Pub Date : 2023-12-05DOI: 10.1109/JISPIN.2023.3339602
Risang Yudanto;Jianqiao Cheng;Erik Hostens;Miel Van der Wilt;Mats Vande Cavey
We show how the state of use of ultra-wideband (UWB) system is improved by removing systematic errors (bias) on device-level to improve accuracy and apply simple procedure to automate calibration process on the system-level to reduce manual efforts. On device-level, we discern the different sources of bias and establish a method that determines their values, for specific hardware and for individual devices. Our comprehensive approach includes simple, easy-to-implement methodologies for compensating these biases, resulting in a significant improvement in ranging accuracy. The mean ranging error has been reduced from 0.15 to 0.007 m, and the three-sigma error margin has decreased from 0.277 to approximately 0.103 m. To demonstrate this, a dedicated test setup was built. On system-level, we developed a method that avoids measuring all anchor positions one by one by exploiting increased redundancy from anchor-to-anchor and anchor-to-tag ranges, and automatically calculating the anchors topology (relative positions between each other). Nonlinear least squares provides the maximum likelihood estimate of the anchor positions and their uncertainty. This approach not only refines the accuracy of tag localization but also offers a predictive measure of its uncertainty, giving users a clearer understanding of the system's capabilities in real-world scenarios. This system-level enhancement is further complemented by the integration of a ranging protocol called automatic UWB ranging any-to-any, which offers additional layers of flexibility, reliability, and ease of deployment to the UWB localization process.
{"title":"Ultra-Wideband Localization: Advancements in Device and System Calibration for Enhanced Accuracy and Flexibility","authors":"Risang Yudanto;Jianqiao Cheng;Erik Hostens;Miel Van der Wilt;Mats Vande Cavey","doi":"10.1109/JISPIN.2023.3339602","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3339602","url":null,"abstract":"We show how the state of use of ultra-wideband (UWB) system is improved by removing systematic errors (bias) on device-level to improve accuracy and apply simple procedure to automate calibration process on the system-level to reduce manual efforts. On device-level, we discern the different sources of bias and establish a method that determines their values, for specific hardware and for individual devices. Our comprehensive approach includes simple, easy-to-implement methodologies for compensating these biases, resulting in a significant improvement in ranging accuracy. The mean ranging error has been reduced from 0.15 to 0.007 m, and the three-sigma error margin has decreased from 0.277 to approximately 0.103 m. To demonstrate this, a dedicated test setup was built. On system-level, we developed a method that avoids measuring all anchor positions one by one by exploiting increased redundancy from anchor-to-anchor and anchor-to-tag ranges, and automatically calculating the anchors topology (relative positions between each other). Nonlinear least squares provides the maximum likelihood estimate of the anchor positions and their uncertainty. This approach not only refines the accuracy of tag localization but also offers a predictive measure of its uncertainty, giving users a clearer understanding of the system's capabilities in real-world scenarios. This system-level enhancement is further complemented by the integration of a ranging protocol called automatic UWB ranging any-to-any, which offers additional layers of flexibility, reliability, and ease of deployment to the UWB localization process.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"242-253"},"PeriodicalIF":0.0,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10342852","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1109/JISPIN.2023.3338186
Changwei Chen;Solmaz S. Kia
Localization using relative ranging from radio frequency (RF) wireless beacons installed in an indoor infrastructure is becoming the hallmark of indoor localization systems for asset tracking. However, the coverage of these beacons is not always complete. Moreover, installing the beacons in underutilized spaces is not cost-effective. Deploying portable on-demand beacons to extend the coverage is a cost-effective solution for a robust and reliable RF beacon-based localization system. The challenge though is how to localize these deployed beacons. This article presents a decentralized algorithm to allow deployed beacons to self-localize themselves. This solution removes the rigid requirement of the beacon connectivity, and thus, the need to deploy the beacons in a priori known and surveyed locations. The deployed beacons localize themselves in a collaborative and decentralized manner without the necessity of each of them being connected to three preinstalled infrastructure beacons. The proposed solution is a robust deployment method in the sense that if a portable beacon is moved for any reason, it can automatically relocalize itself in the decentralized manner. Simulation studies of the ultrawideband beacon deployment and localization demonstrates the effectiveness and robustness of the proposed solution in terms of the accurate autonomous position estimation for multiple beacons with $1text{-m}$ positioning accuracy, and an average error reduction being 79.21% and 34.41% with respect to the conventional methods in literature.
{"title":"Self-Localizing On-Demand Portable Wireless Beacons for Coverage Enhancement of RF Beacon-Based Indoor Localization Systems","authors":"Changwei Chen;Solmaz S. Kia","doi":"10.1109/JISPIN.2023.3338186","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3338186","url":null,"abstract":"Localization using relative ranging from radio frequency (RF) wireless beacons installed in an indoor infrastructure is becoming the hallmark of indoor localization systems for asset tracking. However, the coverage of these beacons is not always complete. Moreover, installing the beacons in underutilized spaces is not cost-effective. Deploying portable on-demand beacons to extend the coverage is a cost-effective solution for a robust and reliable RF beacon-based localization system. The challenge though is how to localize these deployed beacons. This article presents a decentralized algorithm to allow deployed beacons to self-localize themselves. This solution removes the rigid requirement of the beacon connectivity, and thus, the need to deploy the beacons in a priori known and surveyed locations. The deployed beacons localize themselves in a collaborative and decentralized manner without the necessity of each of them being connected to three preinstalled infrastructure beacons. The proposed solution is a robust deployment method in the sense that if a portable beacon is moved for any reason, it can automatically relocalize itself in the decentralized manner. Simulation studies of the ultrawideband beacon deployment and localization demonstrates the effectiveness and robustness of the proposed solution in terms of the accurate autonomous position estimation for multiple beacons with \u0000<inline-formula><tex-math>$1text{-m}$</tex-math></inline-formula>\u0000 positioning accuracy, and an average error reduction being 79.21% and 34.41% with respect to the conventional methods in literature.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"180-186"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10336843","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.1109/JISPIN.2023.3336609
Stepan Mazokha;Fanchen Bao;George Sklivanitis;Jason O. Hallstrom
Many CSI-based localization methods have been proposed over the last decade. Fingerprinting has been one of the highest achieving approaches due to its capacity to capture environmental characteristics that are not readily captured using classic localization mechanisms such as multilateration. However, oftentimes the proposed methods are limited by reliance on large-scale training datasets. Further, methods are rarely evaluated on nonstationary devices, which are the most common in real-world environments. In our work, we address these challenges by introducing MobLoc. We adopt MUSIC pseudospectrum-based fingerprinting, which can benefit from, but does not heavily rely upon a large number of packets for each fingerprint. To evaluate our method, we leverage a publicly available dataset of passively collected CSI measurements, DLoc (Ayyalasomayajula et al., 2020), where an emitter sends signals in motion. We also benchmark MobLoc against a series of state-of-the-art localization methods. The results demonstrate that our method outperforms SpotFi (Kotaru et al., 2015), EntLoc (Chen et al., 2019), and AngLo (Chen et al., 2020), and falls very short of achieving DLoc accuracy. On the DLoc dataset, MobLoc achieves 0.33 m median (and 0.82 m, 90th percentile) localization error in a simple environment and 1.15 m median (2.59 m, 90th percentile) localization error in a complex environment. However, despite MobLoc not exceeding DLoc's accuracy, we consider its performance as a tradeoff for computational resources required to deploy the method in a real-world environment. We anticipate that this advantage will enable the adoption of MobLoc in city-scape localization systems, where the cost of computational resources is key.
{"title":"MobLoc: CSI-Based Location Fingerprinting With MUSIC","authors":"Stepan Mazokha;Fanchen Bao;George Sklivanitis;Jason O. Hallstrom","doi":"10.1109/JISPIN.2023.3336609","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3336609","url":null,"abstract":"Many CSI-based localization methods have been proposed over the last decade. Fingerprinting has been one of the highest achieving approaches due to its capacity to capture environmental characteristics that are not readily captured using classic localization mechanisms such as multilateration. However, oftentimes the proposed methods are limited by reliance on large-scale training datasets. Further, methods are rarely evaluated on nonstationary devices, which are the most common in real-world environments. In our work, we address these challenges by introducing MobLoc. We adopt MUSIC pseudospectrum-based fingerprinting, which can benefit from, but does not heavily rely upon a large number of packets for each fingerprint. To evaluate our method, we leverage a publicly available dataset of passively collected CSI measurements, DLoc (Ayyalasomayajula et al., 2020), where an emitter sends signals in motion. We also benchmark MobLoc against a series of state-of-the-art localization methods. The results demonstrate that our method outperforms SpotFi (Kotaru et al., 2015), EntLoc (Chen et al., 2019), and AngLo (Chen et al., 2020), and falls very short of achieving DLoc accuracy. On the DLoc dataset, MobLoc achieves 0.33 m median (and 0.82 m, 90th percentile) localization error in a simple environment and 1.15 m median (2.59 m, 90th percentile) localization error in a complex environment. However, despite MobLoc not exceeding DLoc's accuracy, we consider its performance as a tradeoff for computational resources required to deploy the method in a real-world environment. We anticipate that this advantage will enable the adoption of MobLoc in city-scape localization systems, where the cost of computational resources is key.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"231-241"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10333260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-28DOI: 10.1109/JISPIN.2023.3337189
Hyeokhyen Kwon;Chaitra Hegde;Yashar Kiarashi;Venkata Siva Krishna Madala;Ratan Singh;ArjunSinh Nakum;Robert Tweedy;Leandro Miletto Tonetto;Craig M. Zimring;Matthew Doiron;Amy D. Rodriguez;Allan I. Levey;Gari D. Clifford
Camera-based activity monitoring systems are becoming an attractive solution for smart building applications with the advances in computer vision and edge computing technologies. In this article, we present a feasibility study and systematic analysis of a camera-based indoor localization and multiperson tracking system implemented on edge computing devices within a large indoor space. To this end, we deployed an end-to-end edge computing pipeline that utilizes multiple cameras to achieve localization, body orientation estimation, and tracking of multiple individuals within a large therapeutic space spanning $text{1700}, text{m}^{2}$ , all while maintaining a strong focus on preserving privacy. Our pipeline consists of 39 edge computing camera systems equipped with tensor processing units (TPUs) placed in the indoor space's ceiling. To ensure the privacy of individuals, a real-time multiperson pose estimation algorithm runs on the TPU of the computing camera system. This algorithm extracts poses and bounding boxes, which are utilized for indoor localization, body orientation estimation, and multiperson tracking. Our pipeline demonstrated an average localization error of 1.41 m, a multiple-object tracking accuracy score of 88.6%, and a mean absolute body orientation error of 29 $^{circ }$ . These results show that localization and tracking of individuals in a large indoor space is feasible even with the privacy constrains.
{"title":"A Feasibility Study on Indoor Localization and Multiperson Tracking Using Sparsely Distributed Camera Network With Edge Computing","authors":"Hyeokhyen Kwon;Chaitra Hegde;Yashar Kiarashi;Venkata Siva Krishna Madala;Ratan Singh;ArjunSinh Nakum;Robert Tweedy;Leandro Miletto Tonetto;Craig M. Zimring;Matthew Doiron;Amy D. Rodriguez;Allan I. Levey;Gari D. Clifford","doi":"10.1109/JISPIN.2023.3337189","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3337189","url":null,"abstract":"Camera-based activity monitoring systems are becoming an attractive solution for smart building applications with the advances in computer vision and edge computing technologies. In this article, we present a feasibility study and systematic analysis of a camera-based indoor localization and multiperson tracking system implemented on edge computing devices within a large indoor space. To this end, we deployed an end-to-end edge computing pipeline that utilizes multiple cameras to achieve localization, body orientation estimation, and tracking of multiple individuals within a large therapeutic space spanning \u0000<inline-formula><tex-math>$text{1700}, text{m}^{2}$</tex-math></inline-formula>\u0000, all while maintaining a strong focus on preserving privacy. Our pipeline consists of 39 edge computing camera systems equipped with tensor processing units (TPUs) placed in the indoor space's ceiling. To ensure the privacy of individuals, a real-time multiperson pose estimation algorithm runs on the TPU of the computing camera system. This algorithm extracts poses and bounding boxes, which are utilized for indoor localization, body orientation estimation, and multiperson tracking. Our pipeline demonstrated an average localization error of 1.41 m, a multiple-object tracking accuracy score of 88.6%, and a mean absolute body orientation error of 29\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000. These results show that localization and tracking of individuals in a large indoor space is feasible even with the privacy constrains.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"187-198"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10329418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-28DOI: 10.1109/JISPIN.2023.3337188
Naman Agarwal;Kyle O'Keefe
This article demonstrates an alternative approach that uses global navigation satellite system (GNSS) Doppler measurements in a Kalman filter (KF) to improve the accuracy of GNSS smartphone positioning. The proposed method automates the process of estimating the uncertainty of the dynamics model of the system, which is still a challenge for the conventional KF-based GNSS positioning methods that require heuristic tuning. Automation of dynamics model uncertainty estimation also demonstrates notable improvement in GNSS outlier detection or fault detection and exclusion. In addition, this article will perform a quality assessment of the GNSS observations obtained from two Android smartphones and investigate the performance of the proposed method when using GPS L1 + Galileo E1 signals compared to GPS L5 + Galileo E5a signals.
{"title":"Use of GNSS Doppler for Prediction in Kalman Filtering for Smartphone Positioning","authors":"Naman Agarwal;Kyle O'Keefe","doi":"10.1109/JISPIN.2023.3337188","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3337188","url":null,"abstract":"This article demonstrates an alternative approach that uses global navigation satellite system (GNSS) Doppler measurements in a Kalman filter (KF) to improve the accuracy of GNSS smartphone positioning. The proposed method automates the process of estimating the uncertainty of the dynamics model of the system, which is still a challenge for the conventional KF-based GNSS positioning methods that require heuristic tuning. Automation of dynamics model uncertainty estimation also demonstrates notable improvement in GNSS outlier detection or fault detection and exclusion. In addition, this article will perform a quality assessment of the GNSS observations obtained from two Android smartphones and investigate the performance of the proposed method when using GPS L1 + Galileo E1 signals compared to GPS L5 + Galileo E5a signals.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"151-160"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10329441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-27DOI: 10.1109/JISPIN.2023.3337055
Cung Lian Sang;Michael Adams;Marc Hesse;Ulrich Rückert
A bidirectional ultrawideband (UWB) localization scheme is one of the three widely adopted design integration processes commonly used in time-based UWB positioning systems. The key property of bidirectional UWB localization is its ability to serve both navigation and tracking tasks within a single localization scheme on demand. Traditionally, navigation and tracking in wireless localization systems were treated as separate entities due to distinct applicable use-cases and methodological needs in each implementation process. Therefore, the ability to flexibly or elastically combine two unique positioning perspectives (navigation and tracking) within a single scheme can be regarded as a paradigm shift in the way location-based services are conventionally observed. This article reviews the mentioned bidirectional UWB localization from the perspective of a flexible and versatile positioning topology and highlights its potential in the field. In this regard, the article comprehensively describes the complete system model of the bidirectional UWB localization scheme using modular processes. It also discusses the demonstrative evaluation of two system integration processes and conducts a strengths, weaknesses, opportunities, and threats analysis of the scheme. Furthermore, the prospect of the presented bidirectional localization scheme for achieving precise location estimation in 5G/6G wireless mobile networks, as well as in Wi-Fi fine-time measurement-based positioning systems was briefly discussed.
{"title":"Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme for GNSS-Deprived Zones","authors":"Cung Lian Sang;Michael Adams;Marc Hesse;Ulrich Rückert","doi":"10.1109/JISPIN.2023.3337055","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3337055","url":null,"abstract":"A bidirectional ultrawideband (UWB) localization scheme is one of the three widely adopted design integration processes commonly used in time-based UWB positioning systems. The key property of bidirectional UWB localization is its ability to serve both navigation and tracking tasks within a single localization scheme on demand. Traditionally, navigation and tracking in wireless localization systems were treated as separate entities due to distinct applicable use-cases and methodological needs in each implementation process. Therefore, the ability to flexibly or elastically combine two unique positioning perspectives (navigation and tracking) within a single scheme can be regarded as a paradigm shift in the way location-based services are conventionally observed. This article reviews the mentioned bidirectional UWB localization from the perspective of a flexible and versatile positioning topology and highlights its potential in the field. In this regard, the article comprehensively describes the complete system model of the bidirectional UWB localization scheme using modular processes. It also discusses the demonstrative evaluation of two system integration processes and conducts a strengths, weaknesses, opportunities, and threats analysis of the scheme. Furthermore, the prospect of the presented bidirectional localization scheme for achieving precise location estimation in 5G/6G wireless mobile networks, as well as in Wi-Fi fine-time measurement-based positioning systems was briefly discussed.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"161-179"},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10328864","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138678660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-22DOI: 10.1109/JISPIN.2023.3335882
Tomas Bravenec;Joaquín Torres-Sospedra;Michael Gould;Tomas Fryza
This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.
{"title":"UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests","authors":"Tomas Bravenec;Joaquín Torres-Sospedra;Michael Gould;Tomas Fryza","doi":"10.1109/JISPIN.2023.3335882","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3335882","url":null,"abstract":"This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"221-230"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10325607","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.1109/JISPIN.2023.3334690
Ali A. Darwish;Arie Nakhmani
This article proposes an architecture for drone navigation and target interception, utilizing a self-supervised, model-free deep reinforcement learning approach. Unlike the traditional methods relying on complex controllers, our approach uses deep reinforcement learning with cascade rewards, enabling a single drone to navigate obstacles and intercept targets using only a forward-facing depth–RGB camera. This research has significant implications for robotics, as it demonstrates how complex tasks can be tackled using deep reinforcement learning. Our work encompasses three key contributions. First, we tackle the challenge of partial observability when employing nonlinear function approximators for learning stochastic policies. Second, we optimize the task of maximizing the overall expected reward. Finally, we develop a software library for training drones to track and intercept targets. Through our experiments, we demonstrated that our approach, incorporating cascade reward, outperforms state-of-the-art deep Q -network algorithms in terms of learning policies. By leveraging our methodology, drones can successfully navigate complex indoor and outdoor environments and effectively intercept targets based on visual cues.
{"title":"Drone Navigation and Target Interception Using Deep Reinforcement Learning: A Cascade Reward Approach","authors":"Ali A. Darwish;Arie Nakhmani","doi":"10.1109/JISPIN.2023.3334690","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3334690","url":null,"abstract":"This article proposes an architecture for drone navigation and target interception, utilizing a self-supervised, model-free deep reinforcement learning approach. Unlike the traditional methods relying on complex controllers, our approach uses deep reinforcement learning with cascade rewards, enabling a single drone to navigate obstacles and intercept targets using only a forward-facing depth–RGB camera. This research has significant implications for robotics, as it demonstrates how complex tasks can be tackled using deep reinforcement learning. Our work encompasses three key contributions. First, we tackle the challenge of partial observability when employing nonlinear function approximators for learning stochastic policies. Second, we optimize the task of maximizing the overall expected reward. Finally, we develop a software library for training drones to track and intercept targets. Through our experiments, we demonstrated that our approach, incorporating cascade reward, outperforms state-of-the-art deep \u0000<italic>Q</i>\u0000-network algorithms in terms of learning policies. By leveraging our methodology, drones can successfully navigate complex indoor and outdoor environments and effectively intercept targets based on visual cues.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"130-140"},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.1109/JISPIN.2023.3334697
Aviad Etzion;Itzik Klein
Mobile robots are used in a variety of applications indoors and outdoors. In real-world scenarios, frequently, the navigation solution relies only on the inertial sensors. Consequently, the navigation solution drifts in time. In this article, we propose the mobile robot pure inertial framework (MoRPI). Instead of travelling in a straight line trajectory, the robot moves in a periodic motion trajectory to enable peak-to-peak estimation. Two types of MoRPI approaches are suggested, one is based on both accelerometer and gyroscope readings while the other requires only the gyroscopes. Closed form analytical solutions are derived to show that MoRPI produces lower position error compared to the classical pure inertial solution. In addition, field experiments were made with a mobile robot equipped with two different types of inertial sensors. The results show the benefits of using our approach.
{"title":"MoRPI: Mobile Robot Pure Inertial Navigation","authors":"Aviad Etzion;Itzik Klein","doi":"10.1109/JISPIN.2023.3334697","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3334697","url":null,"abstract":"Mobile robots are used in a variety of applications indoors and outdoors. In real-world scenarios, frequently, the navigation solution relies only on the inertial sensors. Consequently, the navigation solution drifts in time. In this article, we propose the mobile robot pure inertial framework (MoRPI). Instead of travelling in a straight line trajectory, the robot moves in a periodic motion trajectory to enable peak-to-peak estimation. Two types of MoRPI approaches are suggested, one is based on both accelerometer and gyroscope readings while the other requires only the gyroscopes. Closed form analytical solutions are derived to show that MoRPI produces lower position error compared to the classical pure inertial solution. In addition, field experiments were made with a mobile robot equipped with two different types of inertial sensors. The results show the benefits of using our approach.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"1 ","pages":"141-150"},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.1109/JISPIN.2023.3334693
Danish Gufran;Saideep Tiku;Sudeep Pasricha
Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy. Toward jointly addressing these challenges comprehensively, we propose STELLAR, a novel framework implementing a contrastive learning approach that leverages a Siamese multiheaded attention neural network. STELLAR is the first solution that simultaneously tackles device heterogeneity and temporal variations in indoor localization, without the need for retraining the model (recalibration-free). Our evaluations across diverse indoor environments show 8%–75% improvements in accuracy compared to state-of-the-art techniques, to effectively address the device heterogeneity challenge. Moreover, STELLAR outperforms existing methods by 18%–165% over two years of temporal variations, showcasing its robustness and adaptability.
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