NLOS Detection using UWB Channel Impulse Responses and Convolutional Neural Networks

Stahlke, Maximilian; Kram, Sebastian; Mutschler, Christopher; Mahr, Thomas

doi:10.1109/icl-gnss49876.2020.9115498

Cited by 54 publications

(47 citation statements)

References 20 publications

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“…BSs with censored PL are assumed to be in NLOS. In practice, the LOS state needs to be detected [ 41 , 42 , 43 ]. One 28 GHz case is presented with 100%, 95%, 90%, 85%, and 80% LOS detection rates in the positioning phase.…”

Section: Positioning Simulation Resultsmentioning

confidence: 99%

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Karttunen

Valkama

Talvitie

2021

Sensors

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Positioning is considered one of the key features in various novel industry verticals in future radio systems. Since path loss (PL) or received signal strength-based measurements are widely available in the majority of wireless standards, PL-based positioning has an important role among positioning technologies. Conventionally, PL-based positioning has two phases—fitting a PL model to training data and positioning based on the link distance estimates. However, in both phases, the maximum measurable PL is limited by measurement noise. Such immeasurable samples are called censored PL data and such noisy data are commonly neglected in both the model fitting and in the positioning phase. In the case of censored PL, the loss is known to be above a known threshold level and that information can be used in model fitting and in the positioning phase. In this paper, we examine and propose how to use censored PL data in PL model-based positioning. Additionally, we demonstrate with several simulations the potential of the proposed approach for considerable improvements in positioning accuracy (23–57%) and improved robustness against PL model fitting errors.

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Section: Positioning Simulation Resultsmentioning

confidence: 99%

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Karttunen

Valkama

Talvitie

2021

Sensors

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“…• Model error mitigation [70], [71]: CIRs are used to classify propagation conditions like a missing line-ofsight (LoS) link or to estimate model errors caused by multipath components. The goal is to use CIRs to enhance classic positioning methods.…”

Section: A Track Descriptionmentioning

confidence: 99%

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Potortì

Torres-Sospedra²,

Quezada-Gaibor

et al. 2022

IEEE Sensors J.

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Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Footmounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

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“…A variety of different methods for exploiting additional information in CMs have been published. For instance, error mitigation (EMI) approaches rely on the correction of ranges by estimating systematic offset statistics with regression [8], [9] or classify adverse channel conditions [20]- [22]. Hence, they produce a correction indicator that can be used for the enhancement of LOS range estimates by offset mitigation, error statistic estimation, or the exclusion of unreliable LOS ranges.…”

Section: Related Workmentioning

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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NLOS Detection using UWB Channel Impulse Responses and Convolutional Neural Networks

Cited by 54 publications

References 20 publications

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

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