This paper establishes a cross-band (CB) multi-frequency (MF) combined direct position estimation (DPE) architecture to facilitate DPE algorithm in more challenging environments. A virtual reference model is constructed to project the MF signals in a unified reference domain and combine them using peak-to-average power ratio. The code delay differences and independence of the noises among different bands are also considered to guarantee the feasibility of the CB combination. In addition, GPS L1/L5 data collected in urban canyon is utilized in this paper. The positioning performance analysis showed that the MF DPE can significantly improve the single-frequency DPE algorithms. The navigation domain correlation results showed that comparing GPS L1 to L5, the GPS L5 has the better anti-jamming capability with higher code rate.
{"title":"GNSS Multi-Frequency Combined Direct Position Estimation in the Urban Canyon Environment","authors":"Jihong Huang, Rong Yang, Xingqun Zhan","doi":"10.33012/2023.19350","DOIUrl":"https://doi.org/10.33012/2023.19350","url":null,"abstract":"This paper establishes a cross-band (CB) multi-frequency (MF) combined direct position estimation (DPE) architecture to facilitate DPE algorithm in more challenging environments. A virtual reference model is constructed to project the MF signals in a unified reference domain and combine them using peak-to-average power ratio. The code delay differences and independence of the noises among different bands are also considered to guarantee the feasibility of the CB combination. In addition, GPS L1/L5 data collected in urban canyon is utilized in this paper. The positioning performance analysis showed that the MF DPE can significantly improve the single-frequency DPE algorithms. The navigation domain correlation results showed that comparing GPS L1 to L5, the GPS L5 has the better anti-jamming capability with higher code rate.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135483788","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}
Eslam Mounier, Paulo Ricardo Marques de Araujo, Mohamed Elhabiby, Michael Korenberg, Aboelmagd Noureldin
Inertial Navigation System (INS) is a primary component in various integrated navigation systems. However, the performance of INS is hindered due to the numerical integration of the measurements obtained from the Inertial Measurement Unit (IMU), which are contaminated by various sensor errors, especially with Micro-Electro-Mechanical Systems (MEMS) sensors. To address these challenges, we examine the performance of modern Deep Learning (DL) methods for mitigating such errors. Specifically, we propose a Deep Gyroscope Error (DGE) model designed to estimate and compensate for errors in the gyroscope measurements. The DGE model combines the feature extraction capabilities of a Convolutional Neural Network (CNN) with the sequential data modelling strengths of Long Short-Term Memory (LSTM). Instead of relying on high-grade IMU measurements, we distinctively employ an inverse mechanization algorithm that generates artificial IMU measurements from the integrated navigation solution states. This approach provides accurate ground truth data facilitating direct supervised learning. The proposed model was trained and verified using real data from MEMS-IMU on real road test experiments performed on a land vehicle in Kingston, Ontario, Canada. When subjected to evaluation against unseen data, the DGE model demonstrated significant improvements in standalone inertial navigation scenarios, particularly in mitigating attitude drift errors and subsequently improving position estimation. Over a uniform testing interval, the DGE model achieved an average reduction in attitude RMSE by 43.1% and in position RMSE by 25.4%. This emphasizes the efficacy of the proposed method in improving INS performance, particularly when operating in standalone mode.
{"title":"Exploring the Benefits of Deep Learning-Based Sensors Error Estimation for Improved Attitude and Position Accuracy","authors":"Eslam Mounier, Paulo Ricardo Marques de Araujo, Mohamed Elhabiby, Michael Korenberg, Aboelmagd Noureldin","doi":"10.33012/2023.19273","DOIUrl":"https://doi.org/10.33012/2023.19273","url":null,"abstract":"Inertial Navigation System (INS) is a primary component in various integrated navigation systems. However, the performance of INS is hindered due to the numerical integration of the measurements obtained from the Inertial Measurement Unit (IMU), which are contaminated by various sensor errors, especially with Micro-Electro-Mechanical Systems (MEMS) sensors. To address these challenges, we examine the performance of modern Deep Learning (DL) methods for mitigating such errors. Specifically, we propose a Deep Gyroscope Error (DGE) model designed to estimate and compensate for errors in the gyroscope measurements. The DGE model combines the feature extraction capabilities of a Convolutional Neural Network (CNN) with the sequential data modelling strengths of Long Short-Term Memory (LSTM). Instead of relying on high-grade IMU measurements, we distinctively employ an inverse mechanization algorithm that generates artificial IMU measurements from the integrated navigation solution states. This approach provides accurate ground truth data facilitating direct supervised learning. The proposed model was trained and verified using real data from MEMS-IMU on real road test experiments performed on a land vehicle in Kingston, Ontario, Canada. When subjected to evaluation against unseen data, the DGE model demonstrated significant improvements in standalone inertial navigation scenarios, particularly in mitigating attitude drift errors and subsequently improving position estimation. Over a uniform testing interval, the DGE model achieved an average reduction in attitude RMSE by 43.1% and in position RMSE by 25.4%. This emphasizes the efficacy of the proposed method in improving INS performance, particularly when operating in standalone mode.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135483790","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}
Globally referenced and accurate positioning is of great significance for the realization of fully autonomous systems. The visual and inertial measurement unit (IMU) integrated navigation system (VINS) can provide accurate positioning in a short period but is subject to drift over time. Meanwhile, the performance of the VINS is significantly degraded in urban canyons due to the numerous outlier visual features caused by moving objects and unstable illuminations. The global navigation satellite system (GNSS) can provide reliable and globally referenced positioning in open areas, but it is challenged in urban canyons due to the signal reflections and blockages from tall buildings. To exploit the complementariness of the GNSS and VINS, this paper proposed a sliding window factor graph optimization (FGO) based GNSS/Visual/IMU/Map Integration. First, the window carrier phase (WCP) and the Doppler measurements are explored to constrain the relative motion of the system within consecutive epochs. Second, a novel sliding window (SW) based map matching model is proposed to correct the states using the lightweight OpenStreetMap (OSM). Different from conventional filtering-based map matching, the states within the sliding window of the FGO are associated with the lane information from the OSM which effectively exploited the measurement redundancy arising from the factor graph model. The effectiveness of the proposed method is validated using the challenging dataset collected in the urban canyons of Hong Kong. The results showed that lane-level positioning can be achieved even in dense urban scenarios, with poor satellite visibilities and numerous visual feature outliers.
{"title":"GVIM: GNSS/Visual/IMU/Map Integration Via Sliding Window Factor Graph Optimization in Urban Canyons","authors":"Xiwei Bai, Li-Ta Hsu","doi":"10.33012/2023.19458","DOIUrl":"https://doi.org/10.33012/2023.19458","url":null,"abstract":"Globally referenced and accurate positioning is of great significance for the realization of fully autonomous systems. The visual and inertial measurement unit (IMU) integrated navigation system (VINS) can provide accurate positioning in a short period but is subject to drift over time. Meanwhile, the performance of the VINS is significantly degraded in urban canyons due to the numerous outlier visual features caused by moving objects and unstable illuminations. The global navigation satellite system (GNSS) can provide reliable and globally referenced positioning in open areas, but it is challenged in urban canyons due to the signal reflections and blockages from tall buildings. To exploit the complementariness of the GNSS and VINS, this paper proposed a sliding window factor graph optimization (FGO) based GNSS/Visual/IMU/Map Integration. First, the window carrier phase (WCP) and the Doppler measurements are explored to constrain the relative motion of the system within consecutive epochs. Second, a novel sliding window (SW) based map matching model is proposed to correct the states using the lightweight OpenStreetMap (OSM). Different from conventional filtering-based map matching, the states within the sliding window of the FGO are associated with the lane information from the OSM which effectively exploited the measurement redundancy arising from the factor graph model. The effectiveness of the proposed method is validated using the challenging dataset collected in the urban canyons of Hong Kong. The results showed that lane-level positioning can be achieved even in dense urban scenarios, with poor satellite visibilities and numerous visual feature outliers.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135483914","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}
Zacharias M. Komodromos, Wenkai Qin, Todd E. Humphreys
This paper summarizes the current-known model for Starlink’s Ku-band downlink signal and develops a platform for simulating a received signal. The simulator models key elements of the signal structure, along with channel effects such as noise, delay, and Doppler. Further, this paper outlines a hypothesis testing detection process for acquisition of a Starlink downlink frame. The information in this paper will be of general interest to those seeking to understand the Starlink waveform, but is particularly targeted to those wishing to exploit Starlink as an alternative to GNSS for position, navigation, and timing (PNT). The simulator can also make use of precise ephemerides to generate more faithful signals. Theoretical limits on the minimum signal-to-noise ratio required to detect a frame are presented and supported by simulated signals. Finally, the hypothesis testing detection process is applied to live-captured Starlink data.
{"title":"Signal Simulator for Starlink Ku-Band Downlink","authors":"Zacharias M. Komodromos, Wenkai Qin, Todd E. Humphreys","doi":"10.33012/2023.19308","DOIUrl":"https://doi.org/10.33012/2023.19308","url":null,"abstract":"This paper summarizes the current-known model for Starlink’s Ku-band downlink signal and develops a platform for simulating a received signal. The simulator models key elements of the signal structure, along with channel effects such as noise, delay, and Doppler. Further, this paper outlines a hypothesis testing detection process for acquisition of a Starlink downlink frame. The information in this paper will be of general interest to those seeking to understand the Starlink waveform, but is particularly targeted to those wishing to exploit Starlink as an alternative to GNSS for position, navigation, and timing (PNT). The simulator can also make use of precise ephemerides to generate more faithful signals. Theoretical limits on the minimum signal-to-noise ratio required to detect a frame are presented and supported by simulated signals. Finally, the hypothesis testing detection process is applied to live-captured Starlink data.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135483919","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}
Yi Jin, Di He, Longwei Tian, Wenxian Yu, Shuang Wei, Fusheng Zhu, Zhuoling Xiao
Array imperfection may cause performance degradation to direction-of-arrival (DOA) estimation in practice. Most DOA estimation methods overlook the array imperfection by regarding the array manifold as a piece of precisely prior knowledge. Although previous works suggest some simple calibration processes, limitations of array errors like amplitude and phase deviation (AP) and antenna position perturbation (PP) may still lead to manifold mismatch against high-precision. The application of neural network (NN) methods in DOA estimation has demonstrated improved robustness but is limited in handling complex array errors. In this paper, a Transformer-based calibration network (TCN) is designed to capture global sequence information effectively and generate steering vectors of grid points. Then a framework based on modified root-sparse Bayesian learning (RSBL) is proposed to iterate calibration and estimation steps alternately. Extensive experiments show that the proposed method can achieve better performance in different array imperfections, including AP and PP, than other existing methods. When weak array imperfection exists, the proposed method keeps the average error below 0.5 degrees while MUSIC, OMP, and RSBL reach the highest above 2.7 degrees.
{"title":"A Modified Sparse Bayesian Learning Method for High-Accuracy DOA Estimation with TCN Under Array Imperfection","authors":"Yi Jin, Di He, Longwei Tian, Wenxian Yu, Shuang Wei, Fusheng Zhu, Zhuoling Xiao","doi":"10.33012/2023.19396","DOIUrl":"https://doi.org/10.33012/2023.19396","url":null,"abstract":"Array imperfection may cause performance degradation to direction-of-arrival (DOA) estimation in practice. Most DOA estimation methods overlook the array imperfection by regarding the array manifold as a piece of precisely prior knowledge. Although previous works suggest some simple calibration processes, limitations of array errors like amplitude and phase deviation (AP) and antenna position perturbation (PP) may still lead to manifold mismatch against high-precision. The application of neural network (NN) methods in DOA estimation has demonstrated improved robustness but is limited in handling complex array errors. In this paper, a Transformer-based calibration network (TCN) is designed to capture global sequence information effectively and generate steering vectors of grid points. Then a framework based on modified root-sparse Bayesian learning (RSBL) is proposed to iterate calibration and estimation steps alternately. Extensive experiments show that the proposed method can achieve better performance in different array imperfections, including AP and PP, than other existing methods. When weak array imperfection exists, the proposed method keeps the average error below 0.5 degrees while MUSIC, OMP, and RSBL reach the highest above 2.7 degrees.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135483984","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}
In this paper, we present a methodology for detecting and excluding spoofed Global Navigation Satellite System (GNSS) signals by decomposing Complex Cross Ambiguity Functions (CCAF) into their constitutive components. Building on previous work in [1] and [2] utilizing CCAF decomposition and inverse Receiver Autonomous Integrity Monitoring (RAIM), we integrate CCAF decomposition with an inertial sensor in dynamic environments [3]. This integration enables us to identify and exclude spoofed signals, ensuring continuous tracking of the authentic signal for navigation. The method is effective in spoofing scenarios that can lead to Hazardous Misleading information (HMI) and are difficult to detect by other means. It can identify spoofing in the presence of multipath and when the spoofing signal is power-matched with offsets in code delay and Doppler frequency that are close to the true signal. Using the proposed approach, spoofing can be identified at an early stage within the receiver for dynamic users.
{"title":"GNSS Spoofing Detection and Exclusion by Decomposition of Complex Cross Ambiguity Function (DCCAF) with INS Aiding","authors":"Sahil Ahmed, Samer Khanafseh, Boris Pervan","doi":"10.33012/2023.19349","DOIUrl":"https://doi.org/10.33012/2023.19349","url":null,"abstract":"In this paper, we present a methodology for detecting and excluding spoofed Global Navigation Satellite System (GNSS) signals by decomposing Complex Cross Ambiguity Functions (CCAF) into their constitutive components. Building on previous work in [1] and [2] utilizing CCAF decomposition and inverse Receiver Autonomous Integrity Monitoring (RAIM), we integrate CCAF decomposition with an inertial sensor in dynamic environments [3]. This integration enables us to identify and exclude spoofed signals, ensuring continuous tracking of the authentic signal for navigation. The method is effective in spoofing scenarios that can lead to Hazardous Misleading information (HMI) and are difficult to detect by other means. It can identify spoofing in the presence of multipath and when the spoofing signal is power-matched with offsets in code delay and Doppler frequency that are close to the true signal. Using the proposed approach, spoofing can be identified at an early stage within the receiver for dynamic users.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135483985","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}
RAIM capable receivers cover the necessity of having a reliable navigation instrument for aviation applications in areas where the receiver is not supported by SBAS. Given the limitations of GPS L1 RAIM and taking advantage of the new GNSS systems in terms of constellations, frequencies and signals, RAIM concept is evolving into the Advanced RAIM (ARAIM) that intends to cover some operational scenarios that legacy RAIM does not cover (as for example the vertical guided approach LPV-200). This was detailed in Working-Group C (2016). There are still some of main ARAIM concept features that are not yet closed. Because of that, it is an asset to have a prototype test user receiver that can be easily reconfigurable to evaluate these new concepts that are still under discussion. That is the aim of project DARP. This was a project leaded by Deimos Space and funded by EUSPA with the objective of designing, developing, and integrating a prototype of receiver aimed for aviation applications with ARAIM system integrated as it was introduced in Wis, et al (2020). The activities of the project have focused on three important parts: the development of a flexible receiver platform; the analysis, design, integration, and improvement of the receiver ARAIM algorithm; and the design and execution of the validation campaign of the test user receiver. This paper details these three parts. An overview of the receiver design and implementation is performed, focusing on its key features applied to aviation application. The ARAIM algorithm implementation is described, focusing on the changes that were applied with respect to the baseline algorithm. Finally, the validation campaign is explained in more detail, focusing on its design and resources used for the execution of the tests and showing some of its more relevant results of the ARAIM algorithm.
{"title":"An ARAIM Experimental Test User Receiver. Final Review of Project DARP","authors":"Mariano Wis, Antonio Fernández","doi":"10.33012/2023.19434","DOIUrl":"https://doi.org/10.33012/2023.19434","url":null,"abstract":"RAIM capable receivers cover the necessity of having a reliable navigation instrument for aviation applications in areas where the receiver is not supported by SBAS. Given the limitations of GPS L1 RAIM and taking advantage of the new GNSS systems in terms of constellations, frequencies and signals, RAIM concept is evolving into the Advanced RAIM (ARAIM) that intends to cover some operational scenarios that legacy RAIM does not cover (as for example the vertical guided approach LPV-200). This was detailed in Working-Group C (2016). There are still some of main ARAIM concept features that are not yet closed. Because of that, it is an asset to have a prototype test user receiver that can be easily reconfigurable to evaluate these new concepts that are still under discussion. That is the aim of project DARP. This was a project leaded by Deimos Space and funded by EUSPA with the objective of designing, developing, and integrating a prototype of receiver aimed for aviation applications with ARAIM system integrated as it was introduced in Wis, et al (2020). The activities of the project have focused on three important parts: the development of a flexible receiver platform; the analysis, design, integration, and improvement of the receiver ARAIM algorithm; and the design and execution of the validation campaign of the test user receiver. This paper details these three parts. An overview of the receiver design and implementation is performed, focusing on its key features applied to aviation application. The ARAIM algorithm implementation is described, focusing on the changes that were applied with respect to the baseline algorithm. Finally, the validation campaign is explained in more detail, focusing on its design and resources used for the execution of the tests and showing some of its more relevant results of the ARAIM algorithm.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135484044","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}
The growing dependence of critical infrastructure on Global Navigation Satellite Systems (GNSS) as an accurate and reliable positioning, navigation and timing (PNT) source gives rise to the importance of GNSS interference detection. Although jamming detection capabilities are present in the current market, predominately in the form of specialised GNSS interference detectors or GNSS receivers add-ons. These provide a limited coverage area and their implementation into critical infrastructure operations is rather slow. Therefore, this paper focuses on the detection of GNSS interference using widespread Automatic Dependent Surveillance-Broadcast (ADS-B) technology. The research builds upon previous work and addresses some of its limitations by developing a discrete mathematical model for GNSS jamming detection based on ADS-B quality parameters. To develop and validate the model, a series of experiments involving GNSS jamming in live-sky environments were conducted. The controlled experiments enabled close monitoring of the aircraft navigation systems allowing for precise determination of the aircraft’s jammed/unjammed status. Approximately 75% of the jamming experiment data was used for model development and tuning, while the remaining 25% was reserved for evaluation. The model evaluation leveraging the confusion matrix showed a positive jamming detection rate of over 99% and a false positive jamming detection rate of under 1%. Additionally, the model was tested on ADS-B data from the Atlantic Ocean where no GNSS jamming is expected. Using this data set the model exhibited an under 1% false positive jamming detection rate.
{"title":"Discrete Mathematical Model for GNSS Interference Detection Using ADS-B Quality Parameters","authors":"Jakub Steiner, Ivan Nagy","doi":"10.33012/2023.19383","DOIUrl":"https://doi.org/10.33012/2023.19383","url":null,"abstract":"The growing dependence of critical infrastructure on Global Navigation Satellite Systems (GNSS) as an accurate and reliable positioning, navigation and timing (PNT) source gives rise to the importance of GNSS interference detection. Although jamming detection capabilities are present in the current market, predominately in the form of specialised GNSS interference detectors or GNSS receivers add-ons. These provide a limited coverage area and their implementation into critical infrastructure operations is rather slow. Therefore, this paper focuses on the detection of GNSS interference using widespread Automatic Dependent Surveillance-Broadcast (ADS-B) technology. The research builds upon previous work and addresses some of its limitations by developing a discrete mathematical model for GNSS jamming detection based on ADS-B quality parameters. To develop and validate the model, a series of experiments involving GNSS jamming in live-sky environments were conducted. The controlled experiments enabled close monitoring of the aircraft navigation systems allowing for precise determination of the aircraft’s jammed/unjammed status. Approximately 75% of the jamming experiment data was used for model development and tuning, while the remaining 25% was reserved for evaluation. The model evaluation leveraging the confusion matrix showed a positive jamming detection rate of over 99% and a false positive jamming detection rate of under 1%. Additionally, the model was tested on ADS-B data from the Atlantic Ocean where no GNSS jamming is expected. Using this data set the model exhibited an under 1% false positive jamming detection rate.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135484103","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}
Toni Hammarberg, José M. Vallet García, Jarno N. Alanko, M. Zahidul H. Bhuiyan
The European Global Navigation Satellite System (GNSS) Galileo is launching the Open Service Navigation Message Authentication (OSNMA) to enable navigation message authentication for all users, and therefore increasing the resiliency against spoofing. The Finnish Geospatial Research Institute (FGI) has developed an open source implementation of Galileo’s OSNMA, henceforth known as FGI-OSNMA. FGI-OSNMA is a Python library functioning as a OSNMA computation engine with special emphasis put into its modularity, usability in real time, and integrability as a library in third party applications. In particular, the library is being integrated to the software receiver FGI-GSRx and the GNSS situational awareness service GNSS-Finland. In addition to this, our software package contains useful tools, such as scripts to compute and visualize key performance indicators (KPIs) related to authentication, and a filter to remove unauthenticated messages from RINEX navigation and observables files. This paper presents an overview of the features of FGI-OSNMA, followed by description of the architecture and the rationale behind the design. Finally, the paper concludes by demonstrating practical examples and real-world applications of the library.
{"title":"FGI-OSNMA: An Open Source Implementation of Galileo’s Open Service Navigation Message Authentication","authors":"Toni Hammarberg, José M. Vallet García, Jarno N. Alanko, M. Zahidul H. Bhuiyan","doi":"10.33012/2023.19348","DOIUrl":"https://doi.org/10.33012/2023.19348","url":null,"abstract":"The European Global Navigation Satellite System (GNSS) Galileo is launching the Open Service Navigation Message Authentication (OSNMA) to enable navigation message authentication for all users, and therefore increasing the resiliency against spoofing. The Finnish Geospatial Research Institute (FGI) has developed an open source implementation of Galileo’s OSNMA, henceforth known as FGI-OSNMA. FGI-OSNMA is a Python library functioning as a OSNMA computation engine with special emphasis put into its modularity, usability in real time, and integrability as a library in third party applications. In particular, the library is being integrated to the software receiver FGI-GSRx and the GNSS situational awareness service GNSS-Finland. In addition to this, our software package contains useful tools, such as scripts to compute and visualize key performance indicators (KPIs) related to authentication, and a filter to remove unauthenticated messages from RINEX navigation and observables files. This paper presents an overview of the features of FGI-OSNMA, followed by description of the architecture and the rationale behind the design. Finally, the paper concludes by demonstrating practical examples and real-world applications of the library.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135484111","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}
Sandeep Jada, John Bowman, Mark Psiaki, Steven Langel, Mathieu Joerger
This paper describes our methodology to investigate an unknown type of interference at the GPS L1 frequency. This interference does not cause GPS receivers to lose lock on signals and does not cause significant variations in the carrier-to-noise ratio (C/N0). However, it causes frequent false alerts in GNSS interference monitors, including in our own power-based jamming monitors that we had deployed in Virginia, North Carolina, and Colorado. We obtained data from three other independent groups in the US and Europe experiencing similar unexplained interference showing characteristic on-off keying or binary frequency-shift keying (BFSK). This paper describes how we identified their source as spurious emissions from car key fobs. Other remote-control and wireless devices used in automotive applications generate similar interference despite their specified broadcast frequency being nowhere near L1.
{"title":"Identifying Car Key Fobs as a Cause of Interference at GNSS Frequencies","authors":"Sandeep Jada, John Bowman, Mark Psiaki, Steven Langel, Mathieu Joerger","doi":"10.33012/2023.19376","DOIUrl":"https://doi.org/10.33012/2023.19376","url":null,"abstract":"This paper describes our methodology to investigate an unknown type of interference at the GPS L1 frequency. This interference does not cause GPS receivers to lose lock on signals and does not cause significant variations in the carrier-to-noise ratio (C/N0). However, it causes frequent false alerts in GNSS interference monitors, including in our own power-based jamming monitors that we had deployed in Virginia, North Carolina, and Colorado. We obtained data from three other independent groups in the US and Europe experiencing similar unexplained interference showing characteristic on-off keying or binary frequency-shift keying (BFSK). This paper describes how we identified their source as spurious emissions from car key fobs. Other remote-control and wireless devices used in automotive applications generate similar interference despite their specified broadcast frequency being nowhere near L1.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135484117","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}
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