Moving Horizon Estimation for GNSS-IMU sensor fusion

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.

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

Aerospace ◽

10.3390/aerospace8100280 ◽

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.

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

10.20944/preprints201803.0121.v1 ◽

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.

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

Remote Sensing ◽

10.3390/rs13163205 ◽

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.

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Galileo L10 Satellites: Orbit, Clock and Signal-in-Space Performance Analysis

Sensors ◽

10.3390/s21051695 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1695

Author(s):

Constantin-Octavian Andrei ◽

Sonja Lahtinen ◽

Markku Poutanen ◽

Hannu Koivula ◽

Jan Johansson

Keyword(s):

Performance Indicators ◽

Satellite System ◽

Short Term ◽

Orbital Inclination ◽

Navigation Data ◽

Periodic Signals ◽

Global Navigation Satellite ◽

Minor Correction ◽

Very High

The tenth launch (L10) of the European Global Navigation Satellite System Galileo filled in all orbital slots in the constellation. The launch carried four Galileo satellites and took place in July 2018. The satellites were declared operational in February 2019. In this study, we report on the performance of the Galileo L10 satellites in terms of orbital inclination and repeat period parameters, broadcast satellite clocks and signal in space (SiS) performance indicators. We used all available broadcast navigation data from the IGS consolidated navigation files. These satellites have not been reported in the previous studies. First, the orbital inclination (56.7±0.15°) and repeat period (50680.7±0.22 s) for all four satellites are within the nominal values. The data analysis reveals also 13.5-, 27-, 177- and 354-days periodic signals. Second, the broadcast satellite clocks show different correction magnitude due to different trends in the bias component. One clock switch and several other minor correction jumps have occurred since the satellites were declared operational. Short-term discontinuities are within ±1 ps/s, whereas clock accuracy values are constantly below 0.20 m (root-mean-square—rms). Finally, the SiS performance has been very high in terms of availability and accuracy. Monthly SiS availability has been constantly above the target value of 87% and much higher in 2020 as compared to 2019. Monthly SiS accuracy has been below 0.20 m (95th percentile) and below 0.40 m (99th percentile). The performance figures depend on the content and quality of the consolidated navigation files as well as the precise reference products. Nevertheless, these levels of accuracy are well below the 7 m threshold (95th percentile) specified in the Galileo service definition document.

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

Journal of Navigation ◽

10.1017/s037346332100031x ◽

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.

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Assessing the quality of ionospheric models through GNSS positioning error: methodology and results

GPS Solutions ◽

10.1007/s10291-019-0918-z ◽

2019 ◽

Vol 24 (1) ◽

Cited By ~ 6

Author(s):

Adrià Rovira-Garcia ◽

Deimos Ibáñez-Segura ◽

Raul Orús-Perez ◽

José Miguel Juan ◽

Jaume Sanz ◽

...

Keyword(s):

Satellite System ◽

Ionospheric Delay ◽

Single Frequency ◽

Positioning Error ◽

Dual Frequency ◽

Error Sources ◽

Ionospheric Models ◽

Evaluation Metric ◽

Global Navigation Satellite

Abstract Single-frequency users of the global navigation satellite system (GNSS) must correct for the ionospheric delay. These corrections are available from global ionospheric models (GIMs). Therefore, the accuracy of the GIM is important because the unmodeled or incorrectly part of ionospheric delay contributes to the positioning error of GNSS-based positioning. However, the positioning error of receivers located at known coordinates can be used to infer the accuracy of GIMs in a simple manner. This is why assessment of GIMs by means of the position domain is often used as an alternative to assessments in the ionospheric delay domain. The latter method requires accurate reference ionospheric values obtained from a network solution and complex geodetic modeling. However, evaluations using the positioning error method present several difficulties, as evidenced in recent works, that can lead to inconsistent results compared to the tests using the ionospheric delay domain. We analyze the reasons why such inconsistencies occur, applying both methodologies. We have computed the position of 34 permanent stations for the entire year of 2014 within the last Solar Maximum. The positioning tests have been done using code pseudoranges and carrier-phase leveled (CCL) measurements. We identify the error sources that make it difficult to distinguish the part of the positioning error that is attributable to the ionospheric correction: the measurement noise, pseudorange multipath, evaluation metric, and outliers. Once these error sources are considered, we obtain equivalent results to those found in the ionospheric delay domain assessments. Accurate GIMs can provide single-frequency navigation positioning at the decimeter level using CCL measurements and better positions than those obtained using the dual-frequency ionospheric-free combination of pseudoranges. Finally, some recommendations are provided for further studies of ionospheric models using the position domain method.

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Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

10.5194/hess-2017-597 ◽

2017 ◽

Author(s):

Sibo Zhang ◽

Jean-Christophe Calvet ◽

José Darrozes ◽

Nicolas Roussel ◽

Frédéric Frappart ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Ground Surface ◽

Satellite System ◽

Surface Model ◽

Global Navigation Satellite ◽

Grassland Site

Abstract. This work aims to assess the estimation of surface volumetric soil moisture (VSM) using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 or 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show a good agreement (R2 = 0.86 and RMSE = 0.04 m3 m−3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 cm and 5 cm, especially during light rainfall events.

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Wind Gust Measuring at Low Altitude Using An Unmanned Aerial System

10.32920/ryerson.14653290 ◽

2021 ◽

Author(s):

Alton Yeung

Keyword(s):

Turbulence Models ◽

Satellite System ◽

Data System ◽

Measurement Unit ◽

Wind Gust ◽

Wind Gusts ◽

Low Altitude ◽

And Performance ◽

Global Navigation Satellite ◽

Aerial Platform

A small unmanned aerial vehicle (UAV) was developed with the specific objective to explore atmospheric wind gusts at low altitudes within the atmospheric boundary layer (ABL). These gusts have major impacts on the flight characteristics and performance of modern small unmanned aerial vehicles. Hence, this project was set to investigate the power spectral density of gusts observed at low altitudes by measuring the gusts with an aerial platform. The small UAV carried an air-data system including a fivehole probe that was adapted for this specific application. The air-data system measured the local wind gusts with an accuracy of 0.5 m/s by combining inputs from a five-hole probe, an inertial measurement unit, and Global Navigation Satellite System (GNSS) receivers. Over 20 flights were performed during the development of the aerial platform. Airborne experiments were performed to collect gust data at low altitudes between 50 m and 100 m. The result was processed into turbulence spectrum and the measurements were compared with the MIL-HDBK-1797 von K´arm´an turbulence model and the results have shown the model underpredicted the gust intensities experienced by the flight vehicle. The anisotropic properties of low-altitude turbulence were also observed when analyzing the measured gusts spectra. The wind and gust data collected are useful for verifying the existing turbulence models for low-altitude flights and benefit the future development of small UAVs in windy environment.

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OUTDOOR AR-APPLICATION FOR THE DIGITAL MAP TABLE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliv-3-w1-2020-93-2020 ◽

2020 ◽

Vol XLIV-3/W1-2020 ◽

pp. 93-98

Author(s):

S. Maier ◽

T. Gostner ◽

F. van de Camp ◽

A. H. Hoppe

Keyword(s):

Augmented Reality ◽

Geographical Location ◽

Satellite System ◽

Reference Points ◽

Sensor Data ◽

Measurement Unit ◽

Positional Accuracy ◽

Digital Map ◽

The World ◽

Global Navigation Satellite

Abstract. In many fields today, it is necessary that a team has to do operational planning for a precise geographical location. Examples for this are staff work, the preparation of surveillance tasks at major events or state visits and sensor deployment planning for military and civil reconnaissance. For these purposes, Fraunhofer IOSB is developing the Digital Map Table (DigLT). When making important decisions, it is often helpful or even necessary to assess a situation on site. An augmented reality (AR) solution could be useful for this assessment. For the visualization of markers at specific geographical coordinates in augmented reality, a smartphone has to be aware of its position relative to the world. It is using the sensor data of the camera and inertial measurement unit (IMU) for AR while determining its absolute location and direction with the Global Navigation Satellite System (GNSS) and its magnetic compass. To validate the positional accuracy of AR markers, we investigated the current state of the art and existing solutions. A prototype application has been developed and connected to the DigLT. With this application, it is possible to place markers at geographical coordinates that will show up at the correct location in augmented reality at anyplace in the world. Additionally, a function was implemented that lets the user select a point from the environment in augmented reality, whose geographical coordinates are sent to the DigLT. The accuracy and practicality of the placement of markers were examined using geodetic reference points. As a result, we can conclude that it is possible to mark larger objects like a car or a house, but the accuracy mainly depends on the internal compass, which causes a rotational error that increases with the distance to the target.

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Comparison of UGV Position Estimation Equipped With GNSS-RTK and GPS Using EKF

Volume 7B: Dynamics, Vibration, and Control ◽

10.1115/imece2020-23727 ◽

2020 ◽

Author(s):

Hesham Ismail ◽

Thani Althani ◽

Mohammed Minhas Anzil ◽

Prashanth Subramaniam

Keyword(s):

Extreme Temperature ◽

Processing Technique ◽

Position Estimation ◽

Image Processing Technique ◽

Measurement Unit ◽

Unmanned Ground Vehicle ◽

Camera System ◽

Ground Vehicle ◽

Positioning Device ◽

Global Navigation Satellite

Abstract Site assessments for bifacial Photovoltaic (PV) installation are quite challenging to conduct manually due to the area size and the extreme temperature conditions at desert sites. We designed and built an autonomous Unmanned Ground Vehicle (UGV) fitted with a Global Navigation Satellite Network-System Real-Time Kinematic (GNSS-RTK) positioning device, an Inertial Measurement Unit (IMU), encoder to improve and aid site assessments in desert condition. Sandy terrains deserts are challenging for UGV’s because they increase the likelihood of wheel slippage due to reduced traction. Sensor details such as IMU, GNSS-RTK, and encoder should be taken into consideration to account for the errors that the desert terrains pose. This study compared the Extended Kalman Filter (EKF) for standard GPS & GNSS-RTK to verify which performs better for the UGV’s position estimation. The estimated UGV’s position from the kinematics model and EKF are validated using a drone camera system that uses an image processing technique to verify the UGV’s position with the help of the visible reference cones. Throughout the experiments, the GNSS-RTK performed better than GPS. Also, the EKF performed as well as the GNSS-RTK by trusting it more than the encoder/gyroscope reading.

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