Navigation systems engineering is a red-hot area. More and more technical professionals are entering the field and looking for practical, up-to-date engineering know-how. This single-source reference ...answers the call, providing both an introduction to overall systems operation and an in-depth treatment of architecture, design, and component integration. This book explains how satellite, on-board, and other navigation technologies operate, and it gives practitioners insight into performance issues such as processing chains and error sources. Providing solutions to systems designers and engineers, the book describes and compares different integration architectures, and explains how to diagnose errors. Moreover, this hands-on book includes appendices filled with terminology and equations for quick referencing.
The emerging technology of very inexpensive inertial sensors is available for navigation as never before. The book lays the analytical foundation for understanding and implementing the navigation ...equations. It starts by demystifying the central theme of the frame rotation using such algorithms as the quaternions, the rotation vector and the Euler angles. After developing navigation equations, the book introduces the computational issues and discusses the physical aspects that are tied to implementing these equations. The book then explains alignment techniques.
Using the first full annual cycle of Cyclone Global Navigation Satellite System (CyGNSS) observations, we investigated the limitations and capabilities of CyGNSS observations for soil moisture (SM) ...estimates (0–5 cm). A relative signal‐to‐noise ratio (rSNR) value from a CyGNSS‐derived delay‐Doppler map is introduced to improve the temporal resolution of SM derived from Soil Moisture Active Passive (SMAP) data. We then evaluated the CyGNSS‐derived rSNR using ground‐based SM measurements and the triple collocation method with SMAP and modeled SM products. We found that CyGNSS can provide useful SM estimates over moderately vegetated regions (correlation coefficient of the individual data: 0.77) but shows degraded performance over arid and densely vegetated regions (correlation coefficient of the individual data: 0.68 and 0.67). However, when rSNR data is combined with SM data from SMAP, daily SM estimates can be achieved. These results show that synergistic use of CyGNSS observations can improve on SM estimates from current satellite systems.
Plain Language Summary
Accurate climate forecasting affects our daily lives. Large‐scale farmers depend on weather forecasts to decide when to plant their crops. Bad timing can impact the whole years' harvest and thus the farmers' livelihoods. Even more importantly, people who live in floodplains and hurricane zones trust their lives to accurate weather forecasts. For these reasons and more, hydrologists need up‐to‐date knowledge of Earth's climate systems. And one of the most important sources of data may surprise you. The amount of moisture in just the first 8 mm of topsoil affects all of Earth's climate systems. Currently, National Aeronautics and Space Administration keeps track of soil moisture levels with a satellite called Soil Moisture Active Passive. However, it only provides soil moisture data every 2–3 days. We believe that we can do better, and we believe that we can do it with preexisting satellite systems. In 2017, National Aeronautics and Space Administration (NASA) launched eight microsatellites, called Cyclone Global Navigation Satellite System (CyGNSS), to predict cyclone paths. We have found that while the CyGNSS satellites are predicting cyclone paths, they can simultaneously measure changes in soil moisture around 5 times per day. Augmenting the Soil Moisture Active Passive data with CyGNSS would give us detailed prediction of weather changes in near‐real time, protecting livelihoods and lives.
Key Points
CyGNSS‐derived signal‐to‐noise ratio data were utilized for soil moisture estimation
CyGNSS data can fill the gap of missing spatial and temporal values in existing satellite‐based soil moisture retrieval systems
By combining CyGNSS and SMAP data sets, reliable daily soil moisture estimates from space can be achieved
As a common integrated navigation system, the strapdown inertial navigation system (SINS)/global positioning system (GPS) can estimate velocity and position errors well. Many auxiliary attitude ...measurement systems can be used to improve the accuracy of attitude angle errors. In this paper, the in-flight alignment problem of the integrated SINS/GPS/Polarization/Geomagnetic navigation system is discussed. Firstly, the SINS/Geomagnetic subsystem is constructed to improve the estimation accuracy of horizontal attitude angles. Secondly, the polarization sensor is used to improve the estimation accuracy of heading angle. Then, a federal unscented Kalman filter (FUKF) with non-reset structure is applied to fuse the navigation data. Finally, simulation results for the integrated navigation system are provided based on experimental data. It can be shown that the proposed approach can improve not only the speed and position, but also the attitude error effectively.
Inertial navigation system/global navigation satellite system/celestial navigation system (INS/GNSS/CNS) integration is a favorable navigation mode for hypersonic vehicles. However, since the ...measurements from GNSS and CNS are easily interfered during highly dynamic maneuvers, this integration is very difficult to achieve optimal navigation solution with the existing information fusion techniques. This article proposes a new method of decentralized multisensor information fusion based on robust unscented Kalman filter (UKF) for INS/GNSS/CNS integration to solve the above issue. First, a fault detection-based robust UKF is established for local state estimation, in which a hypothesis test is constructed via the Mahalanobis distance of innovation to detect abnormal measurements in GNSS and CNS; and subsequently, a scalar factor is determined and further introduced into the innovation covariance to decrease the Kalman gain to improve the UKF robustness against abnormal measurements. Second, the traditional multisensor optimal data fusion technique is extended to nonlinear systems by the use of unscented transformation in the framework of minimum variance estimation to fuse the local state estimations from INS/GNSS and INS/CNS subsystems. The proposed information fusion method can achieve the globally optimal fusion estimation results against abnormal measurements for hypersonic vehicle navigation with INS/GNSS/CNS integration. Semi-physical simulations and comparison analysis have validated the superior performance of the proposed method.
On 15 January 2022, the Hunga Tonga‐Hunga Ha’apai submarine volcano erupted violently and triggered a giant atmospheric shock wave and tsunami. The exact mechanism of this extraordinary eruptive ...event, its size and magnitude are not well understood yet. In this work, we analyze data from the nearest ground‐based receivers of Global Navigation Satellite System to explore the ionospheric total electron content (TEC) response to this event. We show that the ionospheric response consists of a giant TEC increase followed by a strong long‐lasting depletion. We observe that the explosive event of 15 January 2022 began at 04:05:54UT and consisted of at least five explosions. Based on the ionospheric TEC data, we estimate the energy released during the main major explosion to be between 9 and 37 Megatons in trinitrotoluene equivalent. This is the first detailed analysis of the eruption sequence scenario and the timeline from ionospheric TEC observations.
Plain Language Summary
On 15 January 2022, the giant explosion of the Hunga Tonga‐Hunga Ha’apai volcano shook the atmosphere of the Earth and generated a tsunami. The exact mechanism and timing of the eruption are not well understood yet, nor is the series of events that occurred directly following the first event. Many scientists are trying to understand the chronology of the eruption using different types of data. Here we investigate the signature of the eruption as recorded in Earth’s ionosphere, the electrically conductive layer of the atmosphere from about 85 to 800 km of altitude. We observe variations in the total electron content (TEC) of the ionosphere using Global Navigation Satellite System receivers (commonly known as GPS receivers). Variations in the TEC through time and space are caused by sound waves from the eruption traveling through the ionosphere. We use these variations to constrain the timing of the eruptive events, identifying at least five major explosions during this eruption. In addition, we use the amplitude of TEC variations to estimate that the largest explosion released energy of about 9–37 Megaton in trinitrotoluene equivalent. This is the first detailed analysis of the eruption scenario and the timeline from ionospheric TEC observations.
Key Points
Ionospheric total electron content (TEC) data reveal that the 15 January 2022 Hunga Tonga volcanic eruption involved at least five large explosions between 4 and 5UT
From TEC observations, we estimate the onset time to be 04:05:54UT and the main explosion energy release of 9–37 Megatons trinitrotoluene equivalent
The eruption‐driven shock wave caused an unprecedentedly strong and long‐lasting depletion in the ionosphere
A tightly coupled inertial navigation system (INS) aided by ambient signals of opportunity (SOPs) is developed. In this system, a navigating vehicle aids its onboard INS using pseudoranges drawn from ...terrestrial SOPs with unknown emitter positions and clock biases through an extended Kalman filter-based radio simultaneous localization and mapping (SLAM) framework. The SOP-aided INS uses both global navigation satellite system (GNSS) and SOP pseudoranges during GNSS availability periods and switches to using SOP pseudoranges exclusively during GNSS unavailability periods. This framework is studied through numerical simulations by varying: 1) Quantity of exploited SOPs and 2) quality of SOP-equipped oscillators. It is demonstrated that the SOP-aided INS using a consumer-grade IMU produces smaller estimation uncertainties compared to a traditional tightly coupled GNSS-aided INS using a tactical-grade IMU. In the absence of GNSS signals, over the simulation finite-time horizon, the errors produced by the SOP-aided INS appear to be bounded, while the errors produced by a traditional tightly coupled GNSS-aided INS diverge unboundedly. Moreover, the article presents experimental results demonstrating an unmanned aerial vehicle using terrestrial cellular SOPs to aid its onboard consumer-grade IMU in the absence of GNSS signals. It is demonstrated that the final position error of a traditional tightly coupled GNSS-aided INS after 30 s of GNSS cutoff was 57.30 m, while the final position error of the tightly coupled SOP-aided INS was 9.59 m.
In general, the strap-down inertial navigation system (SINS)/Doppler velocity log (DVL)-integrated navigation method can provide continuous and accurate navigation information for autonomous ...underwater vehicles (AUV). This SINS/DVL fusion is the loosely integrated method, in which DVL may contain large error or does not work when some beam measurements are inaccurate or outages for complex underwater environment. To solve these problems, in this article, a novel tightly integrated navigation method composed of an SINS, a DVL, and a pressure sensor (PS) is proposed, in which beam measurements are used without transforming them to 3-D velocity. The simulation and vehicle test show that the proposed method can significantly outperform the traditional loosely integrated method in providing estimation continuously with higher accuracy when DVL data are inaccurate or unavailable for a complex environment. Compared with loosely integrated method, the position accuracy of the proposed method has improved by 32.5%.
PDF
