A UWB-Based Indoor Positioning System Employing Neural Networks (original) (raw)
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NLOS Ranging Mitigation with Neural Network Model for UWB Localization
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), 2022
Localization of robots is vital for navigation and path planning, such as in cases where a map of the environment is needed. Ultra-Wideband (UWB) for indoor location systems has been gaining popularity over the years with the introduction of low-cost UWB modules providing centimetre-level accuracy. However, in the presence of obstacles in the environment, Non-Line-Of-Sight (NLOS) measurements from the UWB will produce inaccurate results. As low-cost UWB devices do not provide channel information, we propose an approach to decide if a measurement is within Line-Of-Sight (LOS) or not by using some signal strength information provided by low-cost UWB modules through a Neural Network (NN) model. The result of this model is the probability of a ranging measurement being LOS which was used for localization through the Weighted-Least-Square (WLS) method. Our approach improves localization accuracy by 16.93% on the lobby testing data and 27.97% on the corridor testing data using the NN model trained with all extracted inputs from the office training data.
Neural Networks for Fingerprinting-Based Indoor Localization Using Ultra-Wideband
Journal of Communications, 2009
AbstractͶThis paper discusses the use of neural networks in an underground radio-localization system. In a highly aggressive environment such as mines, reliability and robustness are essential to any operational system. Using UWB as the physical wireless propagation medium and combined with fingerprinting-geolocation and neural networks, this work tends to overcome many of the problems encountered in indoor environments. Full description of the system and the adopted approach will help accentuate the role of neural networks in improving the overall performance. Moreover a comparison between MLP and RBF performance is presented, providing a clear evidence of the role and importance of the neural networks in offering good accuracy and precision to the final system.
Accurate and robust indoor localization systems using ultra-wideband signals
Indoor localization systems that are accurate and robust with respect to propagation channel conditions are still a technical challenge today. In particular, for systems based on range measurements from radio signals, non-line-of-sight (NLOS) situations can result in large position errors. In this paper, we address these issues using measurements in a representative indoor environment. Results show that conventional tracking schemes using high-and a low-complexity ranging algorithms are strongly impaired by NLOS conditions unless a very large signal bandwidth is used. Furthermore, we discuss and evaluate the performance of multipath-assisted indoor navigation and tracking (MINT), that can overcome these impairments by making use of multipath propagation. Across a wide range of bandwidths, MINT shows superior performance compared to conventional schemes, and virtually no degradation in its robustness due to NLOS conditions.
Indoor Navigation and Mapping: Performance Analysis of Uwb-Based Platform Positioning
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
The increasing demand for reliable indoor navigation systems is leading the research community to investigate various approaches to obtain effective solutions usable with mobile devices. Among the recently proposed strategies, Ultra-Wide Band (UWB) positioning systems are worth to be mentioned because of their good performance in a wide range of operating conditions. However, such performance can be significantly degraded by large UWB range errors; mostly, due to non-line-of-sight (NLOS) measurements. This paper considers the integration of UWB with vision to support navigation and mapping applications. In particular, this work compares positioning results obtained with a simultaneous localization and mapping (SLAM) algorithm, exploiting a standard and a Time-of-Flight (ToF) camera, with those obtained with UWB, and then with the integration of UWB and vision. For the latter, a deep learning-based recognition approach was developed to detect UWB devices in camera frames. Such information is both introduced in the navigation algorithm and used to detect NLOS UWB measurements. The integration of this information allowed a 20% positioning error reduction in this case study.
UWB Indoor Localization Using Deep Learning LSTM Networks
Applied Sciences, 2020
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 posit...
Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances
Sensors, 2016
In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.
An Accurate Indoor Localization Approach Using UWB System
2020
Localization using ultra-wide band (UWB) signals gives accurate user position results for indoor localization. In UWB based indoor localization, the system transmits the UWB pulses from UWB tag to anchors. Accurate UWB pulse transmission in the UWB system determines the system’s localization performance. The indoor channel conditions, multipath effects and UWB signal blockage reduce the smooth transmission of the UWB pulses and affects the localization performance of the UWB system. The conventional UWB based localization systems use Gaussian pulse as the UWB pulse and these pulses are easily influenced by the indoor channel conditions. To overcome the localization challenges of the UWB system, we propose an indoor localization approach using second derivative of Gaussian pulses. The proposed second derivative of the Gaussian pulse-based approach reduces the ToA and localization errors and improves the localization performance of the UWB system. The simulation results show that the ...
IEEE Access
Logistics is an important driver for the competitiveness of industries and material supply. The development of smart logistics, powered by precise positioning and communication technologies can significantly improve the efficiency of logistics. The emerging technology of ultra-wideband (UWB) precision positioning has attracted significant attention throughout the previous decade owing to its promising capabilities over other radio frequency-based indoor localisation systems. In addition, UWB is characterised by large bandwidth and data rate, short message length, low transmission power and high penetration capability, which are all favourable for indoor positioning applications. However, UWB localisation technology faces several challenges that are somewhat similar to other technologies, such as mitigating errors that originate from non-line-of-sight (NLOS) situations and tackling signal interference in dense environments, and when required to operate in extreme conditions. This paper reviews the most recent advances made in UWB positioning systems over the last five years, with a focus on high-ranking articles. In addition to going through more conventional solutions to UWB challenges, modern solutions, which involve the use of machine learning and sensor data fusion, are discussed. We highlight the most promising findings of the recently implemented and foreseen UWB positioning systems by providing a summary of each reviewed article. Additionally, we address a major challenge that faces the UWB positioning technology: NLOS situations, focusing on some proposed remedies such as multi-sensor fusion and machine learning. As an application, this study introduces how UWB technology promotes smart logistics by offering indoor positioning to improve efficiencies in the delivery of goods from the source to the customer. Furthermore, it demonstrates the benefits of UWB technology for accurate positioning and tracking of both stationary and moving items, and machinery in an indoor logistics environment. INDEX TERMS Ultra-wideband (UWB), indoor positioning systems (IPS), smart logistics, navigation and localisation, machine learning, sensor fusion.
Improving Indoor Localization Using Mobile UWB Sensor and Deep Neural Networks
IEEE Access, 2022
Accurate localization in indoor environments with ultra-wideband (UWB) technology has long attracted much attention. However, due to the presence of multipath components or non-line of sight (NLOS) propagation of the radio signals, it has been converted to a critical challenge. Existing solutions use many fixed anchors in the indoor environment. Particularly, large areas require many anchor points and in the case of unexpected events that lead to the destruction of existing infrastructures, the fixed anchor points cannot be used. In this paper, a novel localization framework based on the transmitting signal from a mobile UWB sensor on the outside of the building and its received signal regarding the modified Saleh Valenzuela (SV) channel model is presented. After preprocessing the received signals, two new procedures to reduce the ranging error caused by multipath components are proposed. In the first procedure, two machine learning algorithms including multi-layer perceptron (MLP) and support vector machine (SVM) using the extracted features from the received UWB signal time and power vectors are implemented. Moreover, in the second procedure, two deep learning algorithms including MLP and convolutional neural networks (CNNs) using the received UWB signal time and power vectors are implemented to improve the performance of the indoor localization system. The simulation results show that the architecture designed for the convolutional neural network based on the hybrid dataset (the combination of the dataset related to received UWB signal time and power vectors) provides a mean absolute error (MAE) of about 3 cm.
Indoor Positioning System Based on the Ultra Wide Band for Transport Applications
InTech eBooks, 2012
Outdoor positioning can be improved with the start-up of Galileo. So, the accuracy will usually be in the order of a few metres. With Galileo services, the users needs require the same performances in outdoor and indoor applications. This is more obviously true in the construction, hypermarket, museums, where location awareness can become a crucial parameter for value-added services, especially for Galileo-GPS services. Positioning in difficult environments, especially in indoor, represents a current limitation for localisation systems (GPS/Galileo). In fact, indoor positioning faces additional difficulties as compared to outdoor positioning. Attenuation and multipath reflections of the line-of-sight (LOS) signal (or direct path) by the walls, floors, and ceiling of a tunnel are the main factors preventing typical GPS receivers from functioning indoors. Most of the time, the sum of multipath signals is stronger than the direct path signal, thereby preventing the receiver from accurately calculating the time of arrival [1]. The multipath signal distorts the cross correlation function peak, as detected by a receiver. The scientific and industrial community, especially in transport applications, considers that it is important to provide a positioning function in indoor (tunnel, station...) with a good performance of about a few centimetre. So, in order to reach this performance level, different techniques are under development, such as Ultra Wide Band technology. The UWB promises to overcome the power consumption and accuracy limitations of both the GPS and WLAN, and is more suitable for indoor location-based applications. In fact, Ultra Wide Band (UWB) technology provides high accuracy positioning in the multipath and confined environments typically found inside buildings. Integration of UWB with GPS or Galileo can provide a seamless transition from outdoor to indoor position and vice-versa. The ranging accuracy expected from UWB systems should be better than 0.5m in severe multipath environments [2, 3]. This chapter focuses on the indoor positioning system using the Ultra Wide Band, especially for transport applications. The first section is dedicated to introducing the indoor positioning application [3]. After this introduction, we present a brief review of some relevant work [7-9]. In indoor positioning system especially for transport applications two scenarios are considered: the self localisation and server-based localisation