Wingman-based Estimation and Guidance for a Sensorless PN-Guided Pursuer

A novel wingman-based estimation and guidance concept is proposed for a sensorless pursuer. The pursuer is guided towards a maneuvering aerial target using proportional navigation (PN) guidance law. The wingman is assumed to acquire bearings-only measurements of the target and to accurately track the wingman-pursuer relative position. The pursuer-target relative states, needed for the pursuer guidance law implementation, are estimated from the available data to the wingman. The proposed state estimator is implemented using extended Kalman filter equations and transformed wingman's measurements into the moving pursuer frame. Analytical observability analysis of the proposed wingman-based measuring concept suggests an optimal wingman trajectory in terms of the wingman-pursuer relative geometry. The resulting wingman trajectory ensures maximum observability of the pursuer-target line-of-sight (LOS) angle, which is a crucial parameter needed for the PN guidance law implementation. The resulting trajectory can be directly related to the well-known LOS guidance concept. Monte Carlo simulation results validate the analytical findings and demonstrate the potential of the proposed concept.Space Systems Egineerin

Magnetic Detumbling of Fast-tumbling Picosatellites

The problem of pure magnetic detumbling of a fast-tumbling picosatellite is considered. A new weighted B-dot control algorithm is proposed. The algorithm enables power reduction while not sacrificing detumbling performance. Analytical expressions relating the maximal expected rotational rate to the minimum sampling time required are presented. Simulation results demonstrate the practical benefits of the proposed approach for picosatellites.Space Systems EgineeringSpace Engineerin

Model-based FDI for Agile Spacecraft with Multiple Actuators Working Simultaneously

Fast and large-angle attitude slew maneuvers often imply simultaneous use of multiple actuators such as thrusters and reaction wheels (RWs). A fault in any of these actuators might lead to partial or full damage of sensitive spacecraft instruments. In this paper, a model-based Fault Detection and Isolation (FDI) strategy is proposed, which aims at detecting various actuator faults, such as stuck-open/closed thruster, thruster leakage, loss of effectiveness of all thrusters, and change of RW friction torque due to change of Coulomb and/or viscosity factor. The proposed FDI strategy is also able to detect and isolate faults affecting the RWs tachometer sensor. The FDI system design is based on a multiplicative extended Kalman filter and a generalized likelihood ratio thresholding of the residual signals. The performance and robustness of the proposed FDI strategy is evaluated using Monte Carlo simulations and carefully defined FDI performance indices. Preliminary results suggest promising performance in terms of detection/isolation times, miss-detection/isolation rates, and false alarm rates.Space Systems Egineerin

Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft

The relative pose estimation of an inactive target by an active servicer spacecraft is a critical task in the design of current and planned space missions, due to its relevance for close-proximity operations, i.e. the rendezvous with a space debris and/or in-orbit servicing. Pose estimation systems based solely on a monocular camera are recently becoming an attractive alternative to systems based on active sensors or stereo cameras, due to their reduced mass, power consumption and system complexity. In this framework, a review of the robustness and applicability of monocular systems for the pose estimation of an uncooperative spacecraft is provided. Special focus is put on the advantages of multispectral monocular systems as well as on the improved robustness of novel image processing schemes and pose estimation solvers. The limitations and drawbacks of the validation of current pose estimation schemes with synthetic images are further discussed, together with the critical trade-offs for the selection of visual-based navigation filters. The state-of-the-art techniques are analyzed in order to provide an insight into the limitations involved under adverse illumination and orbit scenarios, high image contrast, background noise, and low signal-to-noise ratio, which characterize actual space imagery, and which could jeopardize the image processing algorithms and affect the pose estimation accuracy as well as the navigation filter's robustness. Specifically, a comparative assessment of current solutions is given at different levels of the pose estimation process, in order to bring a novel and broad perspective as compared to previous works.Space Systems EgineeringSpace Engineerin

Comparative Assessment of Image Processing Algorithms for the Pose Estimation of Uncooperative Spacecraft

This paper reports on a comparative assessment of Image Processing (IP) tech- niques for the relative pose estimation of uncooperative spacecraft with a monocular camera. Currently, keypoints-based algorithms suffer from partial occlusion of the target, as well as from the different illumination conditions be- tween the required offline database and the query space image. Besides, al- gorithms based on corners/edges detection are highly sensitive to adverse il- lumination conditions in orbit. An evaluation of the critical aspects of these two methods is provided with the aim of comparing their performance under changing illumination conditions and varying views between the camera and the target. Five different keypoints-based methods are compared to assess the robustness of feature matching. Furthermore, a method based on corners ex- traction from the lines detected by the Hough Transform is proposed and evalu- ated. Finally, a novel method, based on an hourglass Convolutional Neural Net- work (CNN) architecture, is proposed to improve the robustness of the IP during partial occlusion of the target as well as during feature tracking. It is expected that the results of this work will help assessing the robustness of keypoints- based, corners/edges-based, and CNN-based algorithms within the IP prior to the relative pose estimation

Evaluation of tightly- and loosely-coupled approaches in CNN-based pose estimation systems for uncooperative spacecraft

The relative pose estimation of an inactive spacecraft by an active servicer spacecraft is a critical task in the design of current and planned space missions, due to its relevance for close-proximity operations, such as In-Orbit Servicing and Active Debris Removal. This paper introduces a novel framework to enable robust monocular pose estimation for close-proximity operations around an uncooperative spacecraft, which combines a Convolutional Neural Network (CNN) for feature detection with a Covariant Efficient Procrustes Perspective-n-Points (CEPPnP) solver and a Multiplicative Extended Kalman Filter (MEKF). The performance of the proposed method is evaluated at different levels of the pose estimation system. A Single-stack Hourglass CNN is proposed for the feature detection step in order to decrease the computational load of the Image Processing (IP), and its accuracy is compared to the standard, more complex High-Resolution Net (HRNet). Subsequently, heatmaps-derived covariance matrices are included in the CEPPnP solver to assess the pose estimation accuracy prior to the navigation filter. This is done in order to support the performance evaluation of the proposed tightly-coupled approach against a loosely-coupled approach, in which the detected features are converted into pseudomeasurements of the relative pose prior to the filter. The performance results of the proposed system indicate that a tightly-coupled approach can guarantee an advantageous coupling between the rotational and translational states within the filter, whilst reflecting a representative measurements covariance. This suggest a promising scheme to cope with the challenging demand for robust navigation in close-proximity scenarios. Synthetic 2D images of the European Space Agency's Envisat spacecraft are used to generate datasets for training, validation and testing of the CNN. Likewise, the images are used to recreate a representative close-proximity scenario for the validation of the proposed filter.Space Systems EgineeringSpace Engineerin

Comparative Assessment of Image Processing Algorithms for the Pose Estimation of Uncooperative Spacecraft

This paper reports on a comparative assessment of Image Processing (IP) tech- niques for the relative pose estimation of uncooperative spacecraft with a monocular camera. Currently, keypoints-based algorithms suffer from partial occlusion of the target, as well as from the different illumination conditions be- tween the required offline database and the query space image. Besides, al- gorithms based on corners/edges detection are highly sensitive to adverse il- lumination conditions in orbit. An evaluation of the critical aspects of these two methods is provided with the aim of comparing their performance under changing illumination conditions and varying views between the camera and the target. Five different keypoints-based methods are compared to assess the robustness of feature matching. Furthermore, a method based on corners ex- traction from the lines detected by the Hough Transform is proposed and evalu- ated. Finally, a novel method, based on an hourglass Convolutional Neural Net- work (CNN) architecture, is proposed to improve the robustness of the IP during partial occlusion of the target as well as during feature tracking. It is expected that the results of this work will help assessing the robustness of keypoints- based, corners/edges-based, and CNN-based algorithms within the IP prior to the relative pose estimation.Space Systems EgineeringSpace Engineerin