PartLabeling:A label management framework in 3D space

Background In this work,we focus on the label layout problem:specifying the positions of overlaid virtual annotations in Virtual/Augmented Reality scenarios.Methods Designing a layout of labels that does not violate domain-specific design requirements,while at the same time satisfying aesthetic and functional principles of good design,can be a daunting task even for skilled visual designers.Presenting the annotations in 3D object space instead of projection space,allows for the preservation of spatial and depth cues.This results in stable layouts in dynamic environments,since the annotations are anchored in 3D space.Results In this paper we make two major contributions.First,we propose a technique for managing the layout and rendering of annotations in Virtual/Augmented Reality scenarios by manipulating the annotations directly in 3D space.For this,we make use of Artificial Potential Fields and use 3D geometric constraints to adapt them in 3D space.Second,we introduce PartLabeling:an open source platform in the form of a web application that acts as a much-needed generic framework allowing to easily add labeling algorithms and 3D models.This serves as a catalyst for researchers in this field to make their algorithms and implementations publicly available,as well as ensure research reproducibility.The PartLabeling framework relies on a dataset that we generate as a subset of the original PartNet dataset consisting of models suitable for the label management task.The dataset consists of 10003D models with part annotations.

Review on strategies of space-based optical space situational awareness

Space-based optical(SBO)space surveillance has attracted widespread interest in the last two decades due to its considerable value in space situation awareness(SSA).SBO observation strategy,which is related to the performance of space surveillance,is the top-level design in SSA missions reviewed.The recognized real programs about SBO SAA proposed by the institutions in the U.S.,Canada,Europe,etc.,are summarized firstly,from which an insight of the development trend of SBO SAA can be obtained.According to the aim of the SBO SSA,the missions can be divided into general surveillance and space object tracking.Thus,there are two major categories for SBO SSA strategies.Existing general surveillance strategies for observing low earth orbit(LEO)objects and beyond-LEO objects are summarized and compared in terms of coverage rate,revisit time,visibility period,and image processing.Then,the SBO space object tracking strategies,which has experienced from tracking an object with a single satellite to tracking an object with multiple satellites cooperatively,are also summarized.Finally,this paper looks into the development trend in the future and points out several problems that challenges the SBO SSA.

Method for Collision Avoidance in Spacecraft Rendezvous Problems with Space Objects in a Phasing Orbit

As the number of space objects(SO)increases,collision avoidance problem in the rendezvous tasks or reconstellation of satellites with SO has been paid more attention,and the dangerous area of a possible collision should be derived.In this paper,a maneuvering method is proposed for avoiding collision with a space debris object in the phasing orbit of the initial optimal solution.Accordingly,based on the plane of eccentricity vector components,relevant dangerous area which is bounded by two parallel lines is formulated.The axises of eccentricity vector system pass through the end of eccentricity vector of phasing orbit in the optimal solution,and orientation of axis depends on the latitude argument where a collision will occur.The dangerous area is represented especially with the graphical dialogue,and it allows to find a compromise between the SO avoiding and the fuel consumption reduction.The proposed method to solve the collision avoidance problem provides simplicity to calculate rendezvous maneuvers,and possibility to avoid collisions from several collisions or from“slow”collisions in a phasing orbit,when the protected spacecraft and the object fly dangerously close to each other for a long period.

Compressive sensing for small moving space object detection in astronomical images

It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.

Scheduling Optimization of Space Object Observations for Radar

An optimizing method of observation scheduling based on time-division multiplexing is proposed in this paper,and its efficiency is verified by outdoor experiments. The initial observation scheduling is first obtained by using a semi-random search algorithm,and secondly the connection time pair（ CTP） between adjacent objects is optimized by using a genetic algorithm. After obtaining these two parameters,the final observation scheduling can be obtained. According to pre-designed tracks between each adjacent objects in observation order,the seamless observation of neighboring targets is derived by automatically steering the antenna beam,so the observation efficiency is improved.

Re-Entry of Space Objects from Low Eccentricity Orbits

This paper deals with the re-entry predictions of the space objects from the low eccentric orbit. Any re-entering object re-enters the Earth’s atmosphere with a high orbital velocity. Due to the aerodynamic heating the object tends to break into multiple fragments which later pose a great risk hazard to the population. Here a satellite is considered as the space object for which the re-entry prediction is made. This prediction is made with a package where the trajectory path, the time of re-entry and the survival rate of the fragments is done. The prediction is done using DRAMA 2.0—ESA’s Debris Risk Assessment and Mitigation Analysis Tool suite, MATLAB and Numerical Prediction of Orbital Events software. The predicted re-entry time of OSIRIS 3U was found to be on 7th March 2019, 7:25 (UTC), whereas the actual re-entry time was on 7th March 2019, 7:03 (UTC). The trajectory path found was 51.5699 deg. (Lat), −86.5738 deg. (Long.) with an altitude of 168.643 km. But the actual trajectory was 51.76 deg. (Lat), −89.01deg. (Long.) with an altitude of 143.5 km.

QPSO-based algorithm of CSO joint infrared super-resolution and trajectory estimation

The midcourse ballistic closely spaced objects（CSO） create blur pixel-cluster on the space-based infrared focal plane,making the super-resolution of CSO quite necessary.A novel algorithm of CSO joint super-resolution and trajectory estimation is presented.The algorithm combines the focal plane CSO dynamics and radiation models,proposes a novel least square objective function from the space and time information,where CSO radiant intensity is excluded and initial dynamics（position and velocity） are chosen as the model parameters.Subsequently,the quantum-behaved particle swarm optimization（QPSO） is adopted to optimize the objective function to estimate model parameters,and then CSO focal plane trajectories and radiant intensities are computed.Meanwhile,the estimated CSO focal plane trajectories from multiple space-based infrared focal planes are associated and filtered to estimate the CSO stereo ballistic trajectories.Finally,the performance（CSO estimation precision of the focal plane coordinates,radiant intensities,and stereo ballistic trajectories,together with the computation load） of the algorithm is tested,and the results show that the algorithm is effective and feasible.

A High Speed Detection Scheme for Point Targets in a Multitarget Environment

The purpose of this paper is to develop a high speed detection scheme for moving and / or stationary point targets in a multitarget environment as registered in an IR image sequence. An iterative approximate 3-D line searching algorithm based upon the geometric representation of lines (for non-maneuvering targets in space) in a 3-D space is derived. The convergency of the algorithm is proved. An analysis is performed of the theoretical detection performance of the algorithm. The statistical experiment results show high effectiveness and computational efficiency of the algorithm in the case of low SNR. The idea may be employed to satisfy the real-time processing requirement of an IR system.

Multi-view space object recognition and pose estimation based on kernel regression

The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.

An analysis of close approaches and probability of collisions between LEO resident space objects and mega constellations

With the undergoing and planned implementations of mega constellations of thousands of Low Earth Orbiting(LEO)satellites,space will become even more congested for satellite operations.The enduring effects on the long-term space environment have been investigated by various researchers using debris environment models.This paper is focused on the imminent short-term effects of LEO mega constellations on the space operation environment concerned by satellite owners and operators.The effects are measured in terms of the Close Approaches(CAs)and overall collision probability.Instead of using debris environment models,the CAs are determined from integrated orbit positions,and the collision probability is computed for each CA considering the sizes and position covariance of the involving objects.The obtained results thus present a clearer picture of the space operation safety environment when LEO mega constellations are deployed.Many mega constellations are simulated,including a Starlink-like constellation of 1584 satellites,four possible generic constellations at altitudes between 1110 km and 1325 km,and three constellations of 1584 satellites each at the altitudes of 650 km,800 km,and 950 km,respectively,where the Resident Space Object(RSO)spatial density is the highest.The increases in the number of CAs and overall collision probability caused by them are really alarming.The results suggest that highly frequent orbital maneuvers are required to avoid collisions between existing RSOs and constellation satellites,and between satellites from two constellations at a close altitude,as such the constellation operation burden would be very heavy.The study is not only useful for satellite operators but a powerful signal for various stakeholders to pay serious attention to the development of LEO mega constellations.

Comparative evaluation of three machine learning algorithms on improving orbit prediction accuracy

In this paper,the recently developed machine learning(ML)approach to improve orbit prediction accuracy is systematically investigated using three ML algorithms,including support vector machine(SVM),artificial neural network(ANN),and Gaussian processes(GPs).In a simulation environment consisting of orbit propagation,measurement,estimation,and prediction processes,totally 12 resident space objects(RSOs)in solar-synchronous orbit(SSO),low Earth orbit(LEO),and medium Earth orbit(MEO)are simulated to compare the performance of three ML algorithms.The results in this paper show that ANN usually has the best approximation capability but is easiest to overfit data;SVM is the least likely to overfit but the performance usually cannot surpass ANN and GPs.Additionally,the ML approach with all the three algorithms is observed to be robust with respect to the measurement noise.