Global Navigation Satellite System Reflectometry (GNSS-R) has established itself as a versatile remote sensing method applicable to various environmental monitoring tasks, including the water level measurements. In this paper, we propose a novel stochastic approach to estimate water levels in near real-time using GNSS-R. By integrating GNSS signal-to-noise ratio (SNR) measurements with a priori tidal constituent data, our methodology enhances the accuracy and efficiency of water level monitoring. The paper begins with an introduction to the evolution of GNSS-R as a remote sensing tool, with a specific focus on its applications in water level monitoring. By analyzing SNR data, GNSS-R enables the measurement of water surfaces surrounding a GNSS receiver, making it a tool for a coastal monitoring. Our study addresses the primary objective of estimating water levels in near real-time, leveraging the GNSS-R technique. To achieve this, we apply a stochastic model to fuse GNSS SNR measurements with tidal constituents. This integration not only enhances the precision of water level estimations but also simplified the monitoring process. The outcomes of this research hold significant promise for a range of hydrological and environmental applications. By advancing the capabilities of GNSS-R in water level estimation, our stochastic approach contributes to more accurate and timely data for researchers and professionals in the GNSS-R field. Ultimately, this research lays the foundation for improved water resource management and informed decision-making in the face of evolving environmental challenges.
{"title":"A Stochastic Approach for Near Real-Time Estimation Using GNSS-Reflectometry","authors":"Kasidet Srisutha, Jihye Park","doi":"10.33012/2023.19310","DOIUrl":"https://doi.org/10.33012/2023.19310","url":null,"abstract":"Global Navigation Satellite System Reflectometry (GNSS-R) has established itself as a versatile remote sensing method applicable to various environmental monitoring tasks, including the water level measurements. In this paper, we propose a novel stochastic approach to estimate water levels in near real-time using GNSS-R. By integrating GNSS signal-to-noise ratio (SNR) measurements with a priori tidal constituent data, our methodology enhances the accuracy and efficiency of water level monitoring. The paper begins with an introduction to the evolution of GNSS-R as a remote sensing tool, with a specific focus on its applications in water level monitoring. By analyzing SNR data, GNSS-R enables the measurement of water surfaces surrounding a GNSS receiver, making it a tool for a coastal monitoring. Our study addresses the primary objective of estimating water levels in near real-time, leveraging the GNSS-R technique. To achieve this, we apply a stochastic model to fuse GNSS SNR measurements with tidal constituents. This integration not only enhances the precision of water level estimations but also simplified the monitoring process. The outcomes of this research hold significant promise for a range of hydrological and environmental applications. By advancing the capabilities of GNSS-R in water level estimation, our stochastic approach contributes to more accurate and timely data for researchers and professionals in the GNSS-R field. Ultimately, this research lays the foundation for improved water resource management and informed decision-making in the face of evolving environmental challenges.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"52 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":"135482210","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}
Siddharth S. Subramanyam, James L. Garrison, Patrick Smith, Yu Zhang, C.K. Shum
Remote sensing is crucial for our understanding of the Earth’s climate, water cycle, land, and atmosphere. Signals of Opportunity (SoOp) has recently emerged as an innovative method of microwave remote sensing that reutilizes existing, non-cooperative, satellite communication signals as sources of illumination in bistatic radar. Knowledge of the transmitter position is required both for georeferencing the specular point and for accurately estimation the path delay for altimetric observables. This paper describes an experiment using a network of receivers distributed over a continental-scale area to perform time delay of arrival (TDOA) measurements to solve for the position of a geosynchronous satellite transmitting in S-band (2.3 GHz) using a communication signal with a 2 MHz bandwidth. Synchronization was provided by commercial off the shelf (COTS) GNSS timing receivers and the network required only conventional virtual private network (VPN) connections over the internet for communications. A local experiment (in which the path delay errors between receivers can be assumed to cancel) was used to determine the observation error of 30 m. Kinematic solutions for the geosynchronous source were then produced from 6 receivers distributed across the United States in April, 2021. Comparison with two line elements (TLE’s) were within the error bounds predicted by a dilution of precision (DOP) analysis. Fundamental feasibility of this approach was demonstrated with an expected improvement in accuracy through use of wideband signals and statistical orbit determination methods.
{"title":"Time Delay of Arrival Based Orbit Determination of Geosynchronous Signals of Opportunity","authors":"Siddharth S. Subramanyam, James L. Garrison, Patrick Smith, Yu Zhang, C.K. Shum","doi":"10.33012/2023.19342","DOIUrl":"https://doi.org/10.33012/2023.19342","url":null,"abstract":"Remote sensing is crucial for our understanding of the Earth’s climate, water cycle, land, and atmosphere. Signals of Opportunity (SoOp) has recently emerged as an innovative method of microwave remote sensing that reutilizes existing, non-cooperative, satellite communication signals as sources of illumination in bistatic radar. Knowledge of the transmitter position is required both for georeferencing the specular point and for accurately estimation the path delay for altimetric observables. This paper describes an experiment using a network of receivers distributed over a continental-scale area to perform time delay of arrival (TDOA) measurements to solve for the position of a geosynchronous satellite transmitting in S-band (2.3 GHz) using a communication signal with a 2 MHz bandwidth. Synchronization was provided by commercial off the shelf (COTS) GNSS timing receivers and the network required only conventional virtual private network (VPN) connections over the internet for communications. A local experiment (in which the path delay errors between receivers can be assumed to cancel) was used to determine the observation error of 30 m. Kinematic solutions for the geosynchronous source were then produced from 6 receivers distributed across the United States in April, 2021. Comparison with two line elements (TLE’s) were within the error bounds predicted by a dilution of precision (DOP) analysis. Fundamental feasibility of this approach was demonstrated with an expected improvement in accuracy through use of wideband signals and statistical orbit determination methods.","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":"135482493","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}
Mathieu Hussong, Emile Ghizzo, Carl Milner, Axel Garcia-Pena, Julien Lesouple, Christophe Macabiau
. ABSTRACT This paper develops a classification to quantify the impact of a meaconer on an aircraft GNSS receiver, from the tracking loops up to the position estimation, during an SBAS-guided LPV-200 approach. Depending on the scenario, the impact of the meaconer on the estimated position can be catalogued for each GNSS signal at a given epoch into one of the four following situations - nominal, jamming, multipath or spoofing. In the nominal situation, the meaconer has no appreciable impact on the aircraft position. In the jamming situation, the meaconer induces higher noise levels resulting in greater errors in the position estimation than in the nominal situation. In the multipath situation, the meaconer effect on the position is similar to a GNSS multipath error. In the spoofing situation, the meaconer adds a deterministic bias on a subset of PRNs that can lead to significantly erroneous position estimations. Extensive simulations demonstrate that a meaconer with a high power and close to the aircraft trajectory can degrade the GNSS receiver performance and provoke faulty estimations of the aircraft position, which are not compliant with the civil aviation standards.
{"title":"Impact of Meaconers on Aircraft GNSS Receivers During Approaches","authors":"Mathieu Hussong, Emile Ghizzo, Carl Milner, Axel Garcia-Pena, Julien Lesouple, Christophe Macabiau","doi":"10.33012/2023.19423","DOIUrl":"https://doi.org/10.33012/2023.19423","url":null,"abstract":". ABSTRACT This paper develops a classification to quantify the impact of a meaconer on an aircraft GNSS receiver, from the tracking loops up to the position estimation, during an SBAS-guided LPV-200 approach. Depending on the scenario, the impact of the meaconer on the estimated position can be catalogued for each GNSS signal at a given epoch into one of the four following situations - nominal, jamming, multipath or spoofing. In the nominal situation, the meaconer has no appreciable impact on the aircraft position. In the jamming situation, the meaconer induces higher noise levels resulting in greater errors in the position estimation than in the nominal situation. In the multipath situation, the meaconer effect on the position is similar to a GNSS multipath error. In the spoofing situation, the meaconer adds a deterministic bias on a subset of PRNs that can lead to significantly erroneous position estimations. Extensive simulations demonstrate that a meaconer with a high power and close to the aircraft trajectory can degrade the GNSS receiver performance and provoke faulty estimations of the aircraft position, which are not compliant with the civil aviation standards.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"34 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":"135482496","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}
Naishu Yin, Di He, Yan Xiang, Wenxian Yu, Fusheng Zhu, Zhuoling Xiao
The GNSS positioning performance can be significantly degraded in urban canyon environments due to the multipath effect. However, the multipath error cannot be modeled or corrected accurately because it has no spatiotemporal correlation. Recently, machine learning models have shown the ability to learn potential relationships between data and are especially proficient in fitting nonlinear functions. Based on this statement, many researches regard the machine learning models as the most effective and promising tools to mitigate multipath errors, among which the most popular strategy is the classification of LOS (lineof-sight) and NLOS (non-line-of-sight) signals. Traditional machine learning models such as support vector machine (SVM), decision tree, deep learning models such as Multivariate long Short Term Memory-Fully Convolutional Network (MLSTMFCN) and Convolutional Neural Network (CNN) have been used to conduct relevant classification experiments. Pseudorange residuals, carrier-to-noise ratio and elevation are three most widely selected features as the input of models. However, the effectiveness of these features are rarely verified. In this paper, the most basic machine learning model SVM is used to classify LOS and NLOS signals and an average accuracy of 82.15% is achieved. The labels are given by a fisheye camera and six features are visualized and analyzed, which include carrier-to-noise ratio, elevation, azimuth in body frame, pseudorange residuals, pseudorange consistency and double differenced pseudorange. Finally, the kinetic single point positioning (SPP) with detected NLOS exclusion is conducted. The results reveal that, pseudorange residuals, as well as other features with the same distribution, may be unnecessary in LOS and NLOS classifications tasks. Also, the SPP positioning results reveal that NLOS exclusion is a useful and promising multipath mitigation strategy, although it is strongly dependent on considerable number of satellites. Compared with the original SPP method, the SVM-based NLOS exclusion achieves an accuracy improvement of 76.8%, 2.6% and 63.1% in east, north and up directions, respectively.
{"title":"Features Effectiveness Verification Using Machine-Learning-Based GNSS NLOS Signal Detection in Urban Canyon Environment","authors":"Naishu Yin, Di He, Yan Xiang, Wenxian Yu, Fusheng Zhu, Zhuoling Xiao","doi":"10.33012/2023.19363","DOIUrl":"https://doi.org/10.33012/2023.19363","url":null,"abstract":"The GNSS positioning performance can be significantly degraded in urban canyon environments due to the multipath effect. However, the multipath error cannot be modeled or corrected accurately because it has no spatiotemporal correlation. Recently, machine learning models have shown the ability to learn potential relationships between data and are especially proficient in fitting nonlinear functions. Based on this statement, many researches regard the machine learning models as the most effective and promising tools to mitigate multipath errors, among which the most popular strategy is the classification of LOS (lineof-sight) and NLOS (non-line-of-sight) signals. Traditional machine learning models such as support vector machine (SVM), decision tree, deep learning models such as Multivariate long Short Term Memory-Fully Convolutional Network (MLSTMFCN) and Convolutional Neural Network (CNN) have been used to conduct relevant classification experiments. Pseudorange residuals, carrier-to-noise ratio and elevation are three most widely selected features as the input of models. However, the effectiveness of these features are rarely verified. In this paper, the most basic machine learning model SVM is used to classify LOS and NLOS signals and an average accuracy of 82.15% is achieved. The labels are given by a fisheye camera and six features are visualized and analyzed, which include carrier-to-noise ratio, elevation, azimuth in body frame, pseudorange residuals, pseudorange consistency and double differenced pseudorange. Finally, the kinetic single point positioning (SPP) with detected NLOS exclusion is conducted. The results reveal that, pseudorange residuals, as well as other features with the same distribution, may be unnecessary in LOS and NLOS classifications tasks. Also, the SPP positioning results reveal that NLOS exclusion is a useful and promising multipath mitigation strategy, although it is strongly dependent on considerable number of satellites. Compared with the original SPP method, the SVM-based NLOS exclusion achieves an accuracy improvement of 76.8%, 2.6% and 63.1% in east, north and up directions, respectively.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"11 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":"135482773","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}
Various types of harmful Radio Frequency Interference (RFI) have become more prevalent in recent years in disrupting GNSS operations for commercial aviation resulting in threats to safety and significant economic impact to the industry. These newer RFI threats, which include both jamming and spoofing, were not anticipated when GNSS-based navigation was originally certified. Hence, new mitigation techniques are needed to address the new threat environment. In this paper a technique called Staggered Examination of Non-Trusted Receiver Information (SENTRI) will be presented. SENTRI uses the navigation-grade Inertial Reference System (IRS) units already on the aircraft together with GNSS information to detect if the GNSS receiver is being spoofed. The essential idea behind SENTRI is that the IRS solution is compared to the GNSS solution and if there is sufficient divergence between the two, a spoofer alert is indicated. The SENTRI algorithm uses multiple overlapping inertial-only solutions, which are periodically reset when no spoofer is detected, in order to detect a spoofer attack. Utilizing the statistics of the GNSS position and the IRS-only position solution a SENTRI detection test statistic is derived as well as horizontal and vertical Protection Levels (PLs). The PLs are computed using integrity optimized RAIM principles and the detailed mathematical development will be presented in this paper. Given the pre-detection PL values a world-wide availability analysis will also be presented. The operational and performance tradeoffs of using the SENTRI PLs in conjunction with legacy RAIM PLs and SBAS PLs to protect against spoofer attacks and single satellite faults will also be discussed. The paper will conclude with a summary of results and certification recommendations.
{"title":"Staggered Examination of Non-Trusted Receiver Information (SENTRI) Algorithm for Spoofer Detection and Integrity Monitoring in GNSS Receivers","authors":"Bernard A. Schnaufer, Angelo Joseph, Huan Phan","doi":"10.33012/2023.19250","DOIUrl":"https://doi.org/10.33012/2023.19250","url":null,"abstract":"Various types of harmful Radio Frequency Interference (RFI) have become more prevalent in recent years in disrupting GNSS operations for commercial aviation resulting in threats to safety and significant economic impact to the industry. These newer RFI threats, which include both jamming and spoofing, were not anticipated when GNSS-based navigation was originally certified. Hence, new mitigation techniques are needed to address the new threat environment. In this paper a technique called Staggered Examination of Non-Trusted Receiver Information (SENTRI) will be presented. SENTRI uses the navigation-grade Inertial Reference System (IRS) units already on the aircraft together with GNSS information to detect if the GNSS receiver is being spoofed. The essential idea behind SENTRI is that the IRS solution is compared to the GNSS solution and if there is sufficient divergence between the two, a spoofer alert is indicated. The SENTRI algorithm uses multiple overlapping inertial-only solutions, which are periodically reset when no spoofer is detected, in order to detect a spoofer attack. Utilizing the statistics of the GNSS position and the IRS-only position solution a SENTRI detection test statistic is derived as well as horizontal and vertical Protection Levels (PLs). The PLs are computed using integrity optimized RAIM principles and the detailed mathematical development will be presented in this paper. Given the pre-detection PL values a world-wide availability analysis will also be presented. The operational and performance tradeoffs of using the SENTRI PLs in conjunction with legacy RAIM PLs and SBAS PLs to protect against spoofer attacks and single satellite faults will also be discussed. The paper will conclude with a summary of results and certification recommendations.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"33 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":"135482776","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}
This paper shows the viability of improving tracking robustness of global navigation satellite systems (GNSS) pilot signals in high interference and/or jamming conditions by deep coupling with inertial sensors using a Kalman filter. In this work, we confront the limiting factors of typical tracking loops, including the dependency on pre-filtering or coherent averaging (Julien 2014), the adverse correlation effects that would otherwise come from integrating over the Doppler frequency of the incoming signals (Borio et al. 2014, Julien 2014), biased inertial measurement sensor (IMU) accelerometer/gyroscope noise inputs, and local oscillator (LO) phase noise (Misra and Enge 2001). Our deeply coupled Kalman filter is designed to specifically confront these limitations. The use of a deeply coupled Kalman filter also allows for a well-defined analysis of the integrity of the filter’s best state estimate, which can be used to expose noise sources which most quickly degrade estimate quality. Using this analysis, the robustness of this and similar estimators to all noise levels given all available hardware can be extended and defined, and thus provide a valuable asset not only to robustness, but also to estimator and sensing scheme design. We show that this early version of our tracking algorithm is able to maintain signal lock in carrier to noise density ratios as low as 4 dBHz.
研究了利用卡尔曼滤波器与惯性传感器进行深度耦合，提高全球导航卫星系统(GNSS)导频信号在高干扰和/或干扰条件下跟踪鲁棒性的可行性。在这项工作中，我们面对了典型跟踪回路的限制因素，包括对预滤波或相干平均的依赖(Julien 2014)、对输入信号的多普勒频率进行积分所产生的不利相关效应(Borio et al. 2014, Julien 2014)、偏置惯性测量传感器(IMU)加速度计/陀螺仪噪声输入以及本振(LO)相位噪声(Misra and Enge 2001)。我们的深度耦合卡尔曼滤波器就是专门针对这些限制而设计的。深度耦合卡尔曼滤波器的使用也允许对滤波器最佳状态估计的完整性进行明确定义的分析，这可以用来暴露最快速降低估计质量的噪声源。利用这种分析，可以扩展和定义该估计器和类似估计器在给定所有可用硬件的所有噪声水平下的鲁棒性，从而不仅为鲁棒性提供了宝贵的资产，而且还为估计器和传感方案设计提供了宝贵的资产。我们表明，这种早期版本的跟踪算法能够在载波与噪声密度比低至4 dBHz的情况下保持信号锁定。
{"title":"Improving Tracking Robustness Through Interference Using Pilot Signals with a Deeply Coupled Estimator","authors":"Logan Bednarz, Samer Khanafseh, Boris Pervan","doi":"10.33012/2023.19356","DOIUrl":"https://doi.org/10.33012/2023.19356","url":null,"abstract":"This paper shows the viability of improving tracking robustness of global navigation satellite systems (GNSS) pilot signals in high interference and/or jamming conditions by deep coupling with inertial sensors using a Kalman filter. In this work, we confront the limiting factors of typical tracking loops, including the dependency on pre-filtering or coherent averaging (Julien 2014), the adverse correlation effects that would otherwise come from integrating over the Doppler frequency of the incoming signals (Borio et al. 2014, Julien 2014), biased inertial measurement sensor (IMU) accelerometer/gyroscope noise inputs, and local oscillator (LO) phase noise (Misra and Enge 2001). Our deeply coupled Kalman filter is designed to specifically confront these limitations. The use of a deeply coupled Kalman filter also allows for a well-defined analysis of the integrity of the filter’s best state estimate, which can be used to expose noise sources which most quickly degrade estimate quality. Using this analysis, the robustness of this and similar estimators to all noise levels given all available hardware can be extended and defined, and thus provide a valuable asset not only to robustness, but also to estimator and sensing scheme design. We show that this early version of our tracking algorithm is able to maintain signal lock in carrier to noise density ratios as low as 4 dBHz.","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":"135482779","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 objective of this paper is to contribute to the feasibility and trade-off analysis of the different proposed architectures for future dual-frequency multi-constellation ground-based augmentation system (DFMC GBAS). The Authors’ aim is to provide an overall picture of the minimum operational performance capabilities of current and experimental future GBAS approach service types (GASTs), willing to assist the relevant standardization groups in consolidating the DFMC GBAS concept for the development of a future standard. In this paper we present the evaluation of the performances for legacy and experimental DFMC GBAS modes. The accuracy and integrity evaluations are based on ground and airborne data that was collected during flight trials of DREAMS project in the framework of single European sky ATM research (SESAR). We compare the errors and the nominal protection levels of the SESAR proposed processing modes taking as baseline the ones provided by legacy GBAS approach service types such as GAST-C and GAST-D. This work provides elements to verify and validate that in proposed DFMC GBAS architectures the ground and airborne sub-system segments are mutually and correctly interfaced with both GNSS constellations (GPS and GALILEO) and the proposed VHF data broadcast (VDB) link format, addressing the end user needs with respect the proposed requirements.
{"title":"DFMC GBAS Processing of Flight Trial Data – A First Comparison of Options","authors":"Natali Caccioppoli, David Duchet, Andreas Lipp","doi":"10.33012/2023.19477","DOIUrl":"https://doi.org/10.33012/2023.19477","url":null,"abstract":"The objective of this paper is to contribute to the feasibility and trade-off analysis of the different proposed architectures for future dual-frequency multi-constellation ground-based augmentation system (DFMC GBAS). The Authors’ aim is to provide an overall picture of the minimum operational performance capabilities of current and experimental future GBAS approach service types (GASTs), willing to assist the relevant standardization groups in consolidating the DFMC GBAS concept for the development of a future standard. In this paper we present the evaluation of the performances for legacy and experimental DFMC GBAS modes. The accuracy and integrity evaluations are based on ground and airborne data that was collected during flight trials of DREAMS project in the framework of single European sky ATM research (SESAR). We compare the errors and the nominal protection levels of the SESAR proposed processing modes taking as baseline the ones provided by legacy GBAS approach service types such as GAST-C and GAST-D. This work provides elements to verify and validate that in proposed DFMC GBAS architectures the ground and airborne sub-system segments are mutually and correctly interfaced with both GNSS constellations (GPS and GALILEO) and the proposed VHF data broadcast (VDB) link format, addressing the end user needs with respect the proposed requirements.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"301 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":"135482978","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}
Zelin Zhou, Dennis Stefanakis, Baoyu Liu, Hongzhou Yang
Global Navigation Satellite System (GNSS) positioning performance in challenging environments such as urban canyon or indoor environments suffer significant degradations, where frequent none-line-of-sight (NLOS) signals and multipath significantly lower the GNSS positioning accuracy. Consequently, the mitigation of NLOS and multipath effects is important to achieve accurate positioning results. To mitigate the effects from NLOS and multipath signals, the accurate classification of GNSS signal types is required. Recently, the GNSS signal reception classifiers based on deep learning models are drawing more attention due to higher accuracy, better efficiency, and greater convenience. In this paper, a Hopular-based deep learning model is proposed for post-processing GNSS signal classification applications using four GNSS features derived from the raw GNSS measurements: Carrier-to-noise ratio (C/N0), Time-differenced Code-Minus-Carrier (time-differenced CMC), Loss of Lock Indicator (LLI) and Satellites-To-Receiver elevation. The raw GNSS measurements are collected at the two separate locations (Location A & B) under the urban canyon environment in Calgary downtown, using a u-blox ZED F9P receiver. Each measurement is accurately labeled as either line-of-sight (LOS) or NLOS measurement, using a precisely calibrated omnidirectional fish-eye camera with a 360-degree field-of-view lens. Both multi-features and single-feature tests are conducted to evaluate the performance of the Hopular-based model; and their results are compared to another two state-of-the-art machine learning models: Support Vector Machine (SVM) and Gradient Boost Machine (GBM). The trained Hopular-based deep learning model provides a 89.80% and 96.75% classification accuracy of LOS/NLOS signals using all four GNSS features, for dataset A and dataset B respectively. Where the classification accuracy of SVM and GBM models are only 82.66% and 83.71% for dataset A; 80.19% and 82.10% for dataset B. For the dataset A and B, the Hopular-based model has improved 6.09% and 14.65% classification accuracy compared to using GBMs; and 7.14% and 16.56% compared to using SVMs.
{"title":"Hopular-Based GNSS Signal Reception Classification Method for LOS/NLOS Detection in Urban Environments","authors":"Zelin Zhou, Dennis Stefanakis, Baoyu Liu, Hongzhou Yang","doi":"10.33012/2023.19358","DOIUrl":"https://doi.org/10.33012/2023.19358","url":null,"abstract":"Global Navigation Satellite System (GNSS) positioning performance in challenging environments such as urban canyon or indoor environments suffer significant degradations, where frequent none-line-of-sight (NLOS) signals and multipath significantly lower the GNSS positioning accuracy. Consequently, the mitigation of NLOS and multipath effects is important to achieve accurate positioning results. To mitigate the effects from NLOS and multipath signals, the accurate classification of GNSS signal types is required. Recently, the GNSS signal reception classifiers based on deep learning models are drawing more attention due to higher accuracy, better efficiency, and greater convenience. In this paper, a Hopular-based deep learning model is proposed for post-processing GNSS signal classification applications using four GNSS features derived from the raw GNSS measurements: Carrier-to-noise ratio (C/N0), Time-differenced Code-Minus-Carrier (time-differenced CMC), Loss of Lock Indicator (LLI) and Satellites-To-Receiver elevation. The raw GNSS measurements are collected at the two separate locations (Location A & B) under the urban canyon environment in Calgary downtown, using a u-blox ZED F9P receiver. Each measurement is accurately labeled as either line-of-sight (LOS) or NLOS measurement, using a precisely calibrated omnidirectional fish-eye camera with a 360-degree field-of-view lens. Both multi-features and single-feature tests are conducted to evaluate the performance of the Hopular-based model; and their results are compared to another two state-of-the-art machine learning models: Support Vector Machine (SVM) and Gradient Boost Machine (GBM). The trained Hopular-based deep learning model provides a 89.80% and 96.75% classification accuracy of LOS/NLOS signals using all four GNSS features, for dataset A and dataset B respectively. Where the classification accuracy of SVM and GBM models are only 82.66% and 83.71% for dataset A; 80.19% and 82.10% for dataset B. For the dataset A and B, the Hopular-based model has improved 6.09% and 14.65% classification accuracy compared to using GBMs; and 7.14% and 16.56% compared to using SVMs.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"22 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":"135483163","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}
Malfunctions or failures in Global Navigation Satellite System (GNSS) services can result in significant personal, material, and financial damages. By an early identification of anomalous behavior in GNSS signals, timely countermeasures can be taken. However, most of interference monitoring or mitigation techniques are only applicable with the use of high-end receivers and require a certain level of knowledge to be used effectively. This paper presents a GNSS interference monitoring approach employing machine learning methodologies that can be utilized by users of any expertise level and with any type of GNSS receiver capable of outputting raw GNSS observations. By leveraging simple signal-to-noise ratio (SNR) observations, different hybrid autoencoder models, including denoising or variational autoencoder combined with recurrent neural network (RNN) models, are trained and tested on real jamming and spoofing events. The developed monitoring system is represented by a “traffic-lights” system, indicating the severity or level of concern associated with each detected anomaly. The results contain a comparison between different RNN-based autoencoder implementations and have been tested on input data from high-end to low-end GNSS receivers. The analysis of the test set showed that there is a 95% probability of catching anomalies. Additionally, when applied to other geodetic receiver types like u-blox or Javad GNSS receivers, similar results were achieved. However, smartphone data is subject to some limitations. Notably, missed anomalies are primarily attributed to the low transmitting power from the jamming and spoofing devices, which poses challenges for detection.
{"title":"Hybrid Autoencoder for Interference Detection in Raw GNSS Observations","authors":"Karin Mascher, Stefan Laller, Philipp Berglez","doi":"10.33012/2023.19369","DOIUrl":"https://doi.org/10.33012/2023.19369","url":null,"abstract":"Malfunctions or failures in Global Navigation Satellite System (GNSS) services can result in significant personal, material, and financial damages. By an early identification of anomalous behavior in GNSS signals, timely countermeasures can be taken. However, most of interference monitoring or mitigation techniques are only applicable with the use of high-end receivers and require a certain level of knowledge to be used effectively. This paper presents a GNSS interference monitoring approach employing machine learning methodologies that can be utilized by users of any expertise level and with any type of GNSS receiver capable of outputting raw GNSS observations. By leveraging simple signal-to-noise ratio (SNR) observations, different hybrid autoencoder models, including denoising or variational autoencoder combined with recurrent neural network (RNN) models, are trained and tested on real jamming and spoofing events. The developed monitoring system is represented by a “traffic-lights” system, indicating the severity or level of concern associated with each detected anomaly. The results contain a comparison between different RNN-based autoencoder implementations and have been tested on input data from high-end to low-end GNSS receivers. The analysis of the test set showed that there is a 95% probability of catching anomalies. Additionally, when applied to other geodetic receiver types like u-blox or Javad GNSS receivers, similar results were achieved. However, smartphone data is subject to some limitations. Notably, missed anomalies are primarily attributed to the low transmitting power from the jamming and spoofing devices, which poses challenges for detection.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"37 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":"135483231","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}
Better than one meter measurement precision of linearized GNSS pseudoranges requires fine-time: defined as a system time error less than one millisecond. Without fine-time, the linearized pseudorange measurement incurs a computation error equivalent to radial component of the satellite motion between the true and estimated transmission time. This error is referred to as fictitious measurement error and it is additive to the thermal, atmospheric, and multipath errors imposed the code-phase measurements. This condition occurs commonly in mass market receivers that produce a rapid first position fix with a coarse-time solver before a GNSS receiver is able to decode satellite time from the navigation message. This paper presents a method of fusing the coarse-time solver with additional time information available in the physical layer of modernized signals: namely the difference between the measured and predicted secondary code-phase. These time sources provide fine-time but have an ambiguity equal to the length of the secondary code. The method identifies the most likely rounded time estimates among a set of candidate times as the solution with the lowest posterior residuals. With long secondary codes, the fictitious measurement error will dominate at the wrong candidates. Large measurement errors prevent identifying a clear minimum in the posterior residuals across the candidate solutions. An outlier detection and mitigation method are required to remove the larger measurement errors.
{"title":"Fast Time to Fine Time Method to Improve First Fix Accuracy with Modernized Signals in Urban Canyons","authors":"Paul McBurney","doi":"10.33012/2023.19420","DOIUrl":"https://doi.org/10.33012/2023.19420","url":null,"abstract":"Better than one meter measurement precision of linearized GNSS pseudoranges requires fine-time: defined as a system time error less than one millisecond. Without fine-time, the linearized pseudorange measurement incurs a computation error equivalent to radial component of the satellite motion between the true and estimated transmission time. This error is referred to as fictitious measurement error and it is additive to the thermal, atmospheric, and multipath errors imposed the code-phase measurements. This condition occurs commonly in mass market receivers that produce a rapid first position fix with a coarse-time solver before a GNSS receiver is able to decode satellite time from the navigation message. This paper presents a method of fusing the coarse-time solver with additional time information available in the physical layer of modernized signals: namely the difference between the measured and predicted secondary code-phase. These time sources provide fine-time but have an ambiguity equal to the length of the secondary code. The method identifies the most likely rounded time estimates among a set of candidate times as the solution with the lowest posterior residuals. With long secondary codes, the fictitious measurement error will dominate at the wrong candidates. Large measurement errors prevent identifying a clear minimum in the posterior residuals across the candidate solutions. An outlier detection and mitigation method are required to remove the larger measurement errors.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"12 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":"135483277","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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