environment and strong spatial gradients. We consider indoor localization setups consisting of flat coils, allowing for convenient integration of the agent coil into a mobile device (e.g., a smart phone or wristband) and flush mounting of the anchor coils to walls. In order to study such setups systematically, we first express the Cramér-Rao lower bound (CRLB) on the position error for unknown orientation and evaluate its distribution within a square room of variable size, using 15 × 10 cm anchor coils and a commercial NFC antenna at the agent. Thereby, we find cm-accuracy being achievable in a room of 10 × 10 × 3 meters with 12 flat wallmounted anchors and with 10 mW used for the generation of magnetic fields. Practically achieving such estimation performance is, however, difficult because of the nonconvex 5D likelihood function. To that end, we propose a fast and accurate weighted least squares (WLS) algorithm which is insensitive to initialization. This is enabled by effectively eliminating the orientation nuisance parameter in a rigorous fashion and scaling the individual anchor observations, leading to a smoothed 3D cost function. Using WLS estimates to initialize a maximum-likelihood (ML) solver yields accuracy near the theoretical limit in up to 98% of cases, thus enabling robust indoor localization with unobtrusive infrastructure, with a computational efficiency suitable for real-time processing.

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“…Range based positon estimation techniques depend on the radio propagation model, which converts the received signals to distance estimates. Trilateration, Multilateration, MinMax are range based position estimation techniques [7,8]. These techniques depend on the received signals, and modeling of the radio channels specific to the indoor environment which is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

“…In offline phase, first of all fingerprints of the environment are required which is a difficult task. Again if there is any kind of change in the indoor setup, this process needs to be updated [7,8]. In second phase of fingerprinting, which is a positon estimation process, the received signals are matched with the offline fingerprints, which result in an estimated positon.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Indoor Position Estimation using K-NN and MinMax

Subhan¹,

Ahmed²,

Haider³

et al. 2019

KSII TIIS

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Due to the rapid advancement in smart phones, numerous new specifications are developed for variety of applications ranging from health monitoring to navigations and tracking. The word indoor navigation means location identification, however, where GPS signals are not available, accurate indoor localization is a challenging task due to variation in the received signals which directly affect distance estimation process. This paper proposes a hybrid approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea behind this hybrid approach is to use Euclidian distance formulation for distance estimates instead of indoor radio channel modeling which is used to convert the received signal to distance estimates. Due to unpredictable behavior of the received signal, modeling indoor environment for distance estimates is a challenging task which ultimately results in distance estimation error and hence affects position estimation process. Our proposed idea is indoor position estimation technique using Bluetooth enabled smart phones which is independent of the radio channels. Experimental results conclude that, our proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach.

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Magnetic Induction-Based Positioning in Distorted Environments

Cited by 14 publications

References 32 publications

Sensor Fusion for Magneto-Inductive Navigation

Sensor Fusion for Magneto-Inductive Navigation

Robust near-field 3D localization of an unaligned single-coil agent using unobtrusive anchors

Hybrid Indoor Position Estimation using K-NN and MinMax

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