PRISMA Mission Research Articles

Validation of aerosol chemical composition and optical properties provided by Copernicus Atmosphere Monitoring Service (CAMS) using ground-based global data

Monitoring particulate matter (PM) air pollution in terms of both concentration and composition, is very important due to its effects on human health and climate. In the PRIMARY project we aim at retrieving the aerosol composition from space using the hyperspectral observations from the Italian Space Agency's PRISMA mission. To this end, we are developing a machine learning algorithm trained with synthetic top-of-atmosphere reflectances and underlying aerosol fields. As part of this process, we plan to use the global forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) as the core to generate this synthetic dataset. However, to proceed in this direction, a preliminary assessment of the reliability of this model-based dataset when compared to observations is necessary, also to bias correct the output if needed. With this aim, we assess the representation of the aerosol chemical composition and the related optical properties at selected globally distributed sites in CAMS, comparing the simulations with near-surface aerosol chemical analyses from the SPARTAN network and column sun-photometer observations from the AERONET network. We found that CAMS forecasts skills changed over time due to updates in the modelling system, with the latter two version cycles (46 and 47) being similar. Generally, they reproduce the aerosol composition within a factor of 2. We found a substantial overestimation of organic matter (OM) by a factor of 3. Applying a correcting factor to OM (constant at the global level) warrants a much more realistic representation of PM2.5 total mass and relative fraction of single species in CAMS. From the so derived CAMS aerosol-speciated profiles, we calculate aerosol optical properties, needed for subsequent use in a radiative transfer model. Comparison against AERONET indeed shows that OM bias correction resulted in improvements in Extinction Ångstrom Exponent (α440nm870nm). Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA) and Asymmetry Parameter (g) simulations resulted slightly degraded, confirming the possibility of using CAMS as the base for a synthetic retrieval training dataset.

Nonnegative matrix factorization-based environmental monitoring of marine mucilage

ABSTRACT Marine mucilage, also known as sea-snot, is an environmental disaster with ecological, economical, and public health-related impacts, which is occurring with increasing frequency and severity worldwide. One of the latest mucilage occurrences, the mucilage outbreak of Spring 2021 in the Sea of Marmara, lasted for well over three months and covered more than area of 1160 k m 2 , and was repeated in 2022, although to a smaller extent in terms of duration, coverage area, and severity. Remotely sensed images acquired by Earth observation sensors provide a convenient and promising alternative for environmental monitoring of marine mucilage, with respect to field and laboratory work which are costly, require expert knowledge, and which have to be conducted in a small time-window and have limited generalizability due to the under-sampling of a large medium with a highly dynamic character. This work presents an unmixing-based approach using nonnegative matrix factorization for marine mucilage monitoring from remotely sensed hyperspectral data. The proposed method enables to identify the spectral signature of mucilage, and its variants where applicable, and the precise detection and estimation of mucilage concentrations in affected regions. Additionally, the proposed approach facilitates monitoring the temporal variations of mucilage presence, distribution, and accumulation, between data acquired from the same region at different dates or times. This enables quantitative spatio-temporal mapping of distributions and the potential of assessing the severity of outbreaks, without the requirement of manually prepared ground truth maps or carefully selected thresholds. Real hyperspectral data acquired by the PRISMA mission are used to validate the proposed approach qualitatively and quantitatively. The paper also discusses future directions and recommendations to enhance the performance and applicability of the proposed approach for marine mucilage analysis, and environmental monitoring in general.

Using the automated HYPERNETS hyperspectral system for multi-mission satellite ocean colour validation in the Río de la Plata, accounting for different spatial resolutions

Validation of water reflectance using in situ data is essential to ensure the quality of ocean colour satellite-derived products useful for water quality monitoring, like turbidity and chlorophyll-a concentration. Since December 2021, the HYPERNETS automated hyperspectral system has been collecting data in the optically complex and highly turbid waters of the Río de la Plata, an ideal scenario for testing atmospheric correction algorithms’ performance. The site, located 60 km south of Buenos Aires (Argentina), is described in relation to the water reflectance spectral features and variability using high spatial resolution imagery and a methodology is proposed to objectively select a sensor-specific location of a reference pixel for satellite validation. Six months of data is used to evaluate surface water reflectance operational products from multi-spectral systems like Landsat 8&9/OLI (L89/OLI), Sentinel-2/MSI (S2/MSI) & Sentinel-3/OLCI (S3/OLCI), and PlanetScope SuperDoves (PS/SD), and also non standard products for Aqua/MODIS (Aqua/MODIS) and SNPP&JPSS1/VIIRS (SJ/VIIRS) missions. Moreover, the standard surface water reflectance product from the hyperspectral PRISMA mission could also be evaluated. The matchups show general good results when in situ measurements are compared to L2 standard products of high spatial resolution sensors that use land-based atmospheric correction approach, if sun glint contamination is avoided. Low mean relative percentage difference was found for S2/MSI (2.45%) and L89/OLI (−3.52%), but higher for PS/SD (30.7%). In turn, S3/OLCI medium resolution also showed low mean relative differences (2.31%), while SJ/VIIRS and Aqua/MODIS showed larger and negative differences (−16.35 for SJ/VIIRS and −35.6% for Aqua/MODIS) which showed a clear increase towards the shortest blue bands. The results show the great potential of the HYPERNETS automated system to provide high quality and quantity of data for validation of satellite data at all visible and near infrared (VNIR, 400–900 nm) wavelengths in a multi-mission perspective.

Lithological mapping of Nidar ophiolite complex, Ladakh using high-resolution data

Ophiolites represents a distinctive variety of igneous rock that comprises fragments of oceanic crust with the underlying mantle portion. Their mineral assemblages and distribution relation, both spatial and temporal are in close association with the complex mantle dynamics of the Earth. Nidar ophiolite complex located towards SE of Ladakh is one of such ophiolite assemblages exposed well in the Indus Suture Zone of Himalayas. It represents parts of Neo-Tethys ocean that existed during Mesozoic-Cenozoic period between continental plates of India and Eurasia. The earlier studies for Indian ophiolites are mostly field and analytical based with a limited work carried out using remote sensing techniques and that adds scope to apply this approach for further exploration of ophiolites. Advanced tools and techniques especially, hyperspectral remote sensing and machine learning based approach can be effective operational tools for exploration of ophiolites that provide assessable terrains for characterization of Earth’s mantle, serve analogs for planetary crustal rocks and also have economic significance. This study has utilized high-resolution multispectral (Landsat OLI and ASTER) and hyperspectral data from various missions including the recent PRISMA mission which has provided the first hyperspectral datasets for lithological mapping of Nidar ophiolite section. The multiple high-resolution datasets were used for identification and mapping of the various lithounits of the Nidar ophiolites together with the underlying and overlying associated rocks based on band ratioing, derived spectral compositional indices and spectral characterization. The differentiated lithounits of the Nidar ophiolites are interpreted based on spectral response of lithological units in different bands. In addition, classification based on machine learning techniques have been applied to the PRISMA data to generate a detailed classification map of the study area where the various lithological units of the ophiolites and associated rock exposures belonging to various geological formations are clearly discernible. The results have been validated from geological field visit to the study area and spectral analysis of the field samples.

First evaluation of fire severity retrieval from PRISMA hyperspectral data

The unprecedented availability of spaceborne hyperspectral data has great potential to provide fire severity estimates that align with post-fire management needs, overcoming complex logistics and data acquisition costs of airborne hyperspectral sensors, and the suboptimal sensitivity of broadband data to several post-fire ground components. We analyzed the feasibility of the PRISMA mission -one of the first spaceborne spectrometers operationally active- to assess fire severity by leveraging hyperspectral data dimensionality through the retrieval of sub-pixel components directly related to fire severity in the field. Multispectral data provided by Sentinel-2, commonly used in fire severity quantitative assessments, were used as a benchmark method. Multiple endmember spectral mixture analysis (MESMA) was used to retrieve fractional cover of char, photosynthetic vegetation (PV), as well as non-photosynthetic vegetation and soil (NPVS) from post-fire PRISMA Level 2D and Sentinel-2 Level 2A scenes in one of the largest wildfires ever recorded in the western Mediterranean Basin. Ground-truth data were obtained using the Composite Burn Index (CBI) to procure three field-measured severity metrics: vegetation, soil and site. The relationship between the CBI data on a continuum scale and the cover of char, PV and NPVS image fractions retrieved from PRISMA and Sentinel-2 was assessed through Random Forest regression (RFR). Likewise, Ordinal Forests (OF) algorithm was used to classify categorized CBI data (low, moderate and high fire severity). PRISMA-based RFR fire severity estimates at vegetation, soil and site levels (R2 = 0.64–0.79 and RMSE = 0.33–0.41) outperformed those of Sentinel-2 (R2 = 0.27–0.53 and RMSE = 0.54–0.60), and were in line with previous studies using airborne hyperspectral sensors at higher spatial resolution. Fire severity underestimation for high field CBI values was almost unnoticeable in the PRISMA estimates. Categorical fire severity, not currently estimated using hyperspectral data but with high interest in post-fire management, were accurately predicted by PRISMA-based OF classification, with consistent user's and producer's accuracy for each fire severity category. The high confusion between moderate and low/high fire severity categories, typical when unmixing broadband multispectral data, was overcome by the PRISMA-based classification scheme. Our results suggest that new spaceborne spectrometer missions can support reliable fire severity assessments equivalent to airborne spectrometers, but readily applicable to large-scale assessments of extreme wildfire events.

Panchromatic and Hyperspectral Image Fusion: Outcome of the 2022 WHISPERS Hyperspectral Pansharpening Challenge

This article presents the scientific outcomes of the 2022 Hyperspectral Pansharpening Challenge organized by the 12th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE WHISPERS 2022). The 2022 Hyperspectral Pansharpening Challenge aims at fusing a panchromatic image with hyperspectral data to get a high spatial resolution hyperspectral cube with the same spatial resolution of the panchromatic image while preserving the spectral information of hyperspectral data. Four datasets acquired by the PRISMA mission owned and managed by the Italian Space Agency have been prepared for participants. They are made available for the benefit of the scientific community. Each dataset contains a panchromatic image and a hyperspectral cube with different spatial resolutions. More than 100 registrations have been received for the event. Four teams submitted their outcomes. Since no team actually outperformed the baseline provided by the organizers, the challenge was declared inconclusive and no winner was recognized.

Unmixing of Pollution-Associated Sea Snot in the Near Surface After Its Outbreak in the Sea of Marmara Using Hyperspectral PRISMA Data

The mucilage outbreak in the Sea of Marmara in the spring of 2021 has once again emphasized the importance of addressing climate and pollution associated hazards. Although multispectral images have traditionally been used for such purposes, analysis of marine mucilage, with its spectral similarity to marine debris, and its spectral variations due to composition and/or sediment or bacterial aggregation, is a prime candidate to benefit from the advantages of hyperspectral data. The recently launched PRISMA mission provides an important opportunity to this end. This work proposes the use of unmixing on PRISMA datasets in order to analyze the spectral characteristics, the variation due to aggregation, and the spatial distribution, of marine mucilage. The proposed approach provides consistent and relevant information on two different datasets, with the potential to benefit cleaning and understanding efforts for marine mucilage. Additionally, unlike the previous studies with supervised classification, the proposed approach does not require a training step, and the abundance fraction maps obtained using unmixing are easy to interpret and analyze for mucilage aggregation.

Automatic Detection and Correction of Defective Pixels in PRISMA Hyperspectral Data

In satellite hyperspectral sensors, a standard procedure based on homogeneous reference sources is used to regularly update the map of defective pixels (DPs). Unfortunately, this procedure often fails to detect some DPs both because they may arise in the interval between two standard calibration steps and because they may be characterized by subtle or unexpected signal values. The resulting hyperspectral image is affected by residual nonuniformity noise which correlates in the along track direction. This noise source reduces the quality of hyperspectral products, such as classification, unmixing and material detection. In this paper, we present a new procedure to find the location of the residual DPs in the detection matrix and we also propose an effective method to estimate the missing radiance values inferring them from the image pixels by leveraging both spatial and spectral correlation. The procedure, here tailored to PRISMA hyperspectral images, is quite general and can be easily adapted to process images recorded by any satellite pushbroom hyperspectral sensor. The improved image quality yielded by the proposed procedure is first demonstrated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">qualitatively</i> , by comparing the GRX maps on the original image with those obtained after detection of the DPs and correction of their radiance values. The image quality improvement is then <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quantified</i> on a set of nine PRISMA images, recorded in an interval of about two years, using two <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad-hoc</i> defined indexes. The analysis is carried out separately for the VNIR and SWIR spectrometers of the PRISMA mission.

Mapping methane point emissions with the PRISMA spaceborne imaging spectrometer

The detection and repairment of methane leaks from fossil fuel production activities have been identified as a key climate change mitigation strategy. Several types of optical satellite sensors have recently shown potential to support this task. Spaceborne imaging spectrometers measuring in the 400–2500 nm spectral range belong to this group. These instruments measure the solar radiation reflected by the Earth in hundreds of spectral channels with a typical spectral resolution of 10 nm and a spatial resolution of 30 m. The PRISMA mission from the Italian Space Agency is the first system of this type providing data openly to the international scientific community. In this work, we evaluate the potential of PRISMA to map methane point emissions, which are typical for fossil fuel production activities. Our retrieval of methane concentration enhancements is based on a matched-filter based algorithm applied to PRISMA spectra in the 2300 nm shortwave infrared spectral region. We perform a simulation-based sensitivity analysis to assess the retrieval performance for different sites. We find that surface brightness and homogeneity are major drivers for the detection and quantification of methane plumes with PRISMA, with retrieval precision errors ranging from 61 to 197 parts-per-billion in the evaluated images. The potential of PRISMA for methane mapping is further illustrated by real plume detections at different methane hotspot regions, including oil and gas extraction fields in Algeria, Turkmenistan, and the USA (Permian Basin), and coal mines in the Shanxi region in China. Our study reports several important findings regarding the potential and limitations of PRISMA for methane mapping, most of which can be extrapolated to upcoming satellite imaging spectroscopy missions.

The PRISMA imaging spectroscopy mission: overview and first performance analysis

The PRISMA satellite mission launched on March 22nd, 2019 is one of the latest spaceborne imaging spectroscopy mission for Earth Observation. The PRISMA satellite comprises a high-spectral resolution VNIR-SWIR imaging spectrometer and a panchromatic camera. In summer 2019, first operations during the commissioning phase were mainly devoted to acquisitions in specific areas for evaluating instrument functioning, in-flight performance, and mission data product accuracy. A field and airborne campaign was carried out over an agriculture area in Italy to collect in-situ multi-source spectroscopy measurements at different scales simultaneously with PRISMA. The spectral, radiometric and spatial performance of PRISMA Level 1 Top-Of-Atmosphere radiance (LTOA) product were analyzed. The in-situ surface reflectance measurements over different landcovers were propagated to LTOA using MODTRAN5 radiative transfer simulations and compared with satellite observations. Overall, this work offers a first quantitative evaluation about the PRISMA mission performance and imaging spectroscopy LTOA data product consistency. Our results show that the spectral smile is less than 5 nm, the average spectral resolution is 13 nm and 11 nm (VNIR and SWIR respectively) and it varies ±2 nm across track. The radiometric comparison between PRISMA and field/airborne spectroscopy shows a difference lower than 5% for NIR and SWIR, whereas it is included in the 2–7% range in the VIS. The estimated instrument signal to noise ratio (SNR) is ≈400–500 in the NIR and part of the SWIR (<1300 nm), lower SNR values were found at shorter (<700 nm) and longer wavelengths (>1600 nm). The VNIR-to-SWIR spatial co-registration error is below 8 m and the spatial resolution is 37.11 m and 38.38 m for VNIR and SWIR respectively. The results are in-line with the expectations and mission requirements and indicate that acquired images are suitable for further scientific applications. However, this first assessment is based on data from a rural area and this cannot be fully exhaustive. Further studies are needed to confirm the performance for other land cover types like snow, inland and coastal waters, deserts or urban areas.

Neural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous

This article presents the Spacecraft Pose Network (SPN), the first neural network-based method for on-board estimation of the pose, i.e., the relative position and attitude, of a known noncooperative spacecraft using monocular vision. In contrast to other state-of-the-art pose estimation approaches for spaceborne applications, the SPN method does not require the formulation of hand-engineered features and only requires a single grayscale image to determine the pose of the spacecraft relative to the camera. The SPN method uses a convolutional neural network (CNN) with three branches to solve the problem of relative attitude estimation. The first branch of the CNN bootstraps a state-of-the-art object detection algorithm to detect a 2-D bounding box around the target spacecraft in the input image. The region inside the 2-D bounding box is then used by the other two branches of the CNN to determine the relative attitude by initially classifying the input region into discrete coarse attitude labels before regressing to a finer estimate. The SPN method then estimates the relative position by using the constraints imposed by the detected 2-D bounding box and the estimated relative attitude. Further, with the detection of 2-D bounding boxes of subcomponents of the target spacecraft, the SPN method is easily generalizable to estimate the pose of multiple target geometries. Finally, to facilitate integration with navigation filters and perform continuous pose tracking, the SPN method estimates the uncertainty associated with the estimated pose. The secondary contribution of this article is the generation of the Spacecraft PosE Estimation Dataset (SPEED), which is used to train and evaluate the performance of the SPN method. SPEED consists of synthetic as well as actual camera images of a mock-up of the Tango spacecraft from the PRISMA mission. The synthetic images are created by fusing OpenGL-based renderings of the spacecraft's 3-D model with actual images of the Earth captured by the Himawari-8 meteorological satellite. The actual camera images are created using a seven degrees-of-freedom robotic arm, which positions and orients a vision-based sensor with respect to a full-scale mock-up of the Tango spacecraft with submillimeter and submillidegree accuracy. The SPN method, trained only on synthetic images, produces degree-level relative attitude error and cm-level relative position errors when evaluated on the actual camera images with a different distribution not used during training.

Precise line-of-sight modelling for angles-only relative navigation

This work presents a precise analytical model to reconstruct the line-of-sight vector to a target satellite over time, as required by angles-only relative navigation systems for application to rendezvous missions. The model includes the effects of the geopotential, featuring: the analytical propagation in the mean relative orbital elements (up to second-order expansion), the analytical two-way osculating/mean orbital elements’ conversion (second-order in J2 and up to a given degree and order of the geopotential), and a second-order mapping from the perturbed osculating elements’ set to the local orbital frame. Performances are assessed against the line-of-sight reconstructed out of the precise GPS-based positioning products of the PRISMA mission. The line-of-sight modelled over a far-range one day long scenario can be fitted against the true one presenting residuals of the order of ten arc-seconds, which is below the typical sensor noise at far-range.

Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous

This work addresses the design and validation of a robust monocular vision-based pose initialization architecture for close-range onorbit-servicing and formation-flying applications. The aim is to rapidly determine the pose of a passive space resident object using its known three-dimensional wireframe model and a single low-resolution two-dimensional image collected on board the servicer spacecraft. In contrast to previous works, the proposed architecture is onboard executable and capable of estimating the pose of the client without the use of fiducial markers and without any a priori range measurements or state information. A novel feature detection method based on the filtering of the weak image gradients is used to identify the true edges of the client in the image, even in presence of the Earth in background. The detected features are synthesized using simple geometric constraints to dramatically reduce the search space of the feature correspondence problem, which is solved using the method. This approach is proven to be an order of magnitude faster than the state-of-the-art random sample consensus methods. A fast Newton–Raphson method that minimizes the fit error between corresponding image and model features is employed to refine the pose estimate and to resolve pose ambiguity. The proposed methodology is tested using actual space imagery collected during the PRISMA mission at about a 700 km altitude and a 10 m interspacecraft separation.

Radio-frequency sensor fusion for relative navigation of formation flying satellites

The increasing need for robustness, reliability and flexibility of relative navigation systems imposed by the current and future autonomous formation flying missions, calls for the implementation of solutions using an alternative approach to single-sensor systems. These schemes possess different levels of availability, mainly driven by sensor failure and mission orbit profiles. The data obtained from the global positioning system (GPS) and formation flying radio-frequency (FFRF) sensors from the nominal phase of the PRISMA mission provide an opportunity for the implementation of an extended approach for the generation of relative navigation solutions. The present study introduces the design of a simple relative navigation filter based on the sensor fusion concept, which makes use of differential GPS and FFRF measurements obtained from the on-board sensors. The filter design uses the Hill-Clohessy-Wiltshire and Yamanaka-Ankersen relative dynamics models as well as numerical integration methods including the J2-perturbation for the state and associated covariance matrix propagation. The design is tested using flight data obtained during representative formation flying operations of the PRISMA mission.

Flight demonstration of formation flying capabilities for future missions (NEAT pathfinder)

PRISMA is a demonstration mission for formation-flying and on-orbit-servicing critical technologies that involves two spacecraft launched in low Earth orbit in June 2010 and still in operation. Funded by the Swedish National Space Board, PRISMA mission has been developed by OHB-Sweden (formerly Swedish Space Corporation) with important contributions from the German Aerospace Centre (DLR/GSOC), the French Space Agency (CNES), and the Technical University of Denmark (DTU). The paper focuses on the last CNES experiment achieved in September 2012 that was devoted to the preparation of future astrometry missions illustrated by the NEAT and µ-NEAT mission concepts. The experiment consisted of performing the type of formation maneuvers required to point the two-satellite axis to a celestial target and maintain it fixed during the observation period. Achieving inertial pointing for a LEO formation represented a new challenge given the numerous constraints from propellant usage to star tracker blinding. The paper presents the experiment objectives in relation with the NEAT/µ-NEAT mission concept, describes its main design features along with the guidance and control algorithms evolutions and discusses the results in terms of performances achieved during the two rehearsals.

Pose estimation of an uncooperative spacecraft from actual space imagery

This paper addresses the preliminary design of a spaceborne monocular vision-based navigation system for on-orbit-servicing and formation-flying applications. The aim is to estimate the pose of a passive space resident object using its known three-dimensional model and single low-resolution two-dimensional images collected on-board the active spacecraft. In contrast to previous work, no supportive means are available on the target satellite (e.g., light emitting diodes) and no a-priori knowledge of the relative position and attitude is available (i.e., lost-in-space scenario). Three fundamental mechanisms – perceptual organisation, true perspective projection, and random sample consensus – are exploited to overcome the limitations of monocular passive optical navigation in space. The preliminary design is conducted and validated making use of actual images collected in the frame of the PRISMA mission at about 700 km altitude and 10 m inter-spacecraft separation.

Influence of sensor and actuator errors on impulsive satellite formation control methods

This paper investigates how sensor and actuator errors are impacting formation control accuracy and propellant consumption of a two-satellite formation in a low Earth orbit. Realistic relative navigation errors are implemented, based on the results from the PRISMA mission, as well as realistic actuator uncertainty and actuator constraints. Two impulsive control methods are investigated. The first method is based on a controller that is implemented onboard PRISMA and the second method uses linear programming to arrive at a model predictive controller. The control methods are tested in a simulation environment and are subjected to orbital perturbations and realistic sensor errors and actuator errors. Both control methods are able to maintain the desired relative geometry of a projected circular orbit in the presence of the errors. The PRISMA control method demonstrates lower propellant consumption, while the model predictive controller shows better control accuracy. The results show that, based on the used scenario, sensor errors dominate both the formation control accuracy and propellant consumption. The versatility of the model predictive controller is demonstrated in a challenging formation control scenario including formation maintenance and formation reconfiguration tasks.

3-Axis magnetic control: flight results of the TANGO satellite in the PRISMA mission

PRISMA implements guidance, navigation and control strategies for advanced formation flying and rendezvous experiments. The project is funded by the Swedish National Space Board and run by OHB-Sweden in close cooperation with DLR, CNES and the Danish Technical University. The PRISMA test bed consists of a fully manoeuvrable MANGO satellite as well as a 3-axis controlled TANGO satellite without any ΔV capability. PRISMA was launched on the 15th of June 2010 on board DNEPR. The TANGO spacecraft is the reference satellite for the experiments performed by MANGO, either with a “cooperative” or “non-cooperative” behaviour. Small, light and low-cost were the keywords for the TANGO design. The attitude determination is based on Sun sensors and magnetometers, and the active attitude control uses magnetic torque rods only. In order to perform the attitude manoeuvres required to fulfil the mission objectives, using any additional gravity gradient boom to passively stabilize the spacecraft was not allowed. After a two-month commissioning phase, TANGO separated from MANGO on the 11th of August 2010. All operational modes have been successfully tested, and the pointing performance in flight is in accordance with expectations. The robust Sun Acquisition mode reduced the initial tip-off rate and placed TANGO into a safe attitude in <30 min. The Manual Pointing mode was commissioned, and the spacecraft demonstrated the capability to follow or maintain different sets of attitudes. In Sun/Zenith Pointing mode, TANGO points its GPS antenna towards zenith with sufficient accuracy to track as many GPS satellites as MANGO. At the same time, it points its solar panel towards the Sun, and all payload equipments can be switched on without any restriction. This paper gives an overview of the TANGO Attitude Control System design. It then presents the flight results in the different operating modes. Finally, it highlights the key elements at the origin of the successful 3-axis magnetic control strategy on the TANGO satellite.

Flight Results of Precise Autonomous Orbit Keeping Experiment on PRISMA Mission

The autonomous orbit keeping experiment on the PRISMA mission was executed successfully from 18 July to 16 August 2011 and has demonstrated the capability of autonomous precise absolute orbit control. Using GPS-based absolute navigation data, the onboard controller commanded thruster activations in the orbital frame to autonomously control the satellite’s longitude of the ascending node within a predefined window. The main performance requirement of the experiment was a control accuracy of 10 m () with a maneuver velocity increment and decrement available budget of . After a four day commissioning phase, the reference orbit was acquired. A 3.5 day controller tuning was then followed by the fine orbit control phase, which commenced on 30 July 2011 and continued until the end of the experiment. The control accuracy requirement was fulfilled. The mean value of the longitude of the ascending node’s deviation was with a standard deviation of 9.5 m during the fine control phase. The total spent during the entire experiment was , corresponding to 27% of the maneuvers budget allocated.

Results of PRISMA/FFIORD extended mission and applicability to future formation flying and active debris removal missions

CNES performed several experiments during the extended PRISMA mission which started in August 2011. A first session in October 2011 addressed two objectives: 1) demonstrate angles-only navigation to rendezvous with a non-cooperative object; 2) exercise transitions between RF-based and vision-based control during final formation acquisition. A complementary experiment in September 2012 mimicked some future astrometry mission and implemented the manoeuvres required to point the two satellite axis to a celestial target and maintain it fixed during some observation period. In the first sections, the paper presents the experiment motivations, describes its main design features including the guidance and control algorithms evolutions and provides a synthesis of the most significant results along with a discussion of the lessons learned. In the last part, the paper evokes the applicability of these experiment results to some active debris removal mission concept that is currently being studied.