scholarly journals Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform

Aerospace ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 280
Author(s):  
Farzan Farhangian ◽  
Hamza Benzerrouk ◽  
Rene Landry
Keyword(s):  
Kalman Filter ◽  
State Space Model ◽  
Flight Test ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Measurement Unit ◽  
Starting Point ◽  
Leo Satellites ◽  
Positioning Method ◽  
Global Navigation Satellite

With the emergence of numerous low Earth orbit (LEO) satellite constellations such as Iridium-Next, Globalstar, Orbcomm, Starlink, and OneWeb, the idea of considering their downlink signals as a source of pseudorange and pseudorange rate measurements has become incredibly attractive to the community. LEO satellites could be a reliable alternative for environments or situations in which the global navigation satellite system (GNSS) is blocked or inaccessible. In this article, we present a novel in-flight alignment method for a strapdown inertial navigation system (SINS) using Doppler shift measurements obtained from single or multi-constellation LEO satellites and a rotation technique applied on the inertial measurement unit (IMU). Firstly, a regular Doppler positioning algorithm based on the extended Kalman filter (EKF) calculates states of the receiver. This system is considered as a slave block. In parallel, a master INS estimates the position, velocity, and attitude of the system. Secondly, the linearized state space model of the INS errors is formulated. The alignment model accounts for obtaining the errors of the INS by a Kalman filter. The measurements of this system are the difference in the outputs from the master and slave systems. Thirdly, as the observability rank of the system is not sufficient for estimating all the parameters, a discrete dual-axis IMU rotation sequence was simulated. By increasing the observability rank of the system, all the states were estimated. Two experiments were performed with different overhead satellites and numbers of constellations: one for a ground vehicle and another for a small flight vehicle. Finally, the results showed a significant improvement compared to stand-alone INS and the regular Doppler positioning method. The error of the ground test reached around 26 m. This error for the flight test was demonstrated in different time intervals from the starting point of the trajectory. The proposed method showed a 180% accuracy improvement compared to the Doppler positioning method for up to 4.5 min after blocking the GNSS.

scholarly journals Detecting Targets above the Earth’s Surface Using GNSS-R Delay Doppler Maps: Results from TDS-1

2019 ◽  
Vol 11 (19) ◽  
pp. 2327 ◽  
Author(s):  
Changjiang Hu ◽  
Craig Benson ◽  
Hyuk Park ◽  
Adriano Camps ◽  
Li Qiao ◽  
...  
Keyword(s):  
Specular Reflection ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Navigation Satellite ◽  
Reflection Point ◽  
Leo Satellites ◽  
Gnss Reflectometry ◽  
Global Navigation Satellite ◽  
Remotely Sense

Global Navigation Satellite System (GNSS) reflected signals can be used to remotely sense the Earth’s surface, known as GNSS reflectometry (GNSS-R). The GNSS-R technique has been applied to numerous areas, such as the retrieval of wind speed, and the detection of Earth surface objects. This work proposes a new application of GNSS-R, namely to detect objects above the Earth’s surface, such as low Earth orbit (LEO) satellites. To discuss its feasibility, 14 delay Doppler maps (DDMs) are first presented which contain unusually bright reflected signals as delays shorter than the specular reflection point over the Earth’s surface. Then, seven possible causes of these anomalies are analysed, reaching the conclusion that the anomalies are likely due to the signals being reflected from objects above the Earth’s surface. Next, the positions of the objects are calculated using the delay and Doppler information, and an appropriate geometry assumption. After that, suspect satellite objects are searched in the satellite database from Union of Concerned Scientists (UCS). Finally, three objects have been found to match the delay and Doppler conditions. In the absence of other reasons for these anomalies, GNSS-R could potentially be used to detect some objects above the Earth’s surface.

scholarly journals Moving Horizon Estimation for GNSS-IMU sensor fusion

2020 ◽  
pp. 112-122
Author(s):  
Guido Sánchez ◽  
Marina Murillo ◽  
Lucas Genzelis ◽  
Nahuel Deniz ◽  
Leonardo Giovanini
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Satellite System ◽  
Measurement Unit ◽  
Moving Horizon Estimation ◽  
Ground Vehicle ◽  
Global Navigation Satellite ◽  
Time Systems ◽  
True Values

The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor fusion system. To achieve this objective, we introduce a Moving Horizon Estimation (MHE) algorithm to estimate the position, velocity orientation and also the accelerometer and gyroscope bias of a simulated unmanned ground vehicle. The obtained results are compared with the true values of the system and with an Extended Kalman filter (EKF). The use of CasADi and Ipopt provide efficient numerical solvers that can obtain fast solutions. The quality of MHE estimated values enable us to consider MHE as a viable replacement for the popular Kalman Filter, even on real time systems.

scholarly journals Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; GNSS/IMU Case Study

2018 ◽  
Author(s):  
Siavash Hosseinyalamdary
Keyword(s):  
Kalman Filter ◽  
Satellite System ◽  
Measurement Unit ◽  
Sources Of Information ◽  
Error Sources ◽  
Multiple Sensors ◽  
Efficient Integration ◽  
Global Navigation Satellite ◽  
Gnss Observations

The Bayes filters, such as Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of the unknowns. Efficient integration of multiple sensors requires deep knowledge of their error sources and it is not trivial for complicated sensors, such as Inertial Measurement Unit (IMU). Therefore, IMU error modelling and efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we develop deep Kalman filter to model and remove IMU errors and consequently, improve the accuracy of IMU positioning. In other words, we add modelling step to the prediction and update steps of Kalman filter and the IMU error model is learned during integration. Therefore, our deep Kalman filter outperforms Kalman filter and reaches higher accuracy.

scholarly journals Information-Based Georeferencing of an Unmanned Aerial Vehicle by Dual State Kalman Filter with Implicit Measurement Equations

2021 ◽  
Vol 13 (16) ◽  
pp. 3205
Author(s):  
Rozhin Moftizadeh ◽  
Sören Vogel ◽  
Ingo Neumann ◽  
Johannes Bureick ◽  
Hamza Alkhatib
Keyword(s):  
Kalman Filter ◽  
Satellite System ◽  
Measurement Unit ◽  
Case Scenario ◽  
Inner Cities ◽  
Implicit Measurement ◽  
Geometrical Constraints ◽  
Measurement Equations ◽  
Global Navigation Satellite ◽  
Gnss Data

Georeferencing a kinematic Multi-Sensor-System (MSS) within crowded areas, such as inner-cities, is a challenging task that should be conducted in the most reliable way possible. In such areas, the Global Navigation Satellite System (GNSS) data either contain inevitable errors or are not continuously available. Regardless of the environmental conditions, an Inertial Measurement Unit (IMU) is always subject to drifting, and therefore it cannot be fully trusted over time. Consequently, suitable filtering techniques are required that can compensate for such possible deficits and subsequently improve the georeferencing results. Sometimes it is also possible to improve the filter quality by engaging additional complementary information. This information could be taken from the surrounding environment of the MSS, which usually appears in the form of geometrical constraints. Since it is possible to have a high amount of such information in an environment of interest, their consideration could lead to an inefficient filtering procedure. Hence, suitable methodologies are necessary to be extended to the filtering framework to increase the efficiency while preserving the filter quality. In the current paper, we propose a Dual State Iterated Extended Kalman Filter (DSIEKF) that can efficiently georeference a MSS by taking into account additional geometrical information. The proposed methodology is based on implicit measurement equations and nonlinear geometrical constraints, which are applied to a real case scenario to further evaluate its performance.

Kinematic orbit positioning applying the raw observation approach

2020 ◽  
Author(s):  
Barbara Suesser- Rechberger ◽  
Torsten Mayer-Guerr ◽  
Sandro Krauss
Keyword(s):  
Three Dimensional ◽  
Precise Orbit Determination ◽  
Satellite Orbit ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Observation Data ◽  
Kinematic Orbit ◽  
Leo Satellites ◽  
Least Squares Adjustment ◽  
Global Navigation Satellite

<p>The kinematic strategy for precise orbit determination (POD) of low earth orbit (LEO) satellites uses only geometric observations to estimate the satellite orbit and does not take any forces into account. This strategy requires a large amount of observation data for one epoch to determine the three-dimensional satellite position. One possibility to get these data is the usage of the spaceborne global navigation satellite system (GNSS) technology, which provides a high number of accurate observations. Following Zehentner (2016) the kinematic orbit positioning applying the raw observation approach by using a least-squares adjustment has shown promising results with a high accuracy.</p><p>By applying this approach the kinematic orbits for several LEO satellite missions are estimated and subsequently validated by a comparison with state of the art gravity field solutions. Furthermore due to the fact that solar events causes an orbit decay, these precise determined orbit data are used to analyze solar event impacts on LEO satellites.</p>

scholarly journals Intelligent Positioning for a Commercial Mobile Platform in Seamless Indoor/Outdoor Scenes based on Multi-sensor Fusion

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1696 ◽  
Author(s):  
Dongsheng Wang ◽  
Yongjie Lu ◽  
Lei Zhang ◽  
Guoping Jiang
Keyword(s):  
Satellite System ◽  
Working Environment ◽  
Mobile Platform ◽  
Measurement Unit ◽  
Outdoor Scenes ◽  
Positioning Method ◽  
Heading Angle ◽  
Subway Stations ◽  
Global Navigation Satellite ◽  
Intelligent Positioning

Many traffic occasions such as tunnels, subway stations and underground parking require accurate and continuous positioning. Navigation and timing services offered by the Global Navigation Satellite System (GNSS) is the most popular outdoor positioning method, but its signals are vulnerable to interference, leading to a degraded performance or even unavailability. The combination of magnetometer and Inertial Measurement Unit (IMU) is one of the commonly used indoor positioning methods. Within the proposed mobile platform for positioning in seamless indoor and outdoor scenes, the data of magnetometer and IMU are used to update the positioning when the GNSS signals are weak. Because the magnetometer is susceptible to environmental interference, an intelligent method for calculating heading angle by magnetometer is proposed, which can dynamically calculate and correct the heading angle of the mobile platform in a working environment. The results show that the proposed method of calculating heading angle by magnetometer achieved better performance with interference existence. Compared with the uncorrected heading angle, the corrected accuracy results could be improved by 60%, and the effect was more obvious when the interference was stronger. The error of overall positioning trajectory and true trajectory was within 2 m.

scholarly journals Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation

2020 ◽  
Vol 12 (16) ◽  
pp. 2560
Author(s):  
Lingdong Meng ◽  
Jiexian Wang ◽  
Junping Chen ◽  
Bin Wang ◽  
Yize Zhang
Keyword(s):  
Probability Model ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Navigation Satellite ◽  
Constellation Design ◽  
Leo Satellites ◽  
Satellite Visibility ◽  
Global Navigation Satellite ◽  
Gnss Constellation

We proposed an extended geometry and probability model (EGAPM) to analyze the performance of various kinds of (Global Navigation Satellite System) GNSS+ constellation design scenarios in terms of satellite visibility and dilution of precision (DOP) et al. on global and regional scales. Different from conventional methods, requiring real or simulated satellite ephemerides, this new model only uses some basic parameters of one satellite constellation. Verified by the reference values derived from precise satellite ephemerides, the accuracy of visible satellite visibility estimation using EGAPM gets an accuracy better than 0.11 on average. Applying the EGAPM to evaluate the geometry distribution quality of the hybrid GNSS+ constellation, where highly eccentric orbits (HEO), quasi-zenith orbit (QZO), inclined geosynchronous orbit (IGSO), geostationary earth orbit (GEO), medium earth orbit (MEO), and also low earth orbit (LEO) satellites included, we analyze the overall performance quantities of different constellation configurations. Results show that QZO satellites perform slightly better in the Northern Hemisphere than IGSO satellites. HEO satellites can significantly improve constellation geometry distribution quality in the high latitude regions. With 5 HEO satellites included in the third-generation BeiDou navigation satellite system (BDS-3), the average VDOP (vertical DOP) of the 30° N–90° N region can be decreased by 16.65%, meanwhile satellite visibility can be increased by 38.76%. What is more, the inclusion of the polar LEO constellation can significantly improve GNSS service performance. When including with 288 LEO satellites, the overall DOPs (GDOP (geometric DOP), HDOP (horizontal DOP), PDOP (position DOP), TDOP (time DOP), and VDOP) are decreased by about 40%, and the satellite visibility can be increased by 183.99% relative to the Global Positioning System (GPS) constellation.

scholarly journals Compressed pseudo-SLAM: pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation

2021 ◽  
pp. 1-13
Author(s):  
Jonghyuk Kim ◽  
Jose Guivant ◽  
Martin L. Sollie ◽  
Torleiv H. Bryne ◽  
Tor Arne Johansen
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Computational Cost ◽  
Satellite System ◽  
Measurement Unit ◽  
Electronic Systems ◽  
Computationally Efficient ◽  
Global Correlation ◽  
Aerial Vehicle ◽  
Correlation Information ◽  
Global Navigation Satellite

Abstract This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

scholarly journals GNSS RTK Positioning Augmented with Large LEO Constellation

2019 ◽  
Vol 11 (3) ◽  
pp. 228 ◽  
Author(s):  
Xingxing Li ◽  
Hongbo Lv ◽  
Fujian Ma ◽  
Xin Li ◽  
Jinghui Liu ◽  
...  
Keyword(s):  
Low Earth Orbit ◽  
Global Navigation Satellite Systems ◽  
Convergence Time ◽  
Current Status ◽  
Initial Assessment ◽  
Satellite Systems ◽  
Long Baseline ◽  
Leo Satellites ◽  
Global Navigation Satellite ◽  
Long Baselines

It is widely known that in real-time kinematic (RTK) solution, the convergence and ambiguity-fixed speeds are critical requirements to achieve centimeter-level positioning, especially in medium-to-long baselines. Recently, the current status of the global navigation satellite systems (GNSS) can be improved by employing low earth orbit (LEO) satellites. In this study, an initial assessment is applied for LEO constellations augmented GNSS RTK positioning, where four designed LEO constellations with different satellite numbers, as well as the nominal GPS constellation, are simulated and adopted for analysis. In terms of aforementioned constellations solutions, the statistical results of a 68.7-km baseline show that when introducing 60, 96, 192, and 288 polar-orbiting LEO constellations, the RTK convergence time can be shortened from 4.94 to 2.73, 1.47, 0.92, and 0.73 min, respectively. In addition, the average time to first fix (TTFF) can be decreased from 7.28 to 3.33, 2.38, 1.22, and 0.87 min, respectively. Meanwhile, further improvements could be satisfied in several elements such as corresponding fixing ratio, number of visible satellites, position dilution of precision (PDOP) and baseline solution precision. Furthermore, the performance of the combined GPS/LEO RTK is evaluated over various-length baselines, based on convergence time and TTFF. The research findings show that the medium-to-long baseline schemes confirm that LEO satellites do helpfully obtain faster convergence and fixing, especially in the case of long baselines, using large LEO constellations, subsequently, the average TTFF for long baselines has a substantial shortened about 90%, in other words from 12 to 2 min approximately by combining with the larger LEO constellation of 192 or 288 satellites. It is interesting to denote that similar improvements can be observed from the convergence time.

scholarly journals From GPS Receiver to GNSS Reflectometry Payload Development for the Triton Satellite Mission

2021 ◽  
Vol 13 (5) ◽  
pp. 999
Author(s):  
Yung-Fu Tsai ◽  
Wen-Hao Yeh ◽  
Jyh-Ching Juang ◽  
Dian-Syuan Yang ◽  
Chen-Tsung Lin
Keyword(s):  
Satellite System ◽  
Low Earth Orbit ◽  
Export Control ◽  
Navigation Systems ◽  
Gps Receiver ◽  
Design Development ◽  
Satellite Mission ◽  
Essential Components ◽  
Gnss Reflectometry ◽  
Global Navigation Satellite

The global positioning system (GPS) receiver has been one of the most important navigation systems for more than two decades. Although the GPS system was originally designed for near-Earth navigation, currently it is widely used in highly dynamic environments (such as low Earth orbit (LEO)). A space-capable GPS receiver (GPSR) is capable of providing timing and navigation information for spacecraft to determine the orbit and synchronize the onboard timing; therefore, it is one of the essential components of modern spacecraft. However, a space-grade GPSR is technology-sensitive and under export control. In order to overcome export control, the National Space Organization (NSPO) in Taiwan completed the development of a self-reliant space-grade GPSR in 2014. The NSPO GPSR, built in-house, has passed its qualification tests and is ready to fly onboard the Triton satellite. In addition to providing navigation, the GPS/global navigation satellite system (GNSS) is facilitated to many remote sensing missions, such as GNSS radio occultation (GNSS-RO) and GNSS reflectometry (GNSS-R). Based on the design of the NSPO GPSR, the NSPO is actively engaged in the development of the Triton program (a GNSS reflectometry mission). In a GNSS-R mission, the reflected signals are processed to form delay Doppler maps (DDMs) so that various properties (including ocean surface roughness, vegetation, soil moisture, and so on) can be retrieved. This paper describes not only the development of the NSPO GPSR but also the design, development, and special features of the Triton’s GNSS-R mission. Moreover, in order to verify the NSPO GNSS-R receiver, ground/flight tests are deemed essential. Then, data analyses of the airborne GNSS-R tests are presented in this paper.

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