scholarly journals Multiple Simultaneous Ranging in IR-UWB Networks

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5415 ◽  
Author(s):  
Shashi Shah ◽  
Tanee Demeechai
Keyword(s):  
Indoor Localization ◽  
Location Based Services ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Network Congestion ◽  
Wireless Devices ◽  
Range Estimation ◽  
Message Exchange ◽  
Different Sources ◽  
Wireless Indoor Localization

Growth in the applications of wireless devices and the need for seamless solutions to location-based services has motivated extensive research efforts to address wireless indoor localization networks. Existing works provide range-based localization using ultra-wideband technology, focusing on reducing the inaccuracy in range estimation due to clock offsets between different devices. This is generally achieved via signal message exchange between devices, which can lead to network congestion when the number of users is large. To address the problem of range estimation with limited signal messages, this paper proposes multiple simultaneous ranging methods based on a property of time difference of reception of two packets transmitted from different sources in impulse-radio ultra-wideband (IR-UWB) networks. The proposed method maintains similar robustness to the clock offsets while significantly reducing the air time occupancy when compared with the best existing ranging methods. Experimental evaluation of ranging in a line-of-sight environment shows that the proposed method enables accurate ranging with minimal air time occupancy.

scholarly journals Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

2020 ◽  
Vol 10 (11) ◽  
pp. 3980 ◽  
Author(s):  
Cung Lian Sang ◽  
Bastian Steinhagen ◽  
Jonas Dominik Homburg ◽  
Michael Adams ◽  
Marc Hesse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Measurement Data ◽  
Ultra Wideband ◽  
Machine Learning Techniques ◽  
Line Of Sight ◽  
Support Vector ◽  
Case Scenario ◽  
Worst Case ◽  
Worst Case Scenario

In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn.

scholarly journals Indoor Smartphone Localization Based on LOS and NLOS Identification

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3987 ◽  
Author(s):  
Hyeon Jo ◽  
Seungku Kim
Keyword(s):  
Indoor Localization ◽  
Estimation Error ◽  
Distance Estimation ◽  
Location Based Services ◽  
Propagation Path ◽  
Line Of Sight ◽  
Arrival Angle ◽  
Accurate Localization ◽  
Communication Signal ◽  
Localization Scheme

Accurate localization technology is essential for providing location-based services. Global positioning system (GPS) is a typical localization technology that has been used in various fields. However, various indoor localization techniques are required because GPS signals cannot be received in indoor environments. Typical indoor localization methods use the time of arrival, angle of arrival, or the strength of the wireless communication signal to determine the location. In this paper, we propose an indoor localization scheme using signal strength that can be easily implemented in a smartphone. The proposed algorithm uses a trilateration method to estimate the position of the smartphone. The accuracy of the trilateration method depends on the distance estimation error. We first determine whether the propagation path is line-of-sight (LOS) or non-line-of-sight (NLOS), and distance estimation is performed accordingly. This LOS and NLOS identification method decreases the distance estimation error. The proposed algorithm is implemented as a smartphone application. The experimental results show that distance estimation error is significantly reduced, resulting in accurate localization.

scholarly journals Smartphone Sensor Based Indoor Positioning: Current Status, Opportunities, and Future Challenges

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 891
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Ultra Wideband ◽  
Current Status ◽  
Indoor Location ◽  
Use Of Data ◽  
Future Challenges ◽  
Smartphone Sensors

The last two decades have witnessed a rich variety of indoor positioning and localization research. Starting with Microsoft Research pioneering the fingerprint approach based RADAR, MIT’s Cricket, and then moving towards beacon-based localization are few among many others. In parallel, researchers looked into other appealing and promising technologies like radio frequency identification, ultra-wideband, infrared, and visible light-based systems. However, the proliferation of smartphones over the past few years revolutionized and reshaped indoor localization towards new horizons. The deployment of MEMS sensors in modern smartphones have initiated new opportunities and challenges for the industry and academia alike. Additionally, the demands and potential of location-based services compelled the researchers to look into more robust, accurate, smartphone deployable, and context-aware location sensing. This study presents a comprehensive review of the approaches that make use of data from one or more sensors to estimate the user’s indoor location. By analyzing the approaches leveraged on smartphone sensors, it discusses the associated challenges of such approaches and points out the areas that need considerable research to overcome their limitations.

scholarly journals Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

2020 ◽  
Author(s):  
Cung Lian Sang ◽  
Bastian Steinhagen ◽  
Jonas Dominik Homburg ◽  
Michael Adams ◽  
Marc Hesse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Measurement Data ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Support Vector ◽  
Case Scenario ◽  
Worst Case ◽  
Data Set ◽  
Worst Case Scenario

In Ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight~(LOS), non-line-of-sight~(NLOS), and multi-path (MP) conditions are important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS or MP). However, the major contributions in literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. Though, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the mentioned three classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental data-set. The data-set was collected in different conditions at different scenarios in indoor environments. Using the collected real measurement data, we compare three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results show that applying ML methods in UWB ranging systems are effective in identification of the above-mentioned three classes. In specific, the overall accuracy reaches up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it is 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we (will) provide the publicly accessible experimental research data discussed in this paper at PUB - Publications at Bielefeld University. The evaluations of the three classifiers are conducted using the open-source python machine learning library scikit-learn.

scholarly journals Feasibility of LoRa for Smart Home Indoor Localization

2021 ◽  
Vol 11 (1) ◽  
pp. 415
Author(s):  
Kyungki Kim ◽  
Sining Li ◽  
Milad Heydariaan ◽  
Nour Smaoui ◽  
Omprakash Gnawali ◽  
...  
Keyword(s):  
Low Power ◽  
Long Range ◽  
Radio Frequency Identification ◽  
Indoor Localization ◽  
Low Cost ◽  
Smart Homes ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Wireless Technologies ◽  
Better Than

With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization including WiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates.

scholarly journals A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

2019 ◽  
Vol 11 (5) ◽  
pp. 504 ◽  
Author(s):  
Yue Yu ◽  
Ruizhi Chen ◽  
Liang Chen ◽  
Guangyi Guo ◽  
Feng Ye ◽  
...  
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Line Of Sight ◽  
Multiple Sensors ◽  
Ranging Model

More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.

Sign in / Sign up

Export Citation Format

Share Document