Non-Line-of-Sight Identification for UWB Indoor Positioning Systems using Support Vector Machines

Kristensen, Jeppe Bro; Ginard, Michel Massanet; Jensen, Ole Kiel; Shen, Ming

doi:10.1109/ieee-iws.2019.8804072

Cited by 30 publications

(27 citation statements)

References 4 publications

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“…The results from Reference [17] show that the proposed approach reduces the localization error and improves the computational efficiency when compared to k-nearest neighbor (kNN), back propagation neural network (BPNN) and support vector machine (SVM) based methods. An SVM-based localization approach which classify the UWB NLOS conditions for indoor localization is presented in Reference [18]. The SVM model in Reference [18] reached 92% average identification accuracy for NLOS conditions and improved the UWB position accuracy in multipath conditions.…”

Section: Related Workmentioning

confidence: 99%

“…An SVM-based localization approach which classify the UWB NLOS conditions for indoor localization is presented in Reference [18]. The SVM model in Reference [18] reached 92% average identification accuracy for NLOS conditions and improved the UWB position accuracy in multipath conditions. To improve the localization accuracy of the UWB system during NLOS conditions, A two stage SVM classification for UWB LOS/NLOS channel conditions is discussed in Reference [19].…”

Section: Related Workmentioning

confidence: 99%

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UWB Indoor Localization Using Deep Learning LSTM Networks

2020

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Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

UWB Indoor Localization Using Deep Learning LSTM Networks

2020

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“…A 87% detection accuracy is achieved in complex factory environments using three features. In [20] the authors propose a Support-vector machine (SVM) approach and achieve 92% classification accuracy in an anechoic chamber and near 100% accuracy in corridor scenarios. In order to identify which features are most important, the authors of [21] applied genetic algorithms and performed LOS/NLOS detection using SVM.…”

Section: Lowmentioning

confidence: 99%

Edge Inference for UWB Ranging Error Correction Using Autoencoders

et al. 2020

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Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU.

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Section: Uwb-based Positioningmentioning

confidence: 99%

“…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]. Furthermore, a cooperative positioning approach can also be considered when the position of multiple devices has to be simultaneously estimated [26].…”

mentioning

confidence: 99%

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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Non-Line-of-Sight Identification for UWB Indoor Positioning Systems using Support Vector Machines

Cited by 30 publications

References 4 publications

UWB Indoor Localization Using Deep Learning LSTM Networks

UWB Indoor Localization Using Deep Learning LSTM Networks

Edge Inference for UWB Ranging Error Correction Using Autoencoders

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

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