Apparent rotation properties of space debris extracted from photometric measurements

Over the past 60 years, satellite technology has demonstrated its usefulness successfully. However, this usefulness is at stake from a future point of view, due to the well-admitted orbital/space debris threat. This article thoroughly reviews all aspects of space debris issue including causes, amount and sizes of orbital debris, potential threats, counter-strategies with their latest status and related legal issues to highlight the criticality and urgency of the problem. This review elaborates the fact that despite all the worries and threats, the efforts to confront this challenge are considerably insufficient until today. This bitter reality demands for at-least curtailing the number of future launches to ensure the long-term sustainability of space, until the improvement in debris situation. However, contradictory to this necessity, large satellite constellations have been proposed that can drastically increase the existing orbital population in coming years. This approach will certainly not help in improving the space environment in the future; instead, it can worsen the space environment situation as recent studies shows. Also, space resources (i.e. orbital slots and frequencies) are limited to accommodate many more satellite projects from commercial and government organization in the future. So, there is a serious question of how the space industry can move forward to maintain a balance in controlling the future number of the satellite while accommodating many commercial or government space entities. This article also identifies two optimized approaches as a way forward for future satellite projects that can also enhance the effectiveness of space technology in the future.

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Orbital Debris Threat for Space Sustainability and Way Forward (Review Article)

Analysis of the attitude and geometry of space targets with the inverse synthetic aperture radar (ISAR) technique is a significant and difficult task. Most of the existing methods hardly consider the radar observation geometry in the determination of target attitude. This paper proposes a novel approach to estimating the three-dimensional attitude and reconstructing typical component geometry of space targets from an ISAR image sequence. The approach bridges range-Doppler images and target attitude parameters with the accommodation of target trajectory information and the ISAR geometric projection model. By exploring the shape feature within the ISAR sequence, the target attitude and the rectangular component size are estimated through solving an optimization with prior shape constraints. Comparative experiments illustrate the advantages of the proposed method in both feature association and reconstruction feasibility. Moreover, considering practical circumstances, a further analysis is made of the robustness of the proposed algorithm after the attitude estimation experiment.

Attitude Estimation and Geometry Reconstruction of Satellite Targets Based on ISAR Image Sequence Interpretation

Spin motion determination of the Envisat satellite through laser ranging measurements from a single pass measured by a single station

Space debris detection is important in space situation awareness and space asset protection. In this article, we propose a method to detect space debris using feature learning of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.

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Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Man-made space debris orbit around the Earth at many altitudes in different orbits and many launches add further debris to already congested orbits. The European Space Agency (ESA) GSTP-funded Optical IN-SITU Monitor project started in 2016 and aims to analyse the end-to-end processing pipeline from image acquisition to the extracted positions of space debris. A camera will take images and an on-board data processor will filter and compress the images obtained, which – in case of a future flight model of the instrument – shall be downlinked to the ground station. For the image filtering and compression, several data reduction algorithms are considered and also two different kinds of processors are considered. The difficulty of the images is to differentiate between stars in the background (which have to be filtered out) and the debris. A short overview of the project is presented in this paper: its current status, the hardware choices and the data reduction algorithm trade-off.

The use of streak observations to detect space debris

Space debris population increased drastically during the last years. One of the contributing factors is the incidental collisions involving massive objects which are predicted to be more pronounced in the future. The removal of large, massive space debris is considered necessary to stabilize the population. In this respect, not only precise orbits, but also more detailed information about their attitude states such as spin period and spin axis orientation is required. Non-resolving optical observations of the magnitude variations, so-called light curves, are a promising technique to determine the tumbling rates and the orientations of the actual objects' rotational axis, as well as their temporal changes. For this purpose, we use the 1-meter telescope ZIMLAT at the Astronomical Institute of the University of Bern (AIUB) to collect light curves of selected LEO, MEO and GEO objects on a regular basis. We have acquired more than 3,000 light curves from 512 objects in various types since January 2007. By analysing the light curve in the AIUB light curve database we could determine that 
most of the objects in the database were rotators (with known or unknown periods)
. They were located in all orbital regions unlike stable objects which were not found in high elliptical orbit. In addition, rotators, slow rotators (rotators but with unknown periods)
, and stable objects consist of all object types which include payloads, rocket bodies, and debris. Slow rotators and stable objects mostly located in low earth orbits while rotators mostly located in other orbital regions. Periods of rotation in the 
database ranged from less than 1 sec to nearly 900 sec. We identified three patterns in spin rate evolution of the rotating objects in the database which showed oscillating, increasing, or decreasing trends. There were 36 oscillating rotators, 10 increasing rotators, and 18 decreasing rotators. All the oscillating rotators were GLONASS satellites while increasing and decreasing rotators were distributed among payloads and rocket bodies in various orbital regions.

Attitude State Evolution of Space Debris Determined from Optical Light Curve Observations

Since 2007 the Astronomical Institute of the University of Bern (AIUB) is using its 1-meter telescope ZIMLAT situated at the Zimmerwald observatory, Switzerland to collect light curves of space debris objects including defunct 
spacecraft, upper stages and fragments. The last two and half years, 2014,
2015 and 2016, can be considered as the most productive for the Zimmerwald light curve acquisition. Several different photometric campaigns have been 
performed which were focused on Low Earth Orbit (LEO) targets suitable for future Active Debris Removal (ADR) missions, on regular monitoring of large defunct spacecraft in LEO and in the GLONASS constellation, and on new 
objects discovered during the Zimmerwald and ESA Geosynchronous Earth Orbit (GEO) surveys. From each lightcurve we extracted some information about the attitude motion. This information includes a general characterization 
of the attitude behaviour, like “stable” or “tumbling” state, and, if applicable
, also the determination of the apparent spin period and the reconstruction of the phase. Our paper will discuss the photometry observation techniques currently in use at the Zimmerwald observatory and the processing of the light curves with the aim to determine apparent spin periods and to reconstruct the
irphases. Spin rates for objects acquired with the ZIMLAT telescope during the last two years will be discussed and the morphology of their reconstructed phases will be briefly presented.

Light curve database of Astronomical Institute of the University of Bern

The population of space debris increased drastically during the last years. Collisions involving massive objects may produce large number of fragments leading to significantly growth of the space debris population. An effective remediation measure in order to stabilize the population in LEO is therefore the removal of large, massive space debris. To remove these objects, not only precise orbits, but also more detailed 
information about their attitude states will be required. One important property of an object targeted for removal is its spin period and spin axis orientation. Non-resolving optical observations of the magnitude variations, so-called light curves, are a promising technique to determine rotation or tumbling rates and the orientations of the actual rotation axis of objects, as well as their temporal changes. Acquiring such observations as well extracting attitude states from these measurements is challenging and requires sophisticated observations and processing techniques. The paper will introduce the corresponding techniques and methods used at the Astronomical Institute of the University of Bern (AIUB). The 1-meter telescope ZIMLAT of the AIUB is used to collect light curves of LEO, MEO and GEO objects on a regular basis. Recently, Satellite Laser Ranging (SLR) observations of rotating LEO targets were acquired as well. We will present spin rates and their temporal evolution for a large set of decommissioned LEO, MEO and HEO spacecraft and upper stages, including more than 60 abandoned GLONASS satellites. These results were derived from more than 2000 light curves and several hundred SLR passes.

Attitude States of Space Debris determined from Optical Light Curve Observations

The Difference Method algorithm is developed within the European Space Agency (ESA) ”Optical In-Situ Monitor” project by the Astronomical Institute of the University of Bern together with Airbus Defence and Space. Main objectives are the autonomous on-board data reduction, consisting of image segmentation and object detection. 
These steps are critical in order to achieve an effective on-board processing pipeline optimizing the downlink bandwidth usage. Then, complementary analysis regarding astrometry, photometry and debris characterizations can be performed through the related on-ground processing chain. The baseline of the Difference Method is to process two successive exposures and detect moving objects (space debris) by performing a refined frames subtraction. This method is composed of five main parts which are: star detection, frame alignment, frame subtraction, data selection and data compression. 
This algorithm shows very convincing results. It can process two frames and detect faint debris streaks, down to peak SNR2 in less than 1 second of execution time, fulfilling rigorous requirements concerning on-board computation power, available da
ta bandwidth and streaks detection limits.

Jean-Noël Pittet

Papers

The use of streak observations to detect space debris

Attitude State Evolution of Space Debris Determined from Optical Light Curve Observations

Light curve database of Astronomical Institute of the University of Bern

Attitude States of Space Debris determined from Optical Light Curve Observations

The Difference Method: a simple and effective on-board algorithm for space debris detection