A new map based method for NLOS mitigation in the UWB indoor localization system

Djaja-Jośko, Vitomir; Kołakowski, Marcin

doi:10.1109/telfor.2017.8249314

Cited by 19 publications

(6 citation statements)

References 13 publications

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“…Modeling of UWB ranging error is of great importance in order to compensate or mitigate biased ranging due to NLOS. Several UWB ranging error models that are available in the literature [25], [29], [30] focus on NLOS blocked by walls [29], fire door, wall [30] and whiteboard [25]. A few works build ranging error models with respect to human body obstruction.…”

Section: Related Workmentioning

confidence: 99%

Impact of Body Wearable Sensor Positions on UWB Ranging

et al. 2019

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Section: Related Workmentioning

confidence: 99%

Impact of Body Wearable Sensor Positions on UWB Ranging

et al. 2019

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“…In the literature, solutions for the detection and distinguishing of people from other objects, e.g., behind a wall, using a UWB radar [16] and the two-dimensional fast Fourier transform (FFT) [17], are also proposed. In addition, the detection of direct visibility conditions with a UWB radio interface in node-to-node communication was presented in [18][19][20], and, in [21], the authors investigated several other types of obstacles, such as wood, steel and the human body.…”

Section: Uwb Applicationsmentioning

confidence: 99%

User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning

Urwan,

Cwalina

2024

Sensors

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In this paper, the issue of detecting a user’s position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse response of the channel, a measurement stand consisting of EVB1000 devices and DW1000 radio modules was developed and indoor static measurement scenarios were performed. It was proven that for the binary classification of user orientation, neural networks achieved accuracy that was more than 9% higher than that for the well-known threshold method. In addition, the classification of user position angles relative to the reference node was analyzed. It was proven that, using the proposed deep learning approach and the channel impulse response, it was possible to estimate the angle of the user’s position in relation to the antenna geometry. Absolute user orientation angle errors of about 4–7° for convolutional neural networks and of about 14–15° for multilayer perceptrons were achieved in approximately 85% of the cases in both tested scenarios.

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“…Despite the fact that UWB-enabled nodes are able to measure distances up to 100 m with errors in the order of centimeters in ideal cases, the error may rise up to more than 1.5 m when the direct line-of-sight between UWB nodes is obstructed [ 19 ]. The non-line-of-sight (NLOS) condition between UWB nodes is the main source of distance measurement error and existing works have proposed several analytical models, including linear [ 20 ], polynomial [ 21 ], Gaussian [ 22 ] and univariate skew- t distributions [ 23 ], for error mitigation. Extensive UWB distance measurement campaigns and error profiling experiments are conducted in [ 14 ] and a distance measurement error model combining a Gaussian and a gamma distribution is proposed.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking

Tian

Wang

Salčić

2020

Sensors

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Information fusion combining inertial navigation and radio frequency (RF) technologies, is commonly applied in indoor positioning systems (IPSs) to obtain more accurate tracking results. The performance of the inertial navigation system (INS) subsystem is affected by sensor drift over time and the RF-based subsystem aims to correct the position estimate using a fusion filter. However, the inherent sensor drift is usually not corrected during fusion, which leads to increasingly erroneous estimates over a short period of time. Among the inertial sensor drifts, gyroscope drift has the most significant impact in determining the correct orientation and accurate tracking. A gyroscope drift correction approach is proposed in this study and is incorporated in an INS and ultra-wideband (UWB) fusion IPS where only distance measurements from UWB subsystem are used. The drift correction approach is based on turn detection to account for the fact that gyroscope drift is accumulated during a turn. Practical pedestrian tracking experiments are conducted to demonstrate the accuracy of the drift correction approach. With the gyroscope drift corrected, the fusion IPS is able to provide more accurate tracking performance and achieve up to 64.52% mean position error reduction when compared to the INS only tracking result.

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A new map based method for NLOS mitigation in the UWB indoor localization system

Cited by 19 publications

References 13 publications

Impact of Body Wearable Sensor Positions on UWB Ranging

Impact of Body Wearable Sensor Positions on UWB Ranging

User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking

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