Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)最新文献

{"title":"Field Testing of GNSS User Protection Techniques","authors":"S. Cancela, J. Navarro, D. Calle, T. Reithmaier, A. D. Chiara, G. D. Broi, I. Fernández‐Hernández, G. Seco-Granados, J. Simón","doi":"10.33012/2019.17087","DOIUrl":"https://doi.org/10.33012/2019.17087","url":null,"abstract":"GNSS is a key element for a wide range of applications in our daily lives. Mass-market applications such as sports tracking or user guidance, liability-critical applications such as banking and telecommunication time synchronization, and safety critical services such as aviation and automotive-related solutions, all rely on GNSS. The huge growth experimented during the last decade puts GNSS in the target of attackers. The Galileo program is complementing the Galileo Open Service with Navigation Message Authentication (OSNMA) and providing signal authentication through the Commercial Service signals. These new services will be able to provide added protection to the current GNSS applications. Nevertheless, these features will require the users to implement new algorithms to exploit them. In this context, the European Commission launched the Navigation Authentication through Commercial Service-Enhanced Terminal (NACSET) project aiming at researching and implementing different techniques to detect and mitigate thus improving the resilience at user-level. In the frame of the NACSET project, a user terminal has been developed based on a high-end multi-GNSS receiver that is able to track E1/L1 and E6-B/C signals for data and signal protection. The terminal is equipped with a set of resilience techniques. Among these techniques, this paper focuses on an anti-replay technique protecting against zero-delay Secure Code Estimate-Replay (SCER) attacks based on the analysis of the unpredictable symbols from OSNMA cryptographic data. This paper firstly describes the NACSET project and its aim. Secondly, the theory of Anti-replay protection is explained from the point of view of a receiver, and anti-replay techniques based on OSNMA are introduced. Then, we describe the SCER simulator developed to assess the performances of the technique. To conclude, an attack is defined and performed with the SCER simulator over a real receiver. The results with and without OSNMA replay protection are presented and explained, and some conclusions of the experiment are derived.","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127811041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Error Model for Integrity of Pattern-based Visual Positioning","authors":"Chen Zhu, C. Steinmetz, B. Belabbas, M. Meurer","doi":"10.33012/2019.16956","DOIUrl":"https://doi.org/10.33012/2019.16956","url":null,"abstract":"Camera-based visual navigation techniques can provide high precision infrastructure-less localization solutions using visual patterns, and play an important role in the environments where satellite navigation has significantly degraded performance in availability, accuracy, and integrity. However, the integrity monitoring of visual navigation methods is an essential but hardly-solved topic, since modelling the geometric error for cameras is rather challenging. This work proposes a highprecision geometric error model of detected feature corners for chessboard-like patterns. The model is named as Chessboard Corner Geometric Error Model (CCGEM). By applying the model to images containing chessboard-like patterns, the extracted corner location accuracy can be predicted in different lighting conditions. The coefficients in the model can be adapted to each distinct camera-lens system through a calibration process. The proposed method first models the intensity distribution in the local neighboring area of the extracted corner by taking the raw image as measurement input. Then, the geometric error of the feature location is modelled as a function of the distribution parameters. We show that the model fits the measurement error well in both simulated and real images. The proposed CCGEM also provides a conservative fitting model with risk probability information, which can be applied in the integrity monitoring of vision-based positioning. (a) Feature extraction without noise (b) Feature extraction with noise Figure 1: Photometric error and consequential geometric error in feature extraction INTRODUCTION Camera-based visual positioning has been widely investigated for autonomous landing of unmanned aerial vehicles (UAV) using a designed pattern as a landing pad. For instance, the approaches from Sharp et al. [1] and Cesetti et al. [2] have attracted great attentions of the research community. In addition, visual navigation techniques have huge potential in various applications, especially in environments such as urban areas where satellite navigation may have significantly degraded performance due to lacking of signal availability and multipath effects, e.g., as shown in the work from Narula et al. [3]. However, quantitative integrity monitoring of visual navigation is not yet a well-solved problem. Three basic components are essential for developing the visual navigation integrity. First of all, a feature location error model in nominal situations is required. Second, the dilution of precision (DOP) needs to be calculated to evaluate the geometric impact on the estimated position using cameras. Last but not least, specific fault detection and exclusion (FDE) schemes should be developed for different fault modes in visual navigation integrity monitoring. This work focuses on the development of a stochastic error model for the feature location. A stochastic error model is not only required for monitoring the nominal performance of the visual navigation met","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126202664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Improvements to the Location Corrections through Differential Networks (LOCD-IN) System","authors":"Russell Gilabert, Evan Dill, M. Haag","doi":"10.33012/2019.16969","DOIUrl":"https://doi.org/10.33012/2019.16969","url":null,"abstract":"","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133975441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibration-free Visual-Inertial Fusion with Deep Convolutional Recurrent Neural Networks","authors":"S. Sheikhpour, M. Atia","doi":"10.33012/2019.16918","DOIUrl":"https://doi.org/10.33012/2019.16918","url":null,"abstract":"Visual-Inertial Odometry (VIO) has been one of the most popular yet affordable navigation systems for indoor and even outdoor applications. VIO can augment or replace Global Navigation Satellite Systems (GNSSs) under signal degradation or service interruptions. Conventionally, the fusion of visual and inertial modalities has been performed using optimization-based or filtering-based techniques such as nonlinear Least Squares (LS) or Extended Kalman Filter (EKF). These classic techniques, despite several simplifying approximations, involve sophisticated modelling and parameterization of the navigation problem, which necessitates expert fine-tuning of the navigation system. In this work, a calibration-free visual-inertial fusion technique using Deep Convolutional Recurrent Neural Networks (DCRNN) is proposed. The network employs a Convolutional Neural Network (CNN) to process the spatial information embedded in visual data and two Recurrent Neural Networks (RNNs) to process the inertial sensor measurements and the CNN output for final pose estimation. The network is trained with raw Inertial Measurement Unit (IMU) data and monocular camera frames as its inputs, and the relative pose as its output. Unlike the conventional VIO techniques, there is no need for IMU biases and scale factors, intrinsic and extrinsic parameters of the camera to be explicitly provided or modelled in the proposed navigation system, rather these parameters along with system dynamics are implicitly learned during the training phase. Moreover, since the inertial and visual data are fused at mid-layers in the network, deeper correlations of these two modalities are learned compared to a simple combination of the final pose estimates of both modalities at the output layers, hence, the fusion can be considered as a tightly-coupled fusion of visual and inertial modalities. The proposed VIO network is evaluated on real datasets and thorough discussion is provided on the capabilities of the deep learning approach toward VIO.","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121292753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining Visual, Pedestrian, and Collaborative Navigation Techniques for Team Based Infrastructure Free Indoor Navigation","authors":"Aiden Morrison, L. Ruotsalainen, Maija Makela, Jesperi Rantanen, N. Sokolova","doi":"10.33012/2019.17098","DOIUrl":"https://doi.org/10.33012/2019.17098","url":null,"abstract":"In this paper the authors describe the design and evaluation of a multi sensor integrated navigation system specifically targeted at teams of cooperating users operating in transient indoor conditions such as would be encountered by emergency services personnel or soldiers entering unknown buildings. Since these conditions preclude the use of dedicated indoor infrastructure the system depends on the combination of multiple self contained navigation sensors as well as dynamic networking and ranging between the users to form a decentralized cooperative navigating team. Within this paper we will discuss the design and evaluation of a system developed within a North Atlantic Treaty Organization (NATO) Science for Peace and Security (SPS) project executed by the SINTEF and the Finnish Geospatial Researcher Institute (FGI) during 2018 and 2019. The motivation of this project was to combine the expertise of the FGI in pedestrian and camera based infrastructure free navigation with the collaborative navigation and integrated navigation system design expertise of SINTEF towards the accurate navigation and continuous situational awareness of teams of cooperating users. When completed, the combined navigation system will be a shoulder mounted package which comprises a triple frequency GNSS receiver for rapid outdoor initialization, as well as a Micro Electro Mechanical System (MEMS) Inertial Measurement Unit (IMU), barometer, magnetometer, three different navigation and communication radios as well as a stereo vision plus depth sensing camera connected to and synchronized by an integrated processor platform. Two of the three radios provide for user-to-user range measurement and data exchange via each of 2.4 GHz and Ultra Wide-Band (UWB) signals to allow for collaborative navigation as well as situational awareness within the network, while the 3 rd radio provides a link to separate navigation sensors such as a foot mounted IMU pod for enhanced Pedestrian Dead Reckoning (PDR). The integrated camera provides stereo color imaging as well as structured light based infrared depth sensing, while the processor platform is responsible for data collection and processing. Introduction The motivation in pursuing infrastructure free navigation systems relates to the fact that certain classes of user including firefighters, law enforcement, soldiers and others must enter hazardous indoor environments on short notice and without detailed knowledge of the interior structure, layout or contents of these buildings. Additionally, since the building might be on fire or otherwise denied electrical power, reliance on even ad-hoc infrastructure such as Wi-Fi routers may not be a reliable source of navigation data. Assuming that the building materials block the majority of GNSS signals to the users, the remaining options are typically those sources of information that are self-contained to the individual user such as inertial sensors and visual odometry (VO) to allow each user to","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131669733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of GNSS Performance on Dual-Frequency Smartphones","authors":"Thorsten Jahn, M. Kaindl, Ignacio Viciano Semper, S. Damy, M. Navarro-Gallardo†, F. Diani, Justyna Redelkiewicz","doi":"10.33012/2019.16860","DOIUrl":"https://doi.org/10.33012/2019.16860","url":null,"abstract":"","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124255656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter-Receiver GNSS Pseudorange Biases and Their Effect on Clock and DCB Estimation","authors":"A. Hauschild, P. Steigenberger, O. Montenbruck","doi":"10.33012/2019.16975","DOIUrl":"https://doi.org/10.33012/2019.16975","url":null,"abstract":"","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116776380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supercorrelation as a Service: S-GNSS Upgrades for Smartdevices","authors":"Ramsey Faragher, M. Powe, P. Esteves, N. Couronneau, M. Crockett, Henry Martin, Emanuele Ziglioli, Chris Higgins","doi":"10.33012/2019.16983","DOIUrl":"https://doi.org/10.33012/2019.16983","url":null,"abstract":"","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124991891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative Toolbox for Reference Station Multipath and Interference Site Surveying","authors":"A. Emmanuele, M. Puccitelli, Niccolò Pastori, A. Ferrario, L. Marradi, A. Khanal, P. Crosta, R. Sarnadas, T. Thúróczy, R. Guidi","doi":"10.33012/2019.17103","DOIUrl":"https://doi.org/10.33012/2019.17103","url":null,"abstract":"","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126086030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing GNSS Mobile Positioning in Urban Environments through Utilization of Multipath Prediction and Consistency Analysis","authors":"N. Ziedan","doi":"10.33012/2019.16929","DOIUrl":"https://doi.org/10.33012/2019.16929","url":null,"abstract":"","PeriodicalId":381025,"journal":{"name":"Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123281285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}