Abstracts:We present the methodology and results of a simulation to determine the recoverability of LEO objects using a blind stacking technique. The method utilises sCMOS and GPU technology to inject and recover LEO objects in real observed data. We explore the target recovery fraction and pipeline run-time as a function of three optimisation parameters; number of frames per data-set, exposure time, and binning factor. Results are presented as a function of magnitude and velocity. We find that target recovery using blind stacking is significantly more complete, and can reach fainter magnitudes, than using individual frames alone. We present results showing that, depending on the combination of optimisation parameters, recovery fraction is up to 90% of detectable targets for magnitudes up to 13.5, and then falls off steadily up to a magnitude limit around 14.5. Run-time is shown to be a few multiples of the observing time for the best combinations of optimisation parameters, approaching real-time processing.

Abstracts:A synchronization control scheme for underactuated satellite formation reconfiguration in circular orbits is proposed in this paper. The main idea of the synchronization scheme is to control the satellite without radial or along-track thrust to track its desired configuration while synchronizing its motion with that of other underactuated satellites to maintain the predefined or time-varying formation. Firstly, a new underactuated control approach is presented for either underactuated case. Based on the inherent coupling of the system states, the finite-time convergence of the system trajectory could be guaranteed by the sliding mode-based underactuated controller. Secondly, to synchronize the reconfiguration motion between followers, the sum of the sliding mode errors of every two neighbor followers is defined as the synchronization control. The Lyapunov-based method proves the finite-time convergence of synchronization control. More importantly, the minimum nonzero eigenvalue of the Laplace matrix can reduce the system state convergence region. Numerical simulations also verify the performance of the proposed underactuated synchronization controller.

Abstracts:Ambitious satellite constellation projects by commercial entities and the ease of access to space in recent times have led to a dramatic proliferation of low-Earth space traffic. It jeopardizes space safety and long-term sustainability, necessitating better space domain awareness (SDA). Correct modeling of uncertainties in force models and orbital states, among other things, is an essential part of SDA. For objects in the low-Earth orbit (LEO) region, the uncertainty in the orbital dynamics mainly emanate from limited knowledge of the atmospheric drag-related parameters and variables. In this paper, which extends the work by Paul et al. (2021), we develop a feed-forward deep neural network model for the prediction of the satellite drag coefficient for the full range of satellite attitude (i.e., satellite pitch ∈ ( -90°,+90°) and satellite yaw ∈ ( 0°,+360°)). The model simultaneously predicts the mean and the standard deviation and is well-calibrated. We use numerically simulated physical drag coefficient data for training our neural network. The numerical simulations are carried out using the test particle Monte Carlo method using the diffuse reflection with incomplete accommodation gas-surface interaction model. Modeling is carried out for the well-known CHAllenging Minisatellite Payload (CHAMP) satellite. Finally, we use the Monte Carlo approach to propagate CHAMP over a three-day period under various modeling scenarios to investigate the distribution of radial, along-track, and cross-track orbital errors caused by drag coefficient uncertainty. The key takeaways of this paper are - (a) a constant drag coefficient cannot be used for reliable SDA purposes, and (b) stochastic machine learning models allow for the computation of drag coefficients in a timely manner while providing reliable uncertainty estimates.

Abstracts:The contact method based on flexible actuator is safe and efficient for the detumbling task of space nutation targets. In this paper, a detumbling system with a rotatable flexible rod as the end effector is proposed to reduce the rotational angular velocity of the target by contacting the solar sail panel of the target with the flexible rod, and the selection of the initial relative position and attitude is discussed. First, the contact dynamics model of a rotatable flexible rod considering the nonlinear coupling term is derived using the floating frame of reference formulation and validated for comparison. Next, in order to ensure the accuracy of contact detection in the contact model, it is proposed to transform the contact detection of flexible bodies into a nonlinear programming problem and solve it using the quantum particle swarm optimization. Finally, the simulation results show that the detumbling system can effectively reduce the triaxial angular velocity of the nutation target. At the same time, in order to improve the detumbling effect, the contact point should not choose the midpoint of the sail edge in the conventional way, and the relative position and attitude of the end effector before contact must be reasonably set.

Abstracts:Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales.

Abstracts:Microwave signals, for example, those from Global Navigation Satellite System (GNSS) and Very Long Baseline Interferometry (VLBI), are affected by tropospheric turbulence in such a way that the random fluctuations of the atmospheric index of refractivity correlate the phase measurements. A proper modeling of correlations is mandatory to avoid biased analysis, particularly when statistical tests are used. In this contribution, we analyze single differences (SD) computed from Global Positioning System (GPS) phase observations for which the between receiver clock error could be strongly mitigated by a specific common clock setting. We estimate specific parameters from the power spectral density (psd), which is directly related to the correlation function, with the debiased Whittle maximum likelihood and investigate their dependencies with the satellite geometry (elevation, azimuth angles) and the time of the day. We show that (i) the estimated slopes of the psd follow the one predicted by the Kolmogorov turbulence theory and (ii) the cut-off at high frequencies shows daily variations that may be linked with the strength of the turbulence. Based on these findings, we derive an improved spectral density model for GPS phase SD. The results of this study contribute to improving the stochastic description of random effects impacting VLBI and GNSS phase observations by studying variations of parameters from the von Karman spectrum.

Abstracts:The stratiform manganese deposits, hosted within the Sausar Group of rocks, in Madhya Pradesh state of India, has undergone polyphase deformation and metamorphism. The region is an enterprising geosite to understand the lithological and tectonic control on manganese mineralization. We have carried out a conjugate remote sensing based analysis using Visible, Near Infrared and Short Wave Infrared bands (VIS-NIR-SWIR) of optical imagery from multiple sensors supplemented with analysis of L-band microwave data over the Balaghat district of Madhya Pradesh to delineate prospective areas for manganese exploration, supported by the lab and field-based evidence. The enhanced image products (i.e.band ratios, principal components) and spectral anomaly map derived from Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2B multispectral bands are used to delineate the lithological contrast of the host rock and indicate key locales of mineralization. The geological structures interpreted using the merged product of Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR −1), Landsat 8 PC bands (2 & 3) and the processed Horizontal Gradient Magnitude (HGM) from advanced satellite gravity model (EIGEN-6C4), illustrates the structural control on the mineralization. The petrological details derived from the host rock samples shows microstructures like micro-folding, foliations, quartz grain elongation etc., thus representing the microscale deformations within the rock units occurring in the area.The lab-based spectral signatures of manganese-bearing host rock was resampled to image spectra and spectral anomaly map using Constrained Energy Minimization (CEM), which shows good correlation with the existing mine locations. The information from remote sensing and lab analysis has been integrated thematically using the knowledge-based Weighted Sum Method (WSM) with existing mine locations as the training dataset. The derived mineral prospect map indicates several new areas of manganese prospectivity which can serve as a guide for detailed future explorations in the region .

Abstracts:The chemical composition of aerosols is crucial for understanding aerosol radiative characteristics, classifying aerosol sources, and improving atmospheric aerosol satellite retrieval algorithms. In this study, the absorption angström exponent (AAE) and scattering angström exponent (SAE) values are derived from an aethalometer and three wavelength integrating nephelometer. MERRA-2 (Modern-Era Retrospective Study for Research and Applications) speciated aerosol optical depths and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol subtypes are utilized for the identification of a possible aerosol type over a semi-arid station, Anantapur, in Southern peninsular India from March 2016 to February 2018. Analysis from MERRA-2 for different aerosols illustrates that sulphate aerosol optical depth (SO4 AOD) dominates (>0.16) during winter and post-monsoon, while dust aerosol optical depth (DU AOD), organic carbon optical depth (OCAOD), and sulphate aerosol optical depth (SO4 AOD) mainly contributed (>0.07) to the total AOD during summer. SO4 AOD accounted for over ∼ 50% of total AOD during winter and post-monsoon, whereas DU AOD accounted for<10% of total aerosol optical depth in the respective seasons. The seasonal mean values of SAE (AAE) were found to be 1.95 ± 0.37 (1.03 ± 0.08), 1.65 ± 0.55 (1.03 ± 0.08), 2.53 ± 0.20 (1.03 ± 0.08) and 2.66 ± 0.15 (1.03 ± 0.08) during summer, monsoon, post-monsoon, and winter, respectively. AAE and SAE classification schemes showed that during winter, summer, and post-monsoon, continental polluted aerosols dominated (>56%), while dust and marine pollution aerosols dominated (∼47%) to the total aerosols during the monsoon. Continental aerosols are predominant during all seasons except for monsoon, and result from local anthropogenic activities and the transport of mineral dust particles and smoke aerosols from the north and western India. The observed aerosol types are consistent with the air mass back-trajectory cluster analysis performed with the help of Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT). The various aerosol types (absorbing and non-absorbing) and their change over a region are essential for for reducing the uncertainty in the assessment of aerosol radiative effects.

Abstracts:The errors affecting standard point positioning mainly include the ionospheric delay error, tropospheric delay error, clock bias, and multipath effect. The errors are divided into two categories depending on the relation to the signal propagation path: errors related to the propagation path and errors unrelated to the propagation path. The ionospheric delay error, tropospheric delay error, and multipath effect are related to the satellite signal propagation path. Based on this characteristic, a new GPS standard point positioning algorithm based on spherical harmonic expansion (SPP-SH) is proposed, which uses spherical harmonic expansion to represent the errors related to the propagation path. We hope to continuously promote the further development of standard point positioning (SPP) by proposing this new algorithm and to further reduce the redundancy of SPP algorithms and improve the efficiency and reliability of the algorithms. The most important purpose of this manuscript is to validate the proposed SPP-SH algorithm based on GPS data provided by IGS stations. Pseudorange data of multiple continuous epochs are used to achieve a sequential sliding point positioning solution, and the truncated singular value decomposition and iteration method correcting characteristic values are used to solve the ill-conditioned equations in the SPP-SH algorithm. The SPP-SH algorithm is implemented and verified using IGS station data. The positioning accuracy of the SPP-SH algorithm for both dual-frequency data and single-frequency data is better than that of a traditional SPP algorithm. By analyzing the positioning results of the stations at different latitudes, when the spherical harmonic expansion is expanded to the same degree, the positioning accuracy in low latitude areas is poor because the station is seriously affected by factors such as the ionosphere and water vapor, while the positioning accuracy at high latitude stations is the best. The proposed SPP-SH algorithm is also realized in the kinematic scene based on the dual-frequency data collected dynamically. The kinematic data is collected by an Android phone which supports receiving dual-frequency GPS observations. To validate the reliability of the SPP-SH algorithm, two strategies are designed to validate the results, which proves that the newly proposed method can be used for positioning with low accuracy in kinematic conditions.

Abstracts:A new method to define a background for the ionospheric electron density (Ne) is proposed, making use of mid-latitude measurements under different solar conditions from the Langmuir Probes onboard CHAMP and three identical Swarm satellites. In particular, CHAMP measurements during the years 2004 and 2009, and Swarm observations during 2016 and 2017 have been considered in the 15°-wide latitudinal belt from 35°N to 50°N, and from 0° to 360° in longitude. CHAMP/Swarm in-situ Ne measurements have been then used to check and compare this new defined background with the one computed directly from IRI-2016 Ne output at satellite altitude. The distributions of the relative deviations between the two backgrounds, and of positive and negative anomalies (i.e., Ne variations from each background greater than 30%) with respect to the geomagnetic activity levels have been evaluated under each investigated condition, namely year/satellite, season, night-time or noon hours. Results of this comparison highlight a general overestimation of Ne from IRI during noon hours, while a better agreement between the two backgrounds is found during night-time. However, an underestimation of IRI with respect to Swarm-derived background is found for 2017 data. Finally, the analysis of 2004 plasma data suggests that the IRI-2016 model can be used as a background during periods characterized by high levels of geomagnetic activity. Due to the difficulties to construct a background for satellite data, the proposed method can be considered an useful tool for analyses of electron density variations at the heights of the satellites in Low Earth Orbits (LEO).