Tracking Of Resident Space Objects Research Articles

Correction: Hussain et al. Passive Electro-Optical Tracking of Resident Space Objects for Distributed Satellite Systems Autonomous Navigation. Remote Sens. 2023, 15, 1714

There was an error in the original publication [...]

Passive Electro-Optical Tracking of Resident Space Objects for Distributed Satellite Systems Autonomous Navigation

Autonomous navigation (AN) and manoeuvring are increasingly important in distributed satellite systems (DSS) in order to avoid potential collisions with space debris and other resident space objects (RSO). In order to accomplish collision avoidance manoeuvres, tracking and characterization of RSO is crucial. At present, RSO are tracked and catalogued using ground-based observations, but space-based space surveillance (SBSS) represents a valid alternative (or complementary asset) due to its ability to offer enhanced performances in terms of sensor resolution, tracking accuracy, and weather independence. This paper proposes a particle swarm optimization (PSO) algorithm for DSS AN and manoeuvring, specifically addressing RSO tracking and collision avoidance requirements as an integral part of the overall system design. More specifically, a DSS architecture employing hyperspectral sensors for Earth observation is considered, and passive electro-optical sensors are used, in conjunction with suitable mathematical algorithms, to accomplish autonomous RSO tracking and classification. Simulation case studies are performed to investigate the tracking and system collision avoidance capabilities in both space-based and ground-based tracking scenarios. Results corroborate the effectiveness of the proposed AN technique and highlight its potential to supplement either conventional (ground-based) or SBSS tracking methods.

Ground-based laser momentum transfer concept for debris collision avoidance

Satellite laser ranging (SLR) is a well-established technology within the scientific community used since the early 1960s to precisely measure distances. The technology evolved in support of the geodetic research striving for high accuracy measurements which nowadays are achieved by means of high repetition, low energy pulse lasers used in combination with satellites equipped with retroreflectors. The achieved accuracy allowed not only for quality improvement of orbit determination products but also remote estimation of the attitude of the observed target.The SLR technique constitutes a highly accurate, relatively cheap alternative to radars for the tracking of orbiting targets. In the last decade, the successful tracking of resident space objects, not equipped with retroreflectors, made SLR a fundamental and appealing technique also in the space debris domain.In this study, we will introduce a possible step forward - thanks to the availability of commercial high power (> 10 kW) continuous wave (CW) lasers - which consists in the setup of a network of ground stations able to efficiently contribute to space debris collision avoidance manoeuvres in the low Earth orbit (LEO).This paper will summarize the achievements of a conceptual study on ground-based laser momentum transfer to LEO space debris performed by a consortium under the guidance of the German Aerospace Centre (DLR) funded by the European Space Agency (ESA) in the frame of the ESA Space Safety Programme.The study was carried out approaching the problem from an astrodynamics, physical, technological and legal point of view. The required tracking precision and the fundamental physics of the laser momentum transfer (LMT) were studied to evaluate the achievable thrust on LEO debris objects with commercially available components. An astrodynamics analysis was carried out to assess the efficiency of the imposed thrust and the consequences on the probability of collision in LEO. In the paper we will report the outcomes of the study which allowed us to define: the requirements of a laser tracking and momentum transfer (LTMT) station, the minimum size of an LTMT network for LEO collision avoidance operation, the current technological challenges, and gaps to be filled before its implementation.

Autonomous angles-only multitarget tracking for spacecraft swarms

This paper presents a new algorithm for autonomous multitarget tracking of resident space objects using optical angles-only measurements from a spaceborne observer. To enable autonomous angles-only navigation of spacecraft swarms, observers must identify and track multiple known or unknown target space objects in view, without reliance on a-priori relative orbit knowledge. Extremely high tracking precision is necessary despite low measurement frequencies and limited computational resources. The new ’Spacecraft Angles-only MUltitarget tracking System’ (SAMUS) algorithm has been developed to meet these objectives and constraints. It combines domain-specific modeling of target kinematics with multi-hypothesis techniques to autonomously track multiple unknown targets using only sequential camera images. A measurement transform ensures that target motion in the observer reference frame follows a consistent parametric model; curve fitting is used to predict track behavior; and kinematic track gating and scoring criteria improve the efficiency and accuracy of the multi-hypothesis approach. Monte Carlo testing with high-fidelity simulations demonstrates close to 100% data association precision and high recall across a range of multi-spacecraft formations, in both near-circular and eccentric orbits. Tracking is maintained in the presence of eclipse periods, significant measurement noise, and partially known swarm maneuvers. A comparison to other tracking algorithms reveals strong advantages in precision, robustness and computation time, crucial for spaceborne angles-only navigation.

Near earth space object detection using parallax as multi-hypothesis test criterion.

The US Strategic Command (USSTRATCOM) operated Space Surveillance Network (SSN) is tasked with Space Situational Awareness (SSA) for the U.S. military. This system is made up of Electro-Optic sensors, such as the Ground-based Electro-Optical Deep Space Surveillance (GEODSS) and RADAR based sensors, such as the Space Fence Gaps. They remain in the tracking of Resident Space Objects (RSO's) in Geosynchronous Orbits (GEO), due to limitations of SST and GEODSS system implementation. This research explores a reliable, ground-based technique used to quickly determine an RSO's altitude from a single or limited set of observations. Implementation of such sensors into the SSN would mitigate GEO SSA performance gaps. The research entails a method used to distinguish between the point spread function (PSF) observed by a star and the PSF observed from an RSO by using Multi-Hypothesis Testing with parallax as a test criterion. Parallax is the effect that an observed object will appear to shift when viewed from different positions. This effect is explored by generating PSFs from telescope observations of space objects at different baselines. The research has shown the PSF of an RSO can be distinguished from that of a star using single, simultaneous observations from reference and parallax sensing telescopes. This report validates these techniques with both simulations and experimental data from the SST and Naval Observatory sensors.

Visual tracking of resident space objects via an RFS-based multi-Bernoulli track-before-detect method

In this paper, we propose a fast and reliable track-before-detect approach to simultaneously detect, track, and identify an unknown and variable number of resident space objects (RSOs) without any prior information and any explicit detection, which leads to better space domain awareness. Specifically, we use the point spread function concept to propose a separable likelihood function as the observation model in the random finite set-based multi-Bernoulli filtering framework. This framework clearly distinguishes RSOs from any counterfeit objects and detects and tracks them immediately after their respective appearance in background cluttered telescope imagery data. The extensive experimental results on the TAOS dataset demonstrate the robustness of the proposed method in detecting and tracking RSOs with the average optimal subpattern assignment localization error less than 2 pixels in image sequences with the signal to noise ratio as low as 9 dB and under the conditions of varying illumination and occlusion.

Physics and human-based information fusion for improved resident space object tracking

Maintaining a catalog of Resident Space Objects (RSOs) can be cast in a typical Bayesian multi-object estimation problem, where the various sources of uncertainty in the problem – the orbital mechanics, the kinematic states of the identified objects, the data sources, etc. – are modeled as random variables with associated probability distributions. In the context of Space Situational Awareness, however, the information available to a space analyst on many uncertain components is scarce, preventing their appropriate modeling with a random variable and thus their exploitation in a RSO tracking algorithm. A typical example are human-based data sources such as Two-Line Elements (TLEs), which are publicly available but lack any statistical description of their accuracy. In this paper, we propose the first exploitation of uncertain variables in a RSO tracking problem, allowing for a representation of the uncertain components reflecting the information available to the space analyst, however scarce, and nothing more. In particular, we show that a human-based data source and a physics-based data source can be embedded in a unified and rigorous Bayesian estimator in order to track a RSO. We illustrate this concept on a scenario where real TLEs queried from the U.S. Strategic Command are fused with realistically simulated radar observations in order to track a Low-Earth Orbit satellite.

In-Orbit Tracking of Resident Space Objects: A Comparison of Monocular and Stereoscopic Vision

This paper develops new methods for vision-based satellite attitude control aimed at space-based optical tracking of resident space objects (RSOs). An Earth-orbiting chaser satellite equipped with either one or two body-fixed cameras can successfully track an RSO provided that the target is kept within the camera field of view. Because the cameras are body fixed, the attitude of the satellite needs to be controlled to maintain target lock. Novel vision-based control algorithms are developed to align the chaser camera's optical axis with the chaser-target line of sight. Two control architectures are presented for the cases of monocular and stereoscopic vision. In the case of the monocular architecture, relative chaser-target acceleration information is not available. Moreover, in both cases unknown perturbations can impair the tracking performance. To increase the tracking algorithm's robustness to these effects, a variable structure attitude control technique is employed. The stability of the developed control laws are substantiated based on Lyapunov's direct method and demonstrated using Monte Carlo simulations. The results clearly show that using stereoscopic vision yields faster target tracking, increased robustness to noise and field-of-view limits, and reduced fuel consumption compared with monocular vision-based attitude tracking.

Coupling of Estimation and Sensor Tasking Applied to Satellite Tracking

The tracking of resident space objects has been a topic of recent concern due to the numerous objects that must be monitored with respect to relatively few sensors that can observe them. This discrepancy creates situations in which sensors have multiple objects within their view and must decide which to observe and which to ignore at a particular time, a process known as sensor tasking or sensor network management. Previous studies have suggested calculating information- (or covariance-) based metrics to aid in this process, which rely on uncertainty estimates of an object’s location obtained using a nonlinear estimator. In these studies, the coupling between estimation and sensor tasking is investigated using two covariance-based tasking strategies in conjunction with two nonlinear estimators applied within a simple planar satellite-tracking simulation. Results demonstrate that the use of more accurate estimators leads to better overall estimates, not only due to the advantages within the estimation methods, but from the improvement in tasking decisions due to selection of these estimators. In addition, the paper introduces a new utility metric based on approximating stability of the estimation error through estimating a largest Lyapunov exponent, which is shown to outperform an existing Fisher information tasking approach.