Small Satellite Reliability: A Decade in Review

Advances in the small satellite combined with the availability of low-cost launches have led to an increasing number of space missions. As space is more accessible than ever before, new and innovative missions arise. A broad understanding of reliability trends on satellites can guide these future missions towards success. As a result, more and more space industries are focusing on reliability in the early stages of the design. The goal of this paper is to investigate the reliability of small satellites launched over the last three decades. Satellites launched between January 1990 - December 2019 and weighing between 40kg - 500kg are considered for this study. The dataset consists of 866 Earth-orbiting satellites. This study utilizes Kaplan-Meier estimator for calculating non-parametric reliability functions. The reliability results are then used to fit parametric models such as Weibull distribution to identify reliability trends. The dataset is further categorized based on satellite mission, launch year, developer, design life and orbit inclination to analyze their specific reliability trends. Finally, the contribution of satellite subsystems to satellite failure is quantified for this dataset and the subsystems with high(er) propensity for failure are identified. The results obtained in this study shall help in reliability/redundancy allocation of small satellites and its subsystems. It also supports small satellite design decisions, testing strategies and developing reliability growth plans for future missions. Furthermore, understanding the reliability trends in the past decades could potentially improve the reliability of small satellite in this current decade

Relative State Estimation for LEO Formations with Large Inter-satellite Distances Using Single-Frequency GNSS Receivers

Relative baseline estimation is an integral part of satellite formation flying missions. GNSS-based relativepositioning has been a dominating technology for formation missions in LEO, where very precise estimatescould be obtained for formations with small inter-satellite distances (1 − 10 km). Larger baselines betweenthe satellites (> 10 km) pose the problem of considerable differences in the ionospheric delays experiencedby the signals received by each receiver. This problem could be mitigated by using precise ionospheric-freecombinations that could only be obtained by dual-frequency receivers, which is not a cost-efficient optionfor modern low-cost miniature missions. In this paper, the problem of relative baseline vector estimation isaddressed for formation missions with large inter-satellite distances equipped with single-frequency receivers.The problem is approached using the space-proven relatively simple Extended Kalman Filter with anadvantageous setting for the observation vecto

GNSS-based baseline vector determination for widely separated cooperative satellites using L1-only receivers

peer reviewedReal time estimation of the relative position and velocity vectors between two satellites in a formation is an integral part of the formation control loop. Relative positioning based on Global Navigation Satellite Systems (GNSS) has been a dominating technology for formation missions in LEO, where extremely precise estimates could be obtained for formations with small inter-satellite distances (1-10 km). Larger baselines between the satellites (>10 km) are more challenging as they pose the problem of huge differences in the ionospheric delays experienced by the signals received by each receiver. This problem could be mitigated by using precise ionospheric-free combinations that could only be obtained by dual-frequency receivers, which is not a cost-efficient option for the modern low-cost miniature missions. In this paper, the problem of GNSS-based relative navigation between two spacecraft with large inter-satellite distance which are equipped with single-frequency receivers is treated through adopting the space-proven Extended Kalman Filter (EKF). Although using an EKF for relative navigation is a common practice, there are many variants of the filter settings, which vary in terms of the state and measurement vectors to be adopted as well as the techniques to be used to handle the ionospheric delay. In this research, optimal settings of the filter are sought for the problem of relative baseline vector estimation between two spacecraft that have large separation and which are equipped with with single-frequency GNSS receivers.Autonomous Constellation and Formation Control of Microsatellite

Discrete Wavelet Transform and Optimal Spectral Transform Applied to Multicomponent Image Coding

ISBN 978-953-307-482-5 Chapitre 10International audienc

THE H2020 PROJECT REDSHIFT: OVERVIEW, FIRST RESULTS AND PERSPECTIVES

The ReDSHIFT (Revolutionary Design of Spacecraft through Holistic Integration of Future Technologies) project has been approved by the European Community in the framework of the H2020 Protec 2015 call, focused on passive means to reduce the impact of Space Debris by prevention, mitigation and protection. In ReDSHIFT these goals will be achieved through a holistic approach that considers, from the outset, opposing and challenging constraints for the space environment preservation, the spacecraft survivability in the harsh space environment and the safety of humans on ground. The main innovative aspects of the project concern a synergy between theoretical and experimental aspects, such as: long term simulations, astrodynamics, passive de-orbiting devices, 3D printing, design for demise, hypervelocity impact testing, legal and normative issues. The paper presents a quick overview of the first ReDSHIFT results in an effort to highlight the holistic approach of the project covering different aspects of the space debris mitigation field. De- tailed reports on the results of the single Work Packages can be found in other papers in this same volume

Onboard Hyperspectral images compression with exogenous quasi optimal coding transforms

In previous works, we defined the Optimal Transform Code (OTC) assuming high rate coding and using the asymptotical Bennett approximation of the rate. We showed that the OTC gives the optimal linear transform of a multicomponent image compression scheme which consists in applying a linear transform that adapts to the encoded image for reducing the spectral redundancy and a fixed 2-D Discrete Wavelet Transform (DWT) per component for reducing the spatial redundancy. The performances in terms of rate vs PSNR (Peak of Signal to Noise Ratio) are very attractive when evaluated with the Verification Model version 9 of the JPEG2000 committee which is a JPEG2000 codec (coding-decoding). The transform in OTC performs better than the Karhunen Loeve Transform (KLT). The drawback of the OTC is its high computing complexity, since the optimal linear transform must be computed for each encoded image. In order to implement the OTC in an on- board satellite real-time codec system, we propose to pass round the problem of computing complexity by learning only one fixed transform with the OTC algorithms from a set of images instead of computing a new transform for each image. We call the fixed transform computed in this way an exogenous quasi-optimal linear transform. In this paper, we focus the study on hyperspectral images. Our set of images is constituted of ten Hyperion3 hyperspectral images. We have separated the VNIR and the SWIR bands (since they are obtained with two different sensors on- board) and we just focus on the VNIR spectral bands

Comparison of Multidisciplinary Design Optimization Architectures for the design of Distributed Space Systems

Advancement in satellite technology, and the ability to mass-produce cost-effective small satellites has created a compelling interest in Distributed Space System (DSS), such as Low Earth Orbit (LEO) satellite constellations. Optimization of DSS is a complex Multidisciplinary Design Optimization (MDO) problem involving a large number of variables and coupling relations. This paper focuses on comparing three different MDO architectures for a DSS design problem. Initially, an overview of the constellation model, the subsystems model, and the coupling relationships between the subsystems and the constellation are provided. The modelling of the subsystems and the constellation configuration are carried out in OpenMDAO. Later, three monolithic MDO architectures, namely, Individual Discipline Feasible (IDF), Simultaneous Analysis and Design (SAND) and Multidisciplinary Feasible (MDF) are compared by implementing them to the developed DSS model. The results indicate IDF outperforms the rest of the architectures for the conceptual design of DSS. The optimum objective function obtained by IDF is 1% lower than SAND and 7% lower than MDF. While the functional evaluation required for IDF is 50% lower than SAND and 90% lower than MDF

Lossy Hyperspectral Images Coding with Exogenous Quasi Optimal Transforms

International audienceIt is well known in transform coding that the Karhunen-Loève Transform (KLT) can be suboptimal for non Gaussian sources. However in many applications using JPEG2000 Part 2 codecs, the KLT is generally considered as the optimal linear transform for reducing redundancies between components of hyperspectral images. In previous works, optimal spectral transforms (OST) compatible with the JPEG2000 Part 2 standard have been introduced, performing better than the KLT but with an heavier computational cost. In this paper, we show that the OST computed on a learning basis constituted of Hyperion hyperspectral images issued from one sensor performs very well, and even better than the KLT, on other images issued from the same sensor

Lossy and lossless compression of MERIS hyperspectral images with exogenous quasi-optimal spectral transforms

International audienceOur research focuses on reducing complexity of hyperspectral image codecs based on transform and/or subband coding, so they can be on-board a satellite. It is well-known that the Karhunen Loeve transform (KLT) can be sub-optimal for non Gaussian data. However, it is generally recommended as the best calculable coding transform in practice. Now, for a compression scheme compatible with both the JPEG2000 Part2 standard and the CCSDS recommendations for onboard satellite image compression, the concept and computation of optimal spectral transforms (OST), at high bit-rates, were carried out, under low restrictive hypotheses. These linear transforms are optimal for reducing spectral redundancies of multi- or hyper-spectral images, when the spatial redundancies are reduced with a fixed 2-D discrete wavelet transform. The problem of OST is their heavy computational cost. In this paper we present the performances in coding of a quasi-optimal spectral transform, called exogenous OrthOST, obtained by learning an orthogonal OST on a sample of hyperspectral images from the spectrometer MERIS. Moreover, we compute an integer variant of OrthOST for lossless compression. The performances are compared to the ones of the KLT in both lossy and lossless compressions. We observe good performances of the exogenous OrthOST