A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments

Yu, Kegen; Wen, Kai; Li, Yingbing; Zhang, Shuai; Zhang, Kefei

doi:10.1109/tvt.2018.2883810

Cited by 282 publications

(157 citation statements)

References 39 publications

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Section: Uwb-based Positioningmentioning

confidence: 99%

“…Since non-line-of-sight (NLOS) measurements are quite frequent in indoor environments, several recent works consider the problem of identifying NLOS measurements [20] or dynamically adapting the measurement variance in the EKF in order to reduce the effect of outliers [21]. Recent feature-based approaches provided encouraging results on the NLOS identification and mitigation by properly analyzing the characteristics of the received UWB signal [19,22]. Machine learning approaches proved to be well suited for identifying NLOS measurements as well, while they currently do not seem to provide significant improvements for NLOS effects mitigation [23,24].…”

Section: Uwb-based Positioningmentioning

confidence: 99%

“…In the typical case of a moving device to be tracked, the positioning problem can be conveniently coped with a proper model of the device dynamic and an Extended Kalman Filter (EKF). The accuracy of the obtained position estimates depends on the geometry of the network nodes [18], and it can be assessed by means of the geometric dilution of precision [19]. Several commercial and research positioning systems, e.g., the Pozyx system, are using a fixed UWB network architecture in order to properly track moving nodes [17].Since non-line-of-sight (NLOS) measurements are quite frequent in indoor environments, several recent works consider the problem of identifying NLOS measurements [20] or dynamically adapting the measurement variance in the EKF in order to reduce the effect of outliers [21].…”

mentioning

confidence: 99%

“…Several commercial and research positioning systems, e.g., the Pozyx system, are using a fixed UWB network architecture in order to properly track moving nodes [17].Since non-line-of-sight (NLOS) measurements are quite frequent in indoor environments, several recent works consider the problem of identifying NLOS measurements [20] or dynamically adapting the measurement variance in the EKF in order to reduce the effect of outliers [21]. Recent feature-based approaches provided encouraging results on the NLOS identification and mitigation by properly analyzing the characteristics of the received UWB signal [19,22]. Machine learning approaches proved to be well suited for identifying NLOS measurements as well, while they currently do not seem to provide significant improvements for NLOS effects mitigation [23,24].The integration with the information provided by other sensors can be considered to mitigate the UWB measurement error and regularize the estimated trajectory, e.g., pedestrian dead reckoning based on the inertial sensor measurements [25].…”

mentioning

confidence: 99%

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LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

Sakr

Masiero

El‐Sheimy

2019

Sensors

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This paper presents LocSpeck, a collaborative and distributed indoor positioning system for dynamic nodes connected using an ad-hoc network, based on inter-node relative range measurements and Wi-Fi fingerprinting. The proposed system operates using peer-to-peer range measurements and does not need ultra-wideband (UWB) fixed anchor, nor it needs a predefined network topology. The nodes could be asymmetric in terms of the available sensors onboard, the computational resources, and the power capacity. This asymmetry adversely affects the positioning performance of the weaker nodes. Collaboration between different nodes is achieved through a distributed estimator without the need of a single centralized computing element. The ranging measurement component of the system is based on the DW1000 UWB transceiver chip from Decawave, which is attached to a set of smartphones equipped with asymmetric sensors. The distributed positioning filter fuses, locally on each node, the relative range measurements, the reading from the internal sensors, and the Wi-Fi received signal strength indicator (RSSI) readings to obtain an estimate of the position of each node. The described system does not depend on fixed UWB anchors and supports online addition and removal of nodes and dynamic node role assignment, either as an anchor or as a rover. The performance of the system is evaluated by real-world test scenarios using a set of four smartphones navigating an indoor environment on foot. The performance is compared to that of a commercial UWB-based system. The results presented in this paper show that weak mobile nodes, in terms of available positioning sensors, can benefit from collaboration with other nearby nodes.Sensors 2020, 20, 78 2 of 28 using relative measurements can augment the stand-alone observations of each node and improve the positioning accuracy of the ensemble [2,3]. The use of relative range measurements introduces an additional constraint to the position estimation filter, which can improve the positioning accuracy of the collaborating nodes [4]. Ultra-wideband (UWB) ranging devices are more immune to multipath errors because it can distinguish between different events with a precise temporal resolution, thanks to its large channel bandwidth. This makes the UWB-based devices capable of achieving centimeter-level ranging accuracy in ideal operating conditions [5].The objective of this paper is to present LocSpeck, a collaborative and distributed positioning system targeting smartphones and handheld applications, which uses UWB-based relative range measurements, along with Wi-Fi fingerprinting and inertial sensors. The system is evaluated experimentally, and the results are compared to the performance of Pozyx, a commercial UWB-based positioning system. The rest of this section will provide a brief overview of different ranging and positioning techniques used in UWB-based systems, and then it will discuss the different network architectures used for range-based positioning applications. UWB-Based RangingSensors 2020, 20,...

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Section: Uwb-based Positioningmentioning

confidence: 99%

Section: Uwb-based Positioningmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

Sakr

Masiero

El‐Sheimy

2019

Sensors

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“…Then, they used the CNN model to identify and mitigate the NLoS propagation. Indoor propagation channels can be divided into multiple categories so that the channel identification results can be used to evaluate how serious the NLOS effect is [20]. UWB signal attenuation is very serious in the case of NLoS propagation, and the time delay caused by NLoS will cause a large error in high-precision localization, both of which can be features for identifying and mitigating the NLoS [21][22][23].However, all the solutions above for NLoS propagation in harsh environments have two drawbacks.…”

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confidence: 99%

A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments

Tong

Ning

et al. 2019

Sensors

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Wireless sensor networks (WSNs) and the Internet of Things (IoT) have been widely used in industrial, construction, and other fields. In recent years, demands for pedestrian localization have been increasing rapidly. In most cases, these applications work in harsh indoor environments, which have posed many challenges in achieving high-precision localization. Ultra-wide band (UWB)-based localization systems and pedestrian dead reckoning (PDR) algorithms are popular. However, both have their own advantages and disadvantages, and both exhibit a poor performance in harsh environments. UWB-based localization algorithms can be seriously interfered by non-line-of-sight (NLoS) propagation, and PDR algorithms display a cumulative error. For ensuring the accuracy of indoor localization in harsh environments, a hybrid localization approach is proposed in this paper. Firstly, UWB signals cannot penetrate obstacles in most cases, and traditional algorithms for improving the accuracy by NLoS identification and mitigation cannot work in this situation. Therefore, in this study, we focus on integrating a PDR and UWB-based localization algorithm according to the UWB communication status. Secondly, we propose an adaptive PDR algorithm. UWB technology can provide high-precision location results in line-of-sight (LoS) propagation. Based on these, we can train the parameters of the PDR algorithm for every pedestrian, to improve the accuracy. Finally, we implement this hybrid localization approach in a hardware platform and experiment with it in an environment similar to industry or construction. The experimental results show a better accuracy than traditional UWB and PDR approaches in harsh environments.Sensors 2020, 20, 193 2 of 20 most important feature of harsh environments. Indoor localization technology is still very challenging under NLoS propagation in harsh environments.The current mainstream indoor localization systems are radio frequency (RF)-based. This kind of system basically requires beacon nodes, or beacon nodes (BNs), with known coordinates in the localization system. Each target to be located has an electronic tag, or tag node (TN). Specific features generated from the communication between the TN and BN are used for localization. These features mainly include the radio signal strength (RSS), time, signal angle, etc. The challenge of indoor localization systems in harsh environments is that these features can be severely disturbed [1] by NLoS propagation or other interference. As a result, the localization accuracy will be greatly reduced.In harsh environments, interference of an RF-based localization system can be mainly divided into three types. Firstly, multipath refraction is a common type of interference in harsh environments. It means that an RF signal will be transmitted to the receiver through multiple paths, which causes the transmission time to be unreliable and unpredictable RSS superposition. Additionally, it apparently has a greater impact on the signal angle. Secondly, non-line-of-sight (NLoS) propagati...

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CS-Dict: Accurate Indoor Localization with CSI Selective Amplitude and Phase Based Regularized Dictionary Learning

Jiang

Zhao

et al. 2020

Algorithms and Architectures for Parallel Processing

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A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments

Cited by 282 publications

References 39 publications

LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments

CS-Dict: Accurate Indoor Localization with CSI Selective Amplitude and Phase Based Regularized Dictionary Learning

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