Global Navigation Satellite Systems (GNSS) today face challenges such as signal path loss, interference, jamming, and timing inaccuracy in receivers. These challenges can be mitigated and GNSS systems can be modernized by the development of Low Earth Orbit (LEO) GNSS satellites that transmit signals on much higher frequencies and with much wider bandwidths. In this paper, we assess the feasibility of these changes from the point of view of signal acquisition. We investigate the challenges to acquisition that arise due to these changes, and we find that the most significant challenge is a dramatic and potentially prohibitive increase in acquisition time. We then attempt to use computational methods to reduce acquisition time. The Galileo E5 AltBOC(15,10) signal is used as a model wide bandwidth signal, and one satellite transmitting this signal is acquired from a live-sky observation data set using traditional acquisition techniques. To improve acquisition time, we implement a circular frequency shift algorithm, and we run the tested acquisition algorithms on a graphics processing unit (GPU). We attempt AltBOC signal acquisition with both techniques independently and together, and we find that when used together, acquisition time can be reduced by about 40%. Because acquisition time can be reduced, we conclude that acquisition is adaptable to the proposed changes to GNSS systems and thus, they are feasible from this point of view. We also attempt to create a computational complexity model to understand the complexity of acquisition in terms of computer operations and how time reduction techniques might also reduce computational complexity. We develop a model and find that it accurately represents the computational complexity and time of acquisition on a central processing unit (CPU) but does not accurately represent the computation time on a GPU, because it does not capture the high efficiency of the GPU.
{"title":"Future GNSS Acquisition Strategies and Algorithms","authors":"Anna Cismaru, Nicholas Spens, Dennis M. Akos","doi":"10.33012/2023.19232","DOIUrl":"https://doi.org/10.33012/2023.19232","url":null,"abstract":"Global Navigation Satellite Systems (GNSS) today face challenges such as signal path loss, interference, jamming, and timing inaccuracy in receivers. These challenges can be mitigated and GNSS systems can be modernized by the development of Low Earth Orbit (LEO) GNSS satellites that transmit signals on much higher frequencies and with much wider bandwidths. In this paper, we assess the feasibility of these changes from the point of view of signal acquisition. We investigate the challenges to acquisition that arise due to these changes, and we find that the most significant challenge is a dramatic and potentially prohibitive increase in acquisition time. We then attempt to use computational methods to reduce acquisition time. The Galileo E5 AltBOC(15,10) signal is used as a model wide bandwidth signal, and one satellite transmitting this signal is acquired from a live-sky observation data set using traditional acquisition techniques. To improve acquisition time, we implement a circular frequency shift algorithm, and we run the tested acquisition algorithms on a graphics processing unit (GPU). We attempt AltBOC signal acquisition with both techniques independently and together, and we find that when used together, acquisition time can be reduced by about 40%. Because acquisition time can be reduced, we conclude that acquisition is adaptable to the proposed changes to GNSS systems and thus, they are feasible from this point of view. We also attempt to create a computational complexity model to understand the complexity of acquisition in terms of computer operations and how time reduction techniques might also reduce computational complexity. We develop a model and find that it accurately represents the computational complexity and time of acquisition on a central processing unit (CPU) but does not accurately represent the computation time on a GPU, because it does not capture the high efficiency of the GPU.","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":"135483281","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 development of re-entry – and hypersonic - vehicles calls for an accurate knowledge of the surrounding aero-thermodynamic field, which is strongly modified by ionization processes also responsible for the communications black-out. This paper aims to introduce a novel technique to study the plasma layer surrounding the vehicle by means of the radio-frequency signals subject to the black out during some time intervals of the descent. Signals to be considered are the ones transmitted from GNSS sources, nowadays in a large number, with stable characteristics and above all impinging on the vehicle from well-known and sparse directions. It would be possible to track these signals all along the descent, until their disappearance and then since their return after the black-out phase, to infer the properties of the ionized flow surrounding the re-entry vehicle. Such a tracking could be conveniently accomplished by sampling and recording onboard the signals received by a set of antennas, ideally providing an almost spherical coverage all around the vehicle, and then performing a detailed post-flight analysis, combined with flight data, by means of a software receiver to detect the captured or disappearing signals and evaluate their attenuation. Notice that, due to the limited request of onboard equipment, and to the likely availability of GNSS receivers in modern re-entry vehicle, the implementation of the technique looks not especially difficult nor expensive. The concept, fully original in the knowledge of the authors, is presented in the paper, together with a very preliminary example. While it is likely that this technique cannot fully substitute complex and time expensive aero-thermodynamic simulations, the exercise shows its possible usefulness in complementing and validating the numerical analyses.
{"title":"A GNSS–Based Technique to Investigate the Black-Out During Space Vehicles’ Re-Entry","authors":"Giovanni B. Palmerini, Prakriti Kapilavai","doi":"10.33012/2023.19183","DOIUrl":"https://doi.org/10.33012/2023.19183","url":null,"abstract":"The development of re-entry – and hypersonic - vehicles calls for an accurate knowledge of the surrounding aero-thermodynamic field, which is strongly modified by ionization processes also responsible for the communications black-out. This paper aims to introduce a novel technique to study the plasma layer surrounding the vehicle by means of the radio-frequency signals subject to the black out during some time intervals of the descent. Signals to be considered are the ones transmitted from GNSS sources, nowadays in a large number, with stable characteristics and above all impinging on the vehicle from well-known and sparse directions. It would be possible to track these signals all along the descent, until their disappearance and then since their return after the black-out phase, to infer the properties of the ionized flow surrounding the re-entry vehicle. Such a tracking could be conveniently accomplished by sampling and recording onboard the signals received by a set of antennas, ideally providing an almost spherical coverage all around the vehicle, and then performing a detailed post-flight analysis, combined with flight data, by means of a software receiver to detect the captured or disappearing signals and evaluate their attenuation. Notice that, due to the limited request of onboard equipment, and to the likely availability of GNSS receivers in modern re-entry vehicle, the implementation of the technique looks not especially difficult nor expensive. The concept, fully original in the knowledge of the authors, is presented in the paper, together with a very preliminary example. While it is likely that this technique cannot fully substitute complex and time expensive aero-thermodynamic simulations, the exercise shows its possible usefulness in complementing and validating the numerical analyses.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"439 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":"135483290","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}
Global navigation satellite systems (GNSS) have played an important role in space stations, aviation, maritime and mass transit. One of the main disadvantages of GNSS is their vulnerability to spoofing. A successful spoofing attack can have serious consequences. In regards to this issue, our method of GNSS spoofing detection based on radio frequency fingerprint (RFF) is considered a promising technology. Due to manufacturing defects, even GNSS transmitters of the same model exhibit subtle differences known as RFF, which possess uniqueness and persistence, and can be considered as the DNA of GNSS transmitters. Our method autonomously extracts the RFF from the received signals by exploiting deep learning, which avoids the laborious manual feature selection process compared to other methods. The time-frequency representation of the signal is used as input to the deep learning. We evaluate Shorttime Fourier Transform (STFT) time-frequency representation method. We explore the possibility of using the Support Vector Data Description (SVDD) for GNSS spoofing detection. We evaluate two deep learning-based GNSS signal classification methods. One is RFF identification based on the original signal, namely IQ+CNN in this article, which preprocesses the collected IQ samples and directly inputs them into the deep learning model for training and classification. This method completely uses the deep learning model to learn the physical layer characteristics of wireless signal. The second is RFF identification based on two-dimensional representation of signals, namely STFT+RESNET50 in this article, which aims to extract RFF in the time-frequency domain. The experimental dataset is generated by software, and we compare the classification accuracy of the two methods at different SNRs. The experiments show that our method is reasonable for GNSS spoofing detection. In addition, the research of RFF-based GNSS spoofing detection is still in its infancy, and we promote the development of this field.
{"title":"A Robust RF Fingerprint Extraction Scheme for GNSS Spoofing Detection","authors":"Chengjun Guo, Zhongpei Yang","doi":"10.33012/2023.19302","DOIUrl":"https://doi.org/10.33012/2023.19302","url":null,"abstract":"Global navigation satellite systems (GNSS) have played an important role in space stations, aviation, maritime and mass transit. One of the main disadvantages of GNSS is their vulnerability to spoofing. A successful spoofing attack can have serious consequences. In regards to this issue, our method of GNSS spoofing detection based on radio frequency fingerprint (RFF) is considered a promising technology. Due to manufacturing defects, even GNSS transmitters of the same model exhibit subtle differences known as RFF, which possess uniqueness and persistence, and can be considered as the DNA of GNSS transmitters. Our method autonomously extracts the RFF from the received signals by exploiting deep learning, which avoids the laborious manual feature selection process compared to other methods. The time-frequency representation of the signal is used as input to the deep learning. We evaluate Shorttime Fourier Transform (STFT) time-frequency representation method. We explore the possibility of using the Support Vector Data Description (SVDD) for GNSS spoofing detection. We evaluate two deep learning-based GNSS signal classification methods. One is RFF identification based on the original signal, namely IQ+CNN in this article, which preprocesses the collected IQ samples and directly inputs them into the deep learning model for training and classification. This method completely uses the deep learning model to learn the physical layer characteristics of wireless signal. The second is RFF identification based on two-dimensional representation of signals, namely STFT+RESNET50 in this article, which aims to extract RFF in the time-frequency domain. The experimental dataset is generated by software, and we compare the classification accuracy of the two methods at different SNRs. The experiments show that our method is reasonable for GNSS spoofing detection. In addition, the research of RFF-based GNSS spoofing detection is still in its infancy, and we promote the development of this field.","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":"135483395","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}
Munther A. Hassouneh, Darren Midkiff, Luke M.B. Winternitz, Samuel R. Price, Luke Thomas, David Hatke, Tyler Lee, William Bamford, Jason W. Mitchell
This paper describes development and testing of NASA Goddard Space Flight Center’s new NavCube3-mini (NC3m) spaceborne, weak-signal GNSS receiver, which targets all Earth orbit regimes with special focus on lunar applications. NC3m derives from the ground-breaking Magnetospheric Multiscale (MMS) mission Navigator GPS receiver. The MMS-Navigator (launched 2015) holds a Guinness World Record for highest altitude GPS fix and is currently in a highly elliptic orbit with a 29 Earth radii apogee, nearly half lunar distance. NC3m has reduced size, weight, and power compared to the MMS-Navigator, making it suitable for smallsat applications, and adds multi-frequency and multi-GNSS capabilities, among other improvements. A NC3m engineering test unit was subjected to and successfully completed a comprehensive Technology Readiness Level 6 (system/subsystem model or prototype demonstration in a relevant environment) testing campaign in the second half of 2022. This involved high-fidelity simulations in LEO, GEO, and lunar regimes and full environmental testing, including vibration, thermal vacuum, and electromagnetic compatibility. An overview of the NC3m receiver, the test setup, and a sample of results is presented. The results include predicted performance at high-altitude and in the lunar regime.
{"title":"NavCube3-mini Lunar GNSS Receiver","authors":"Munther A. Hassouneh, Darren Midkiff, Luke M.B. Winternitz, Samuel R. Price, Luke Thomas, David Hatke, Tyler Lee, William Bamford, Jason W. Mitchell","doi":"10.33012/2023.19343","DOIUrl":"https://doi.org/10.33012/2023.19343","url":null,"abstract":"This paper describes development and testing of NASA Goddard Space Flight Center’s new NavCube3-mini (NC3m) spaceborne, weak-signal GNSS receiver, which targets all Earth orbit regimes with special focus on lunar applications. NC3m derives from the ground-breaking Magnetospheric Multiscale (MMS) mission Navigator GPS receiver. The MMS-Navigator (launched 2015) holds a Guinness World Record for highest altitude GPS fix and is currently in a highly elliptic orbit with a 29 Earth radii apogee, nearly half lunar distance. NC3m has reduced size, weight, and power compared to the MMS-Navigator, making it suitable for smallsat applications, and adds multi-frequency and multi-GNSS capabilities, among other improvements. A NC3m engineering test unit was subjected to and successfully completed a comprehensive Technology Readiness Level 6 (system/subsystem model or prototype demonstration in a relevant environment) testing campaign in the second half of 2022. This involved high-fidelity simulations in LEO, GEO, and lunar regimes and full environmental testing, including vibration, thermal vacuum, and electromagnetic compatibility. An overview of the NC3m receiver, the test setup, and a sample of results is presented. The results include predicted performance at high-altitude and in the lunar regime.","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":"135483462","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}
Inverse mechanization converts position, velocity, and attitude (pose) data into inertial measurement unit sensor data (specific forces and rotation rates). It removes the need for expensive, real-world flights just to get reasonable sensor recordings for inertial navigation simulations. This can be helpful when real pose data is available but no inertial sensor data is included. Actually, the pose data itself could be synthetic. The researcher can then use this estimated sensor data to forward mechanize and get pose data, which should exactly match the original pose data. After generating the sensor data, simulated sensor noise could be added to improve realism, but it is essential that the inverse and forward mechanization algorithms themselves do not add any additional noise because of a lack of duality; they should be perfectly consistent with each other. This tutorial details the set of equations for inverse and forward mechanization. It also shows how to calculate velocity information from position information and how to estimate attitude information from velocities. As a demonstration of the accuracy of the equations, real-world pose and sensor data are used as inputs to the algorithms and the outputs are compared.
{"title":"Tutorial on Inverse Mechanization","authors":"David Woodburn","doi":"10.33012/2023.19180","DOIUrl":"https://doi.org/10.33012/2023.19180","url":null,"abstract":"Inverse mechanization converts position, velocity, and attitude (pose) data into inertial measurement unit sensor data (specific forces and rotation rates). It removes the need for expensive, real-world flights just to get reasonable sensor recordings for inertial navigation simulations. This can be helpful when real pose data is available but no inertial sensor data is included. Actually, the pose data itself could be synthetic. The researcher can then use this estimated sensor data to forward mechanize and get pose data, which should exactly match the original pose data. After generating the sensor data, simulated sensor noise could be added to improve realism, but it is essential that the inverse and forward mechanization algorithms themselves do not add any additional noise because of a lack of duality; they should be perfectly consistent with each other. This tutorial details the set of equations for inverse and forward mechanization. It also shows how to calculate velocity information from position information and how to estimate attitude information from velocities. As a demonstration of the accuracy of the equations, real-world pose and sensor data are used as inputs to the algorithms and the outputs are compared.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"123 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":"135483543","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}
Shuo Li, Maxim Mikhaylov, Nikolay Mikhaylov, Thomas Pany
The integration of global navigation satellite system (GNSS) and inertial navigation system (INS) is a powerful technology that provides accurate, available, and continuous navigation solutions, which is critical for autonomous vehicles (Mikhaylov et al., 2020). Due to the advancements in micro-electromechanical system (MEMS) inertial sensor technology, the use of low-cost, small size, and low power consumption MEMS inertial measurement units (IMU) becomes attractive for land vehicles (Li et al., 2019; Yang et al., 2014). However, the INS cannot operate stand-alone to provide long-term accuracy in the GNSS challenging environments because the errors in the IMU measurements are integrated into the navigation solutions (Woodman, 2007). The accumulated errors and the IMU measurement errors are usually estimated by an error-state extended Kalman filter (ES-EKF) (Madyastha et al., 2011). The performance of the integration algorithm is highly dependent on the knowledge of noise statistics and system models. The noise covariance matrices are formulated empirically under independent Gaussian noise assumptions whereas the system models are designed by linearizing the nonlinear equations of the system. Considering the highly nonlinear error propagation and the complex IMU error model of low-cost MEMS IMU, the ES-EKF based GNSS/INS integration is not sufficient for meeting the navigation requirements of land vehicles. In order to address the nonlinear issue, several advanced integration algorithms are utilized such as unscented Kalman filter (Meng et al., 2016), cubature Kalman filter (Cui et al., 2017) and factor graph (Wen et al., 2021). An alternative approach is to estimate other IMU error components other than bias (Godha, 2006). Despite advancements, these algorithms are still unable to optimally address nonlinear issues or require significant computational loads. On the other hand, external sensors such as odometer, lidar, and camera can be integrated into the system to improve the performance by providing additional measurements (Chiang et al., 2019). The use of auxiliary sensors could limit the application areas and increase costs. Given the remarkable success of deep learning (DL) in various fields and the impressive learning capability of deep neural networks (DNN) (LeCun et al., 2015), we present a DL-assisted integration algorithm in this paper.
全球导航卫星系统(GNSS)和惯性导航系统(INS)的集成是一项强大的技术，可提供准确、可用和连续的导航解决方案，这对自动驾驶汽车至关重要(Mikhaylov等人，2020)。由于微机电系统(MEMS)惯性传感器技术的进步，使用低成本、小尺寸和低功耗的MEMS惯性测量单元(IMU)对陆地车辆具有吸引力(Li et al.， 2019;Yang等人，2014)。然而，惯性导航系统无法在具有挑战性的GNSS环境中独立运行以提供长期精度，因为IMU测量中的误差被集成到导航解决方案中(Woodman, 2007)。累积误差和IMU测量误差通常由误差状态扩展卡尔曼滤波器(ES-EKF)估计(Madyastha等，2011)。积分算法的性能高度依赖于噪声统计和系统模型的知识。噪声协方差矩阵是在独立高斯噪声假设下的经验表达式，系统模型是通过对系统的非线性方程进行线性化来设计的。考虑到低成本MEMS IMU的高度非线性误差传播和复杂的IMU误差模型，基于ES-EKF的GNSS/INS集成不足以满足陆地车辆的导航要求。为了解决非线性问题，使用了几种先进的集成算法，如unscented卡尔曼滤波器(Meng等人，2016)，cubature卡尔曼滤波器(Cui等人，2017)和因子图(Wen等人，2021)。另一种方法是估计除偏差以外的其他IMU误差成分(Godha, 2006)。尽管取得了进步，但这些算法仍然无法最优地解决非线性问题或需要大量的计算负载。另一方面，可以将里程表、激光雷达和摄像头等外部传感器集成到系统中，通过提供额外的测量来提高性能(Chiang et al.， 2019)。使用辅助传感器会限制应用领域，增加成本。鉴于深度学习(DL)在各个领域的显著成功以及深度神经网络(DNN)令人印象深刻的学习能力(LeCun et al.， 2015)，我们在本文中提出了一种DL辅助集成算法。
{"title":"First Real-World Results of a Deep Neural Network Assisted GNSS/INS Kalman-Filter with MEMS Inertial Sensors for Autonomous Vehicle","authors":"Shuo Li, Maxim Mikhaylov, Nikolay Mikhaylov, Thomas Pany","doi":"10.33012/2023.19301","DOIUrl":"https://doi.org/10.33012/2023.19301","url":null,"abstract":"The integration of global navigation satellite system (GNSS) and inertial navigation system (INS) is a powerful technology that provides accurate, available, and continuous navigation solutions, which is critical for autonomous vehicles (Mikhaylov et al., 2020). Due to the advancements in micro-electromechanical system (MEMS) inertial sensor technology, the use of low-cost, small size, and low power consumption MEMS inertial measurement units (IMU) becomes attractive for land vehicles (Li et al., 2019; Yang et al., 2014). However, the INS cannot operate stand-alone to provide long-term accuracy in the GNSS challenging environments because the errors in the IMU measurements are integrated into the navigation solutions (Woodman, 2007). The accumulated errors and the IMU measurement errors are usually estimated by an error-state extended Kalman filter (ES-EKF) (Madyastha et al., 2011). The performance of the integration algorithm is highly dependent on the knowledge of noise statistics and system models. The noise covariance matrices are formulated empirically under independent Gaussian noise assumptions whereas the system models are designed by linearizing the nonlinear equations of the system. Considering the highly nonlinear error propagation and the complex IMU error model of low-cost MEMS IMU, the ES-EKF based GNSS/INS integration is not sufficient for meeting the navigation requirements of land vehicles. In order to address the nonlinear issue, several advanced integration algorithms are utilized such as unscented Kalman filter (Meng et al., 2016), cubature Kalman filter (Cui et al., 2017) and factor graph (Wen et al., 2021). An alternative approach is to estimate other IMU error components other than bias (Godha, 2006). Despite advancements, these algorithms are still unable to optimally address nonlinear issues or require significant computational loads. On the other hand, external sensors such as odometer, lidar, and camera can be integrated into the system to improve the performance by providing additional measurements (Chiang et al., 2019). The use of auxiliary sensors could limit the application areas and increase costs. Given the remarkable success of deep learning (DL) in various fields and the impressive learning capability of deep neural networks (DNN) (LeCun et al., 2015), we present a DL-assisted integration algorithm in this paper.","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":"135483544","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}
Current GNSS receivers mainly use classical third order tracking loops which are well documented in the literature. However, there is a coming need for fourth order loops to track the signals of future navigation systems that investigate the use of low orbit satellites and frequency bands higher than the L band. Unfortunately, there is a few literatures dealing with the fourth order loops implementation. This paper proposes to investigate and solve the different issues arising when implementing a high order tracking loop.
{"title":"High Order DPLL for High Order Doppler Dynamics Tracking","authors":"Sébastien Roche","doi":"10.33012/2023.19264","DOIUrl":"https://doi.org/10.33012/2023.19264","url":null,"abstract":"Current GNSS receivers mainly use classical third order tracking loops which are well documented in the literature. However, there is a coming need for fourth order loops to track the signals of future navigation systems that investigate the use of low orbit satellites and frequency bands higher than the L band. Unfortunately, there is a few literatures dealing with the fourth order loops implementation. This paper proposes to investigate and solve the different issues arising when implementing a high order tracking loop.","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":"135483607","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}
Natural Phenomena such as earthquakes, volcanic eruptions, solar storms cause variation of Total Electron Contents (TEC), and Global Navigation Satellite System (GNSS) has been used to monitor these ionospheric variations. Especially, Rate of TEC Index (ROTI) which can be derived from geometry-free combination of signals and S_4, sigma_phi indices that denotes scintillation of signal have been used to detect ionospheric irregularities. However, ROTI shows different calculation result with respect to different sampling rate and averaging time interval, and S_4,sigma_phi indices are unsuitable for small and fast-moving ionospheric irregularities. Thus, in this paper, we suggest a method of detecting ionospheric irregularities due to earthquakes using ROT and have studied the method of estimating epicenter of the earthquakes. ROT fluctuations and observed at different times depending on the location of the satellite and the reference stations. By estimating this time difference using Inter-Station Cross Correlation, it is possible to estimate the speed of ionospheric irregularities. The location of the epicenter can be estimated using the geometric relationship between the reference stations and the Ionospheric Pierce Point (IPP) where ROT fluctuation had occurred. Algorithm of epicenter estimation was analyzed based on actual data, and we had proposed the fields where these methods are applicable.
{"title":"Ionospheric Irregularities Signature Correlation on ROT Variation for Earthquake Detection and Epicenter Estimation: Case Study of Tohoku (2011) & Turkey-Syria (2023) Earthquakes","authors":"Minhyoung Cho, Jeonghyeon Yun, Byungwoon Park","doi":"10.33012/2023.19405","DOIUrl":"https://doi.org/10.33012/2023.19405","url":null,"abstract":"Natural Phenomena such as earthquakes, volcanic eruptions, solar storms cause variation of Total Electron Contents (TEC), and Global Navigation Satellite System (GNSS) has been used to monitor these ionospheric variations. Especially, Rate of TEC Index (ROTI) which can be derived from geometry-free combination of signals and S_4, sigma_phi indices that denotes scintillation of signal have been used to detect ionospheric irregularities. However, ROTI shows different calculation result with respect to different sampling rate and averaging time interval, and S_4,sigma_phi indices are unsuitable for small and fast-moving ionospheric irregularities. Thus, in this paper, we suggest a method of detecting ionospheric irregularities due to earthquakes using ROT and have studied the method of estimating epicenter of the earthquakes. ROT fluctuations and observed at different times depending on the location of the satellite and the reference stations. By estimating this time difference using Inter-Station Cross Correlation, it is possible to estimate the speed of ionospheric irregularities. The location of the epicenter can be estimated using the geometric relationship between the reference stations and the Ionospheric Pierce Point (IPP) where ROT fluctuation had occurred. Algorithm of epicenter estimation was analyzed based on actual data, and we had proposed the fields where these methods are applicable.","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":"135483615","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}
Paulo Ricardo Marques de Araujo, Eslam Mounier, Mohamed Elhabiby, Sidney Givigi, Aboelmagd Noureldin
This paper investigates RIDR (Radar Inertial Dead Reckoning), a novel positioning system using gyroscopes and radar-based forward speed estimation. It presents a formulation for vector space Kalman filters and compares RIDR with INS and RISS. An error state Kalman filter is proposed for precise position and attitude corrections. Experimental validation in diverse urban environments demonstrates the promising performance of RIDR, reinforcing its viability as a robust alternative to IMU-based algorithms. This research highlights challenges and opportunities for future advancements in the field, paving the way for improved autonomous vehicle navigation and control systems.
{"title":"Improving Land Vehicle Navigation: A Study on RIDR and Kalman Filters","authors":"Paulo Ricardo Marques de Araujo, Eslam Mounier, Mohamed Elhabiby, Sidney Givigi, Aboelmagd Noureldin","doi":"10.33012/2023.19212","DOIUrl":"https://doi.org/10.33012/2023.19212","url":null,"abstract":"This paper investigates RIDR (Radar Inertial Dead Reckoning), a novel positioning system using gyroscopes and radar-based forward speed estimation. It presents a formulation for vector space Kalman filters and compares RIDR with INS and RISS. An error state Kalman filter is proposed for precise position and attitude corrections. Experimental validation in diverse urban environments demonstrates the promising performance of RIDR, reinforcing its viability as a robust alternative to IMU-based algorithms. This research highlights challenges and opportunities for future advancements in the field, paving the way for improved autonomous vehicle navigation and control systems.","PeriodicalId":498211,"journal":{"name":"Proceedings of the Satellite Division's International Technical Meeting","volume":"26 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":"135483669","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}
Lisa Guerriero, Elisa Benedetti, Maria L. Ivanovici, Florin C. Grec
The precise positioning for mass-market optimal data dissemination demonstrator (POMELO) is the outcome of a collaboration led by GMV with Telespazio France (TPZ-F), GEOFLEX and Thales Alenia Space France (TAS-F) under a NAVISP EL1 programme funded by the European Space Agency (ESA). The project objective was to explore the feasibility of delivering broadcast real-time kinematic (RTK) and precise point positioning (PPP) corrections using communication protocols aligned with the third-generation partnership project (3GPP) standards to make high-accuracy positioning services accessible to massmarket users of the fourth and fifth generation networks (4G/5G). These positioning techniques are usually adopted by professional users and even though the global navigation satellite system (GNSS) industry has embarked on the path to high-precision GNSS at low costs and low power consumption, the currently available dissemination techniques are still not affordable on a large scale, requiring either high-cost equipment and large investments or significant challenges especially when there is the need to extend the service to a very large amount of users. A possible way forward would be to allow the use of terrestrial wireless networks to broadcast multi-GNSS augmentation services in real-time at a low cost. This would require the mobile network operators to transfer data based on a ‘Send-To-All’ type of dissemination. The main achievement of the POMELO project is the implementation of the first testbed able to demonstrate that it is possible to exploit a part of the wireless network resources available to host high-accuracy GNSS assistance data and broadcast it through cellular signals. Although some limitations still need to be addressed, this achievement represents a significant step forward in making precise GNSS data accessible to a broader range of users through existing communication infrastructures and protocols. The implementation of this service potentially enables any users to adopt high-accuracy positioning techniques through an affordable service.
{"title":"POMELO: A 4G Prototype Testbed to Demonstrate Scalable and Bandwidth Efficient Broadcast of GNSS Corrections","authors":"Lisa Guerriero, Elisa Benedetti, Maria L. Ivanovici, Florin C. Grec","doi":"10.33012/2023.19298","DOIUrl":"https://doi.org/10.33012/2023.19298","url":null,"abstract":"The precise positioning for mass-market optimal data dissemination demonstrator (POMELO) is the outcome of a collaboration led by GMV with Telespazio France (TPZ-F), GEOFLEX and Thales Alenia Space France (TAS-F) under a NAVISP EL1 programme funded by the European Space Agency (ESA). The project objective was to explore the feasibility of delivering broadcast real-time kinematic (RTK) and precise point positioning (PPP) corrections using communication protocols aligned with the third-generation partnership project (3GPP) standards to make high-accuracy positioning services accessible to massmarket users of the fourth and fifth generation networks (4G/5G). These positioning techniques are usually adopted by professional users and even though the global navigation satellite system (GNSS) industry has embarked on the path to high-precision GNSS at low costs and low power consumption, the currently available dissemination techniques are still not affordable on a large scale, requiring either high-cost equipment and large investments or significant challenges especially when there is the need to extend the service to a very large amount of users. A possible way forward would be to allow the use of terrestrial wireless networks to broadcast multi-GNSS augmentation services in real-time at a low cost. This would require the mobile network operators to transfer data based on a ‘Send-To-All’ type of dissemination. The main achievement of the POMELO project is the implementation of the first testbed able to demonstrate that it is possible to exploit a part of the wireless network resources available to host high-accuracy GNSS assistance data and broadcast it through cellular signals. Although some limitations still need to be addressed, this achievement represents a significant step forward in making precise GNSS data accessible to a broader range of users through existing communication infrastructures and protocols. The implementation of this service potentially enables any users to adopt high-accuracy positioning techniques through an affordable service.","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":"135483671","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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