Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

Sousa, Marcelo N. de; Thomä, Reiner S.

doi:10.3390/s18114073

Cited by 26 publications

(8 citation statements)

References 42 publications

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“…During calculation of the geolocation and emitter tracking in the urban area, an NLOS signal should be taken into consideration. For example, if we determine the emitter location as NLOS by TDOA or FDOA measurements, it will be directed to highly bias filtering [34][35][36]. Geolocation and target tracking algorithms may be affected by NLOS error, which may produce very high errors in geolocation and target estimation.…”

Section: Urban Environment Characteristicsmentioning

confidence: 99%

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A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

et al. 2021

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A single Radio-Frequency Interference (RFI) is a disturbance source of modern wireless systems depending on Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom). In particular, significant applications such as aeronautics and satellite communication can be severely affected by intentional and unintentional interference, which are unmitigated. The matter requires finding a radical and effective solution to overcome this problem. The methods used for overcoming the RFI include interference detection, interference classification, interference geolocation, tracking and interference mitigation. RFI source geolocation and tracking methodology gained universal attention from numerous researchers, specialists, and scientists. In the last decade, various conventional techniques and algorithms have been adopted in geolocation and target tracking in civil and military operations. Previous conventional techniques did not address the challenges and demand for novel algorithms. Hence there is a necessity for focussing on the issues associated with this. This survey introduces a review of various conventional geolocation techniques, current orientations, and state-of-the-art techniques and highlights some approaches and algorithms employed in wireless and satellite systems for geolocation and target tracking that may be extremely beneficial. In addition, a comparison between different conventional geolocation techniques has been revealed, and the comparisons between various approaches and algorithms of geolocation and target tracking have been addressed, including H∞ and Kalman Filtering versions that have been implemented and investigated by authors.

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Section: Urban Environment Characteristicsmentioning

confidence: 99%

“…In addition, some drawbacks occur in the TDOA method. Firstly, it is hard to achieve synchronization accuracy between all emitters and sensors, in addition to the synchronization error, which can easily translate into 300 meters of range error [36,45].…”

Section: Time Difference Of Arrival (Tdoa)mentioning

confidence: 99%

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

et al. 2021

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“…To address these issues jointly (i.e., FP observation likelihood modeling, spatially sparse datasets, and compact CM representation), we propose Gaussian process regression (GPR) of features extracted from CM using an autoencoder (AE) neural network [15] and model-based propagation-related features [8], [12], [16] to generate a statistical observation likelihood model. Unlike state-of-the-art FP methods based on deep (convolutional) neural networks (CNNs) [11], [14], [17], GPR requires less labeled data. Furthermore, it estimates an obser-This work has been submitted to the IEEE for possible publication.…”

Section: Introductionmentioning

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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“…For localization in wireless sensor networks, NLOS identification and localization algorithms based on the residual analysis [ 14 ] or data association [ 15 ] have been proposed. The localization method was proposed using the multipath fingerprints produced by ray tracing and machine learning [ 16 ] or constrained L1-norm minimization method [ 17 ]. Furthermore, the simulator designed for the UWB positioning in LOS/NLOS environments has been developed [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

Hyun

Myung

2021

Sensors

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Recently, technology utilizing ultra-wideband (UWB) sensors for robot localization in an indoor environment where the global navigation satellite system (GNSS) cannot be used has begun to be actively studied. UWB-based positioning has the advantage of being able to work even in an environment lacking feature points, which is a limitation of positioning using existing vision- or LiDAR-based sensing. However, UWB-based positioning requires the pre-installation of UWB anchors and the precise location of coordinates. In addition, when using a sensor that measures only the one-dimensional distance between the UWB anchor and the tag, there is a limitation whereby the position of the robot is solved but the orientation cannot be acquired. To overcome this, a framework based on an interacting multiple model (IMM) filter that tightly integrates an inertial measurement unit (IMU) sensor and a UWB sensor is proposed in this paper. However, UWB-based distance measurement introduces large errors in multipath environments with obstacles or walls between the anchor and the tag, which degrades positioning performance. Therefore, we propose a non-line-of-sight (NLOS) robust UWB ranging model to improve the pose estimation performance. Finally, the localization performance of the proposed framework is verified through experiments in real indoor environments.

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Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

Cited by 26 publications

References 42 publications

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

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