Using SAGE on COTS UWB Signals for TOA Estimation and Body Shadowing Effect Quantification

Coene, Sander; Cock, Cedric De; Tanghe, Emmeric; David, Paul A.; Martens, Luc; Joseph, Wout

doi:10.1109/ipin51156.2021.9662465

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…For a fair comparison, we implement the same observation likelihood and dynamics of our pipeline. We consider this a feasible state-of-the-art approach, however there are also other methods for error mitigation, including body shadowing models [41] and TOF regression [42].…”

Section: Baseline Methodsmentioning

confidence: 99%

Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

Kram¹,

Kraus²,

Feigl³

et al. 2022

Preprint

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Recent localization frameworks exploit spatial information of complex channel measurements (CMs) to estimate accurate positions even in multipath propagation scenarios. Stateof-the art CM fingerprinting(FP)-based methods employ convolutional neural networks (CNN) to extract the spatial information. However, they need spatially dense data sets (associated with high acquisition and maintenance efforts) to work well -which is rarely the case in practical applications. If such data is not available (or its quality is low), we cannot compensate the performance degradation of CNN-based FP as they do not provide statistical position estimates, which prevents a fusion with other sources of information on the observation level.We propose a novel localization framework that adapts well to sparse datasets that only contain CMs of specific areas within the environment with strong multipath propagation. Our framework compresses CMs into informative features to unravel spatial information. It then regresses Gaussian processes (GPs) for each of them, which imply statistical observation models based on distance-dependent covariance kernels. Our framework combines the trained GPs with line-of-sight ranges and a dynamics model in a particle filter. Our measurements show that our approach outperforms state-of-the-art CNN fingerprinting (0.52 m vs. 1.3 m MAE) on spatially sparse data collected in a realistic industrial indoor environment.

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Section: Baseline Methodsmentioning

confidence: 99%

Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

Kram¹,

Kraus²,

Feigl³

et al. 2022

Preprint

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show abstract

“…This error is a direct result of the leading edge algorithm implemented on-chip failing to correctly and precisely identify the first path. This occurs due to number of factors including the received signal strength and used bandwidth (impacting the width of the first peak) [57], low SNR (< 6 dB) [58] and due to the presence of humans or other obstructions (NLOS) [59]. With ML, the goal is to identify this error (output data) using precise ground truth and the associated CIR data (input data).…”

Section: A Uwb ML Modelsmentioning

confidence: 99%

Transfer Learning for UWB Error Correction and (N)LOS Classification in Multiple Environments

Fontaine

Che

Shahid

et al. 2024

IEEE Internet Things J.

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Ultra Wideband (UWB) is a popular technology to address the need for high precision indoor positioning systems in challenging industry 4.0 use cases. In line-of-sight (LOS) environments, UWB positioning errors in the order of 1 − 10 cm can be achieved. However, in non-line-of-sight (NLOS) conditions, this precision drops significantly, with errors typically > 30 cm. Machine learning has been proposed to improve the precision in such NLOS conditions, but is typically environmentspecific and lacks generalization to new environments and UWB configurations. As such, it is necessary to collect large datasets to train a neural network for each new environment or UWB configuration. To remedy this, this paper proposes automatic optimizations for transfer learning (TL) deep neural networks towards new environments and UWB configurations. We analyze error correction and (non)-line-of-sight ((N)LOS) classification models, using either feature-or channel impulse response-based (CIR) input data. Our TL solutions show a 50% error improvement and 15% (N)LOS classification accuracy improvement (for both feature-and CIR-based approaches) compared to a model trained in a different environment. We also analyze the impact on TL using a limited number of samples (25 to 400 samples). The highest accuracy is typically achieved by the CIR-based approach, where with only 50 samples from the new mixed (N)LOS environment, we show ±10 cm precision after error correction with 93% (N)LOS detection. The presented results demonstrate high precision UWB localization (from 643 mm to 245 mm) through ML with minimal data collection effort in challenging NLOS environments.

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“…On the other hand, the lack of reflective surfaces in the outdoor experiment in [22] allows for creeping waves to be dominant for all φ > 90 • . Fourthly, range estimation algorithms perform differently under HBS conditions as reported in [32], which compared the leading-edge detection algorithm with the SAGE algorithm. Lastly, given the fact that the creeping waves are heavily attenuated for high φ values, it is suspected that the transmit power also affects the range error distribution.…”

Section: Effects Of Human Body Shadowing On Uwb Rangingmentioning

confidence: 99%

“…The biggest advantage of these machine learning and deep learning techniques is that they are parameterless, but they do require a substantial amount of training data. Also, a large part of experimental UWB-related research has been conducted with low-cost COTS UWB hardware, primarily using the Decawave DW1000 transceiver [32]. A disadvantage of the DW1000 is that reading the CIR from the device's serial port is time consuming, which makes NLoS mitigation challenging in a dynamic setting.…”

Section: Detection and Mitigation Of Human Body Shadowing Effectsmentioning

confidence: 99%

Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning

De Cock,

Tanghe,

Joseph

et al. 2023

Sensors

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Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body–tag–anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error.

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Using SAGE on COTS UWB Signals for TOA Estimation and Body Shadowing Effect Quantification

Cited by 5 publications

References 17 publications

Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

Transfer Learning for UWB Error Correction and (N)LOS Classification in Multiple Environments

Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning

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