Setting up an Ultra-Wideband positioning system using off-the-shelf components

Gunia, Marco; Protze, Florian; Joram, Niko; Ellinger, Frank

doi:10.1109/wpnc.2016.7822860

Cited by 14 publications

(7 citation statements)

References 8 publications

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“…The performance of the gradient-based ranging approach suffers from multipath effects. Moreover, for larger values of LOS distance, measurement errors have a stronger impact on the estimated range [9].…”

Section: Traditional Range Estimatorsmentioning

confidence: 99%

“…The Active Reflector (AR)-principle is a well-known method [7], [8] for ranging where two radio transceivers, called the Initiator and the Reflector, send and receive constant tone signals over multiple frequencies. Different range estimators, such as gradient-based [9], Fourier-transformbased [10] and super-resolution-based [6] approaches are then applied to extract the range information from multifrequency channel measurements. These range estimators model the channel impulse response (CIR) as a superposition of multipath components (MPCs) defined by their complex magnitudes, angles-of-arrival (AoAs), and ToFs.…”

Section: Introductionmentioning

confidence: 99%

“…The gradient-based approach utilizes the slope of CFR phase data, which depends on the LOS ToF, to estimate the range, as shown in [9]. However, in the presence of multipath, the gradient-based approach performs poorly as the phase information tends to over-estimate the range.…”

Section: Introductionmentioning

confidence: 99%

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Reduced-Complexity Decimeter-Level Bluetooth Ranging in Multipath Environments

et al. 2022

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In this paper, we investigate enhanced super-resolution range estimators with decimeter-level accuracy for multi-antenna and multipath Bluetooth systems. To enhance the traditional MUSIC range estimator for a two-way frequency-hopping Bluetooth channel model, we apply forward-backward averaging and bandwidth extrapolation using Burg's algorithm to improve ranging accuracy, which is limited by the used Bluetooth bandwidth and the quality of the estimated sample covariance matrix. For the multiantenna case, we compare the Summed Antenna Processing and Individual Antenna Processing methods to process multiple-antenna Bluetooth channel measurements and enhance the range estimation accuracy compared to the single-antenna case. In addition, we investigate a sparsity-aware range estimator which exploits the sparsity of Bluetooth channel impulse response and achieves comparable ranging accuracy to the enhanced MUSIC estimator but at a much lower computational complexity. We apply the greedy Orthogonal Matching Pursuit algorithm to heuristically solve the sparsity-constrained optimization problem for Bluetooth ranging. Furthermore, we evaluate the computational complexity of our investigated Bluetooth range estimators with two complexity-reduction techniques to further reduce the complexity of MUSIC range estimator. Moreover, we analyze the Cramer-Rao Lower Bound (CRLB) on unbiased range estimation using the frequency-hopping Bluetooth channel model and derive a new insightful CRLB expression for a two-path channel model. Finally, we evaluate the Root-Mean-Square Error and Empirical Cumulative Distribution Function performance of our investigated range estimators both on simulated and real-world Bluetooth data that we collected in line-of-sight (LOS) and non-line-of-sight (NLOS) multipath scenarios. Our proposed enhancements on the range estimators improved the ranging accuracy by 58% for our collected Bluetooth data.

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Section: Traditional Range Estimatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reduced-Complexity Decimeter-Level Bluetooth Ranging in Multipath Environments

et al. 2022

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“…1b. Here, the values of the capacitances 1 C and 2 C are changed due to the relocation of the mass as a result of external acceleration. It is worth mentioning again, that the values of accelerometers are referred to an inertial navigation system.…”

Section: Sensorsmentioning

confidence: 99%

“…For instance, a Wireless Local Area Network receiver in a smartphone could be localized by measuring the signal strength regarding multiple access points. Another example is an ultra-wideband radar system consisting of multiple base stations, which determine the distance to a mobile entity, respectively, by measuring the time of flight [1]. Another famous approach is FMCW (frequency modulated continuous wave) radar, where the distance is computed by applying frequency chirps and utilizing time difference of arrival [2].…”

Section: Introductionmentioning

confidence: 99%

Building up an Inertial Navigation System Using Standard Mobile Devices

Gunia¹,

Wu²,

Joram³

et al. 2017

JEE

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This paper presents the development of a PNS (Pedestrian Navigation System), which utilizes accelerometer, gyroscope and magnetometer data to enable accurate positioning. Therefore, the sensor basics as well as the mathematics regarding reference frames and coordinate transformations are introduced initially. Particular focus is given to quaternions, since they provide a performance-effective means to execute rotations. In great detail the two distinct approaches for a PNS are introduced, i.e. INS (inertial navigation systems) and PDR (pedestrian dead reckoning). For each, a comparison of state-of-the-art techniques is presented. Special attention is paid to orientation estimation and stance phase detection. Our system combines the most promising techniques and describes improvements, whose usefulness becomes obvious in our experiments. We have applied our PNS in five different test scenarios. For the most complex rectangular-shaped use case, we achieve on average error of 1.66% with regard to the total travelled distance, which is superior to other recent PNS utilizing comparable sensors.

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