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Earth observation satellite (EOS) scheduling is critical for improving the performance of Earth observation system. Most of the existing studies on satellite observation scheduling problems focus on static tasks and real-time dynamic tasks also known as emergency tasks while few attempts have been made for common dynamic tasks yet. The common dynamic tasks are characterized by uncertain arrival times and delayable observations while the emergency tasks require immediate observations. The number of common dynamic tasks is rapidly increasing with the expansion of satellite observation applications, which poses great challenges to EOS observation scheduling. To address this issue, this paper investigates the satellite observation scheduling problem with common dynamic tasks. Firstly, a centralized onboard dynamic scheduling framework based on a periodic-triggered rolling horizontal optimization (RHO) strategy is proposed and a novel two-stage mathematical model is established for the periodic rescheduling problem. Secondly, we propose a low-complexity onboard periodic rescheduling algorithm (OPR), which consists of a greedy-based task allocation algorithm, a pointer network (Ptr-network) based task scheduling algorithm and an iterative local search algorithm. In the greedy-based task allocation algorithm, we define a Task-EOS Fitness Indicator (TEFI) and each task is greedily allocated to the EOS with maximum TEFI. In the Ptr-network based task scheduling algorithm, the allocated task sets of all EOSs are fed into the Ptr-network in parallel to generate the observation scheduling result. Afterward, an iterative local search algorithm is proposed to further improve the quality of the observation scheduling result. Finally, extensive experiments are conducted to demonstrate the superiority of the OPR algorithm for the satellite observation scheduling problem with common dynamic tasks. [ABSTRACT FROM AUTHOR]

• Dynamics of multi-layer membrane structure with elastic spacers is studied. • An optimization criterion to eliminate the crossing modes is proposed. • The optimal spacer positions are calculated by Particle Swarm Optimizer algorithm. In recent years, there has been a growing interest in the dynamics of space membrane structures. However, existing research primarily focuses on single-layer membrane structures, leaving a significant research gap in the study of multi-layer structures with elastic spacers. To address this gap, this paper studies the dynamics and interlayer spacing accuracy of multi-layer membrane structures with elastic spacers. Specifically, the research focuses on the placement optimization of elastic spacers. A novel optimization criterion for the elastic spacers has been proposed, and the Particle Swarm Optimizer (PSO) algorithm has been utilized to calculate the optimal spacers positions. Results show that optimally placed elastic spacers can eliminate the crossing modes and improve the interlayer spacing accuracy during vibration. [ABSTRACT FROM AUTHOR]

• Space debris detection; • Payload camera breadboard for space surveillance; • Star tracking and space debris detection algorithms; • Hybrid time-index image algorithm. Space debris threatens space activities, demanding continuous observation and tracking by the Space Surveillance Network (SSN) to secure the Earth's orbits. However, SST efforts are limited by the size and brightness of the debris, detecting only a small amount of the total. Seeking to overcome such limitations, this study proposes an alternative payload camera capable of extracting the attitude of a satellite in orbit and detecting under-catalogued debris. This work is a sequential study of previous research, where the camera breadboard was designed and implemented. The contribution of this paper is the evaluation of star tracking and space debris algorithms to be implemented in the payload camera, the development and implementation of a new hybrid algorithm and the elaboration of performance metrics for comparison between the algorithms. The observation data of the previous research was used as input for the algorithms' tests. For star identification and, consequently, attitude extraction, the chosen algorithm was Tetra. The results were compared to the standard star identification software, Astrometry.net , to assess the attitude accuracy. For debris detection, ASTRiDE and the Hybrid time-index image algorithms were used. A comparison of the results was made to establish a performance evaluation metric in terms of detectability, time, and computational cost. [ABSTRACT FROM AUTHOR]

• A computing method for constellation-to-ground coverage is investigated. • The maximum and minimum coverage regions are calculated. • Simulation results demonstrate the validity and effectiveness of the proposed algorithm. The issue of constellation-to-ground coverage is a research focus in Earth observation applications. Traditional calculations often rely on grid point methods or their derivatives, but these can be limited in their application, relatively costly, inefficient, and often do not account for potential errors. This paper proposes a novel, efficient method based on latitude strip division for calculating the ground area coverage of satellite constellations, capable of providing the upper and lower bounds of coverage ratio for any ground area. Initially, the ground target area is divided into several latitude strips, and the target area range is utilized to determine the longitude range of each latitude strip. Subsequently, the upper and lower bounds of coverage of each strip are calculated according to the satellite ground coverage range. On this basis, the coverage boundary function is defined and the coverage ratio is derived through comprehensive statistics analysis. Finally, depending on the accuracy of the latitude strip division, the precise coverage area and coverage ratio with upper and lower bounds are determined for instantaneous, continuous, and cumulative coverage problems. Numerical simulation experiments were carried out and compared with the traditional grid point method to validate the effectiveness and computational efficiency of this algorithm in addressing the coverage issue for arbitrarily shaped ground areas. When compared to the longitude strip method, it was confirmed that for ground areas where the longitude exceeds the latitude range, this approach offers superior computational efficiency than the latitude strip method. [ABSTRACT FROM AUTHOR]

Due to the fast progress of Deep Learning (DL) methods in remote sensing applications, many change detection (CD) algorithms have been recently proposed based on CNN networks and the mechanism of self-attention. These algorithms extract features without focusing on the temporal dependency between features. This shortcoming led to the introduction of the Transformer mechanism. In this paper, we design a convolutional transformer network with a Siamese U-shaped structure and name it CTS-Unet to solve the CD problem. We exploit the ability of the CNN to extract effective semantic features and that of the transformer to extract global information effectively. The Siamese architecture allows using CNN to simultaneously extract effective semantic features from low-resolution bi-temporal images. The transformer part contains an encoder and a decoder, all of which use the Swin transformer module as their basic unit. The encoder processes the features extracted from the CNN using patch merging and the Swin transformer module to produce semantic features. The encoder extracts the detailed information from the features using patch expansion, the Swin transformer module, and convolutional upsampling to create a CD map. The experiments were performed on the widely used LEVIR-CD and DSIFN-CD datasets. Compared with other state-of-the-art CD methods, CTS-Unet provides higher performance with F1-scores of 91.87% and 69.60% for LEVIR-CD and DSIFN-CD datasets, respectively. [ABSTRACT FROM AUTHOR]

Accurate prediction of re-entry time and location of an uncontrolled re-entering space object is crucial but challenging. This paper presents in-house re-entry prediction algorithms: RSMGA, STKOptim, STKLTOptim, and ABPro. Case studies on re-entry prediction of Starlink-26 (COSPAR ID 2019-029F or NORAD ID 44240) and CZ-5B (COSPAR ID 2021-035B or NORAD ID 48275) during the IADC re-entry test campaigns 2021/1 and 2021/2, respectively are presented. Simultaneous application of these re-entry prediction algorithms facilitates decision-making on automation of switching between these algorithms during various phases (short term or long-term) of the re-entry campaign. The effectiveness and applicability of these algorithms are studied based on percentage error. The study focuses on the overall and time-based performance of the four methods. Six more re-entered objects are selected (three Geosynchronous transfer orbit (GTO) objects and 3 Low-Earth orbit (LEO) objects), and their re-entry time prediction has been carried out using the four methods for different time instances before re-entry. Prediction estimates are compared among the methods for all the time instances in terms of percentage error. Also, the study helps in fixing the suitability of the re-entry time prediction algorithm for objects from GTO and LEO. [ABSTRACT FROM AUTHOR]

During the spacecraft attitude maneuvers for the observation of mission targets, multiple instruments may face different pointing constraints, and they are rarely considered by the current attitude maneuver planning methods. In this case, this paper proposes an improved rapidly–exploring random trees star (RRT*) algorithm to obtain discrete quaternion path nodes while satisfying the pointing constraints of multiple sensors. Next, the temporal properties of these path nodes are defined by a trapezoidal path planning method, and bounded constraints on the angular velocity and control moment during the attitude maneuver are introduced. Then, the continuous quaternion curves through the discrete quaternion path nodes are calculated by a segmented cubic spline interpolation function. Finally, the angular velocity and control moment are calculated through the inverse dynamics. The simulation results verify the feasibility of the method. It can satisfy complex constraints and obtain the attitude optimization path, and alleviate the large control moment variations in the start and stop phases of the rest–to–rest maneuver. [ABSTRACT FROM AUTHOR]

• A multi-constraint model for impulsive orbital pursuit-evasion game (OPEG) is constructed. • A learning-based algorithm (i.e. PRD-MADDPG) for impulsive OPEG is designed. • The Predict-Reward-Detect (PRD) training framework for improving MADDPG is established. • The PRD-MADDPG performs better than conventional MADDPG in impulsive OPEGs. • The winning mechanism of the game for both players under multiple factors is analyzed. This paper comprehensively investigates the problem of impulsive orbital pursuit-evasion games (OPEGs) by using an artificial intelligence-based approach. First, the mathematical model for the impulsive OPEGs in which the pursuer and evader both perform their orbital maneuvers by imposing the impulsive velocity increments is constructed. Second, the problem of impulsive OPEGs is transformed into a bilateral optimization problem with a minimum–maximum optimization index in terms of terminal time and multiple constraints such as maneuverability, total fuel consumption, and mission time, etc. To determine the optimal impulsive maneuvers for both sides, a PRD-MADDPG (Predict-Reward-Detect Multi-Agent Deep Deterministic Policy Gradient) algorithm in the frame of multi-agent reinforcement learning is designed. This novel algorithm uses the basic MADDPG to achieve the strategies training and learning, and applies the supplemental PRD to predict the change of game state during the interval between two adjacent impulsive maneuvers and incorporate these information into the algorithm training in the form of predicted reward. Finally, some pursuit-evasion missions near the Geosynchronous Earth Orbit are numerically analyzed to verify the validness and effectiveness of the algorithm. The results prove that the PRD-MADDPG algorithm is very efficient to find applicable strategies even considering rather complex constraints. It is also shown that the learning-based strategies can be effectively applied in the extended scenarios which are not seen in the training process. [ABSTRACT FROM AUTHOR]

In recent years, the research of indoor positioning technology has mainly focused on WI-FI, Bluetooth, ultra-wideband, and pseudo-satellites. Each of these indoor positioning technologies has different advantages, but also has various limitations, especially in indoor areas such as corridors, tunnels, and stairs. Due to the limitations of application scenarios, navigation and positioning accuracy and efficiency, it is difficult to form a satisfactory navigation and positioning solution for indoor special environment. In order to solve the positioning problem of indoor long and narrow areas, a multi-source fusion navigation and positioning algorithm based on pseudo-satellite assistance is proposed in this paper. Under certain map information constraints, it can use the direct arrival signal of a single pseudo-satellite to correct the location information output by mobile phone PDR(Pedestrian Dead Reckoning), and improve the navigation and positioning performance of indoor passageway and corridor areas. To realize the system, it is necessary to ensure that a direct pseudo-satellite signal can be received in any area of the corridor. The position of the pseudo-satellite can be determined according to the captured pseudo-satellite signal, and the pseudo-range change rate can be estimated by calculating the carrier phase change rate. In this paper, we deduce the relationship between the pseudo-range change rate and the change of the mobile phone PDR position, and build an optimization model based on map information constraints, which improves the positioning accuracy of the system for indoor narrow and long areas. A large number of experimental results show that the system has high positioning accuracy in the real indoor long and narrow environment. The average positioning accuracy of the algorithm in indoor environments is better than 0.4 m, and there is no significant change in the performance after a long time of cumulative positioning. [ABSTRACT FROM AUTHOR]

• The reduction of CIT approach to FBP algorithm. • The rapid 2-D CIT algorithm of electron density profile. • The possibility of detection small ionospheric irregularities. The usage of a satellite cluster of satellite-to-satellite sounding of the ionosphere makes it possible to obtain the global height distribution of an electron density between 200 and 500 km height range in the orbital plane of the cluster. This paper introduces a novel rapid two-dimensional ionospheric radio tomography algorithm that uses total electron content (TEC) data measured between low Earth orbit (LEO) satellites. This algorithm reduces the problem of the ionospheric radio tomography to the rapid filtered back-projection (FBP) algorithm, which was not previously used in the computerized tomography of the ionosphere, and which allows to obtain a high resolution nowcast. Data sets of TEC measurements were simulated via IRI-2016 model and used as an input for the developed algorithm. The reconstructed profiles of the electron density were compared with those profiles from IRI-2016 model to calculate the difference between them. The obtained accuracy is comparable to the accuracy of algebraic reconstruction algorithms (ART), which are classical in the ionospheric radio tomography, and makes it possible to use the developed method to detect local disturbances in the ionosphere that is demonstrated on a test case in this paper. [ABSTRACT FROM AUTHOR]

The ionospheric effect on radio wave propagation error has a significant impact on navigation, positioning, and real-time measurements. It is the primary source of errors in high precision aviation and measurement systems. A set of eight coefficients is used to correct ionospheric errors for Indian Regional Navigation Satellite System (IRNSS) users. Currently, the Klobuchar model is an effective low computational mathematical model for ionospheric range error mitigation through a set of eight coefficients. It has low accuracy in Equatorial Ionization Anomaly (EIA) regions and can correct up to ∼60% of the ionospheric range error. This paper proposes an ionospheric error correction model that can be applied to such measurements as well as an associated error model to compute accuracy with a differential interval of observations 5 m, 30 m and 60 m. The numerical simulation results show that compared to widely used algorithms, the proposed strategy can enhance the 83.13% ionospheric correction with Klobuchar- like coefficients at a sampling rate of 5 m. The proposed solution could be effective for Regional Navigational Satellite Systems (RNSS) for ionospheric delay corrections in low-latitude regions. [ABSTRACT FROM AUTHOR]

This paper discusses the estimation of zonal geomagnetic indices of two super geomagnetic activities of 2015 in the 24th solar cycle. It estimates the zonal geomagnetic indices (Dst, ap, AE) of 17 March and 22 June 2015 super storms with an artificial neural network model. The activities that happened in March and June are considered on the solar wind parameters (B z , E, P, N, v, T) and zonal geomagnetic indices obtained from NASA coordinated data analysis web. Descriptive values of the variables are indicated, binary correlations of the data are shown with the covariance matrix and the hierarchical cluster of the data are presented by the dendrogram. In the paper, the physical principles govern the artificial neural network model. The model utilizes solar wind parameters as inputs and zonal geomagnetic indices as outputs. The causality principle shapes the models by cause-effect relationship. Back propagation algorithm is specified as Levenberg–Marquardt (trainlm) and 30 neural numbers are used in the artificial neural network. The neural network model estimates the Dst, ap, and AE indices of 17 March and 22 June activities with reliable accuracy. The geomagnetic activity estimation can support interplanetary studies. [ABSTRACT FROM AUTHOR]

In this paper, the far-distance nonplanar rapid cooperative rendezvous with multiple specific-direction thrusts is investigated. In the optimization process, the indirect method is adopted. The homotopy method is used to smooth the dynamical equations. The solutions of the initial co-state variables of the energy-optimal problem are obtained by the quantum-behaved particle swarm optimization (QPSO) algorithm. Sequential quadratic programming (SQP) algorithm is coupled into the homotopy method to overcome the difficulty in nonsmooth integration when the thrust switches on & off frequently. The solutions of the fuel-optimal problem are obtained by the homotopy-SQP coupled method. The simulation results show the feasibility of the cooperative rendezvous with multiple specific-direction thrusts. The "suitable point" of normal thrust is investigated. [ABSTRACT FROM AUTHOR]

In this paper, an integrated optimal flight plan with a guidance plan in a general transfer orbit is considered. Due to the need to simultaneously reduce energy consumption and flight time in some space missions, the spacecraft trajectory is optimized based on two criteria to minimize fuel consumption and flight time in the orbital transfer process. For this purpose, the total velocity impulse on both sides of the transition orbit and the elapsed time are defined as the criteria of the objective function which includes the spacecraft's insertion conditions and orbital parameters related to the shape of the trajectory. The optimal transition (coasting) trajectory is then obtained using the multi-objective genetic algorithm (MOGA) search method. Subsequently, the velocity-to-be-gained guidance steering scheme is integrated to track the trajectory onboard and reach the final orbit. The numerical results of the proposed algorithm are extracted in three case studies and compared with other references that show its ability to find the optimal transfer. [ABSTRACT FROM AUTHOR]

The effectiveness of the forest height inversion methods using polarimetric SAR interferometry data is affected by the estimation accuracy of ground phase, volume only coherence and model prediction for forest canopy layer. Finding the forest model that considers the effects of the signal penetration in the forest medium as well as optimum volume coherence, which is an important purpose of the forest inversion method. This paper suggests a novel inversion algorithm to increase the accuracy of forest height estimation as well as ground phase estimation based on mean of coherence set. For this purpose, an adaptive total least square line fit is proposed to estimate the ground phase and coherence line. Then, the combination between the VE-RVoG model and an optimization volume only coherence algorithm based on mean coherence set and polarization signature is developed to find more accurate forest parameter values. The proposed algorithm applies forest height estimation using L-band PolInSAR data acquired by PolSARProSim software, spaceborne SIR-C and UAVSAR system. The method was further validated by using UAV L-band PolInSARata and the reference data of LiDAR canopy height model over rainforest Lope National Park, Gabon. Results showed that RMSE of results approximate 2.91 m and R2 is 0.909. The obtained results demonstrate the potential of proposed method in forest parameters estimation. [ABSTRACT FROM AUTHOR]

Abstract In the last 20 years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2 m. [ABSTRACT FROM AUTHOR]

Abstract Capturing an uncontrolled space target is a tremendously challenging research topic. Target capture by a space robot can be well planned according to predicted motion of the target. In this paper, motion prediction of an uncontrolled space target is studied and a motion prediction algorithm is proposed. In the proposed algorithm, firstly a method for identifying the parameters of motion state and inertial property of the target is established; and then through substituting the identified parameters into the dynamic equations of the target, the motion of the target can be predicted as the solution of the equations. In the identification of the parameters, the unscented Kalman filter (UKF) is applied. In order to support the UKF, a method for estimating noise level of the observation data is developed, so our motion prediction algorithm is noise adaptive. A practical convergent criterion is also designed to determine the time when the estimated result of the UKF is accurate enough, such that the predicted motion is credible enough. After that, the accuracy of the prediction is further improved by an optimization method. In the end of this paper, numerical simulations are done to verify the validity of the proposed motion prediction algorithm. Simulation results indicate that the proposed algorithm is able to predict the motion of the target precisely. [ABSTRACT FROM AUTHOR]

This paper focuses on the track-to-object association problem based on the two-line elements (TLE) set. The TLE's short-term propagation error characteristics are analyzed to capture its uncertainty. Further, a four-step track-to-object association algorithm is designed for the optical observation data. First, for too-short arc tracklets, a circular orbit determination algorithm is proposed to calculate the inclination and the right ascension of the ascending node. Second, the TLEs are filtered based on these results. Nearly 96% of the TLEs can be filtered, which significantly improves the association efficiency. The last two steps consist of two association processes. A first-order association process is implemented first to get candidate objects, with the angles root mean square error as the metric. Then a precise association process checks the candidate objects and gives the final association result. The proposed approach is tested with simulated and observed data, respectively. With simulated data, the true positive rate is 98.7%. With the observed data, the association results were validated using the precise orbit ephemerides. [ABSTRACT FROM AUTHOR]

With the rapid growth of the number of Earth observation satellite (EOS) supporting critical applications, it is required to improve the security techniques to protect the sensitive data and images during the transmission between the satellites and the ground stations. This paper introduces a new satellite image encryption algorithm based on the Linear Feedback Shift Register (LFSR) generator, SHA 512 hash function, hyperchaotic systems, and Josephus problem. LFSR generates a matrix that is used to construct the 512-bits value of the hash function. These bits are used to set the initial values and parameters of the proposed encryption algorithm. Firstly, the six dimensions (6-D) hyperchaotic system is divided into three parts, where every two equations are considered as one part. Secondly, the 1-D hyperchaotic logistic-tent system is considered as the controller to select one part. The selected part is used to generate a matrix that is XORed with the original image. Thirdly, the scrambling operation by Josephus sequences is applied to the output of the previous step by scrambling the rows and the columns according to the selected part to produce the pre-encrypted image. Finally, if the number of iterations is less than the required number which is considered as a parameter of the secret key, the previous operations will be repeated in the pre-encrypted image; otherwise, the pre-encrypted image is considered as the final cipher image. Experimental and analyses results show that the proposed algorithm has good performance in terms of high level of security, large enough key-space, tolerance to Single Event Upsets (SEU) as well as low time complexity. [ABSTRACT FROM AUTHOR]

To meet the urgent needs of emergency observation missions, a well-designed constellation is key to ensuring system performance. This paper presents a multi-objective optimization framework which is well suited for constellation designers to identify key trade-offs and make decisions. Firstly, this work investigates the metrics of responsiveness and coverage simultaneously to approximate to the actual conditions of emergencies, and a multi-objective mathematical model is constructed to explore solutions to enhance the overall system performance. Then, according to the characteristics of emergency missions, a hierarchical chromosome encoding method is proposed in the framework to encode a solution that explores different sized constellations without constraining its specific geometry. Finally, we propose the use of radial axes plots to help constellation designers to gain pragmatic insights for informed decision making. The simulation of disaster management verifies that the proposed chromosome encoding method effectively promotes the convergence of the algorithm, and the multi-objective optimization framework helps to generate a rapid and adequate observation satellite constellation to provide maximum coverage performance while ensuring timeliness. [ABSTRACT FROM AUTHOR]

Owing to the increasing numbers of navigation satellites, available frequencies, and tracking stations, the computational efficiency of large GNSS networks has become a critical issue, especially when undifferenced (UD) pseudorange and carrier phase observations are used. By converting UD carrier phases into carrier ranges before implementing the integrated processing of a whole network, the UD calculation time can be greatly decreased. However, because a common multicore hardware platform is not fully utilized to optimize the algorithm, the processing of a whole network is still time consuming. In this paper, this problem is overcome by applying the multicore parallel computing technology, namely the Task Parallel Library (TPL). To improve the processing efficiency of large GNSS networks using UD carrier ranges and based on the task parallelism and data parallelism offered through TPL, the wide-lane (WL) and narrow-lane (NL) fractional cycle biases (FCBs), and carrier ranges are computed in parallel, and the integrated processing of a whole network is performed by establishing the normal equation in parallel and adopting parallel computing for Cholesky decomposition. The proposed strategy is validated using GPS, BDS and Galileo observation data recorded from January 6, 2019, to January 26, 2019 by the IGS network with a global distribution of approximately 500 stations. The average positioning accuracies in the east, north and vertical components are 2.54, 2.34 and 4.41 mm, respectively, and the average SDBS ambiguity fixing rate is 93.18%. The average time consumptions for step 1, 2 and 3 are reduced from 151.45, 248.80 and 44.35 min to 32.20, 52.71 and 14.25 min, respectively, with acceleration ratios of 4.70, 4.72 and 3.11 times, respectively, on a six-core platform. [ABSTRACT FROM AUTHOR]

This paper derives a control solution for mitigation of Parametrically excited vibration of taut cable in cable-truss couple system, which ensures safe and stable regulation for cable towards the desired patterns. The dynamic characteristics with periodic perturbation and the parametrically excited instability of cable are both analyzed; the physical factors of damping influence on the cable are investigated and demonstrated. Based on the linear quadratic regulator principle (LQR), the optimal control algorithms are presented, which play an effective role in suppressing the the vibration distribution and improving significantly the transverse stability of the inclined cable system. The simulation results demonstrate the validity and effectiveness of the presented control method. [ABSTRACT FROM AUTHOR]

The objective of the present paper is to show a method to find orbits near artificial equilibrium points for a satellite equipped with a continuous thrust that allows it to stay near the poles of a celestial body. The physical system includes the presence of a moon of the celestial body under observation, and the perturbation caused by this moon is counteracted by an algorithm to help the satellite to stay close to its original position, instead of escape from it. The equations of motion are changed under some approximations, and analytical solutions for these equations are obtained and analyzed. Initial conditions are used such that their secular terms are nullified. These solutions are restricted to a short period of time, but we propose a method in which there are periodic updates in the thrust. Thus, the solutions can be extended for the duration of the mission. A numerical simulation is obtained, whose results are required to be in agreement with the analytical solution using these periodic adjustments of the thrust. This agreement means that the motion of the spacecraft remains bounded close to its initial position for longer times. Several systems with different sizes and mass parameters are used to show the results of the research, like Sun-Earth-Moon, Sun-Ida-Dactyl, Sun-Saturn-Titan and Sun-Mars-Phobos systems. The results also indicate the locations of points that require minimum magnitude of the thrust. [ABSTRACT FROM AUTHOR]

Selection of the satellite equipment layout with performance constraints is a complex task which can be viewed as a constrained multi-objective optimization and a multiple criteria decision making problem. The layout design of a satellite cabin involves the process of locating the required equipment in a limited space, thereby satisfying various behavioral constraints of the interior and exterior environments. The layout optimization of satellite cabin in this paper includes the C.G. offset, the moments of inertia and the space debris impact risk of the system, of which the impact risk index is developed to quantify the risk to a satellite cabin of coming into contact with space debris. In this paper an optimization tool for the integration of CAD software as well as the optimization algorithms is presented, which is developed to automatically find solutions for a three-dimensional layout of equipment in satellite. The effectiveness of the tool is also demonstrated by applying to the layout optimization of a satellite platform. [ABSTRACT FROM AUTHOR]

Capturing a non-cooperative space target is a tremendously challenging research topic. Effective acquisition of motion information of the space target is the premise to realize target capture. In this paper, motion prediction of a free-floating non-cooperative target in space is studied and a motion prediction algorithm is proposed. In order to predict the motion of the free-floating non-cooperative target, dynamic parameters of the target must be firstly identified (estimated), such as inertia, angular momentum and kinetic energy and so on; then the predicted motion of the target can be acquired by substituting these identified parameters into the Euler’s equations of the target. Accurate prediction needs precise identification. This paper presents an effective method to identify these dynamic parameters of a free-floating non-cooperative target. This method is based on two steps, (1) the rough estimation of the parameters is computed using the motion observation data to the target, and (2) the best estimation of the parameters is found by an optimization method. In the optimization problem, the objective function is based on the difference between the observed and the predicted motion, and the interior-point method (IPM) is chosen as the optimization algorithm, which starts at the rough estimate obtained in the first step and finds a global minimum to the objective function with the guidance of objective function’s gradient. So the speed of IPM searching for the global minimum is fast, and an accurate identification can be obtained in time. The numerical results show that the proposed motion prediction algorithm is able to predict the motion of the target. [ABSTRACT FROM AUTHOR]

• A new pansharpening alternative is proposed for the fusion of satellite images. • A new approximation of the weighting coefficients is proposed. • The scheme can inject more spatial information and assure more color preservation. In remote sensing applications, pan-sharpening or image fusion focuses on improving the low spatial resolution (LSR) of multispectral (MS) images by injecting the details information selected from the panchromatic (PAN) images with high spatial resolution (HSR). This paper presents a new pansharpening alternative based on the combination of the well-known Intensity-Hue-Saturation (IHS) method and multiscale analysis by using wavelets. The multiscale wavelet decomposition has been achieved by using the "à trous" wavelet transform (ATWT). In this novel approach, a new approximation of the weighting coefficients of the multispectral bands based on the combination of energy and correlation coefficients of the MS and PAN images is proposed. Therefore, the intensity element is extracted from the up-sampled MS images and the weighting coefficients of the intensity component are computed by using the average energy ratio and the correlation coefficients between the wavelet-based decomposed PAN image and the different MS bands in order to achieve more detail information. The detail information is then injected to the MS bands to obtain a fused image with high spectral and spatial information. The experimental results confirmed that a superior trade-off between spectral and spatial quality can be achieved by using the proposed approach. Moreover, compared to numerous classical and state-of-the-art pansharpening methods, the proposed scheme can inject more spatial information and guarantee more color preservation. [ABSTRACT FROM AUTHOR]

The precise positioning of a lander is a key issue during in situ planetary exploration. This paper proposes an optimal weighting algorithm based on a Helmert-variance component estimation applied to VLBI phase delay data. We validated our algorithm on Chang'e 3 lander VLBI phase delay from the Chinese VLBI Network. We consider that the lander positioning accuracy achieved with our new algorithm is at least 53 % better, compared to previous estimations based on the same observations. The position of the lander based on continuous 4-day observations was estimated to be 44.1215 ° N (± 21.9 m), −19.5135 ° E (± 4.2 m) and −2632.7 m (± 18.9 m) with respect to a moon reference system. This result agrees well with the position derived from images taken by the Narrow Angle Camera on the Lunar Reconnaissance Orbiter. [ABSTRACT FROM AUTHOR]

This paper proposes a solution for multiple-impulse orbital maneuvers near circular orbits for special cases where orbital observations are not globally available and the spacecraft is being observed through a limited window from a ground or a space-based station. The current study is particularly useful for small private launching companies with limited access to global observations around the Earth and for orbital maneuvers around other planets for which the orbital observations are limited to the in situ equipment. An appropriate cost function is introduced for the sake of minimizing the total control/impulse effort as well as the orbital uncertainty. It is subsequently proved that for a circle-to-circle maneuver, the optimization problem is quasi-convex with respect to the design variables. For near circular trajectories the same cost function is minimized via a gradient based optimization algorithm in order to provide a sub-optimal solution that is efficient both with respect to energy effort and orbital uncertainty. As a relevant case study, a four-impulse orbital maneuver between circular orbits under Mars gravitation is simulated and analyzed to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems Reflectometry (GNSS-R) utilizes GNSS signals reflected off the Earth surface for remote sensing applications. Due to weak power of reflected signals, GNSS-R receiver needs to track reflected signals by open loop. The first step is to calculate the position of specular point. The specular point position error of the existing algorithm—Quasi-Spherical Earth (QSE) Approach—is about 3 km which may cause troubles in data post-processing. In this paper, gradient descent algorithm is applied to calculate position of specular point and the calculation is based on World Geodetic System 1984 (WGS 84) ellipsoid in geodetic coordinate. The benefit of this coordinate is that it is easy to investigate the effect of real surface's altitude. Learning rate—the key parameter of the algorithm—is adaptively adjusted according to initial error, latitude and gradient descent rate. With self-adaptive learning rate strategy, the algorithm converges fast. Through simulation and test on Global Navigation Satellite System Occultation Sounder II (GNOS II), the performances of the algorithm are validated. The specular point position error of the proposed algorithm is about 10 m. The speed of the proposed algorithm is competitive compared with the existing algorithm. The test on GNOS II shows that the proposed algorithm has good real-time performance. [ABSTRACT FROM AUTHOR]

In the field of visual navigation, crater is an ideal navigation landmark on planet surface, because of its universality and significance. During the descent phase of lander, crater needs to be extracted and matched. However, as the landing proceeds, images taken by the on-board camera will change, such as the variation of scale, translation, rotation, illumination which will increase the difficulty of the matching algorithm. This paper proposes a more accurate and faster crater matching algorithm based on feature descriptor. This algorithm is designed for navigation during the descending phase. It has the invariance of scaling, rotation, illumination. First, this algorithm extracts circular arc by Gaussian Pyramid and ELSD algorithm, so that this algorithm has the invariance against scale variation. Then, in order to describe the circular arc, this algorithm determine the direction of the circular arc by the direction histogram. And this algorithm constructs the circular arc band descriptors in the support region to achieve translation, rotation and illumination invariance. At last, matching criteria is the combination of the Nearest Neighbor Distance Ratio (NNDR) and the Euclidean distance constraint. The results are obtained under different image variations. The matching results show that this crater matching algorithm has high crater correct extracting and matching rate and high computationally efficient. [ABSTRACT FROM AUTHOR]

Attitude determination of the space vehicle always plays a pivotal role in space missions. Usually, the fourth-order Runge Kutta algorithm is taken as more common attitude integration algorithm. However, more appropriate algorithms are desired in order to meet variable requirement. This paper investigates the performance of the fourth-order Runge Kutta and Taylor series algorithms in coning environment, and presents a new algorithm which takes advantages from above both. Series of simulation experiments have been conducted to verify the effect of the new algorithm. Their results show that the new algorithm outperforms the fourth-order Runge Kutta and the four-sample rotation vector algorithms over long-duration run even in complex engineering applications where the attitude change is not too fast. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) offer promising opportunities to automatically glean scientifically relevant information directly from annotated images, without needing to handcraft features for detection. Crater counting started with hand counting hundreds, thousands, or even millions of craters in order to determine the age of geological units on planetary bodies of the solar system. Automated crater detection algorithms have attempted to speed up this process. Previous research has employed computer vision techniques with handcrafted features such as light and shadow patterns, circle finding, or edge detection. This research continues, but now some researchers use techniques like convolutional neural networks that enable the algorithm to develop its own features. As the field of machine learning undergoes exponential growth in terms of paper count and research methods, the crater counting application can benefit from the new research, especially when conducting joint interdisciplinary projects. Despite these advancements, the crater counting community has not yet adopted standard methods for automating the process despite decades of research. This survey enumerates challenges for both planetary geologists and machine learning researchers, looks at the recent automatic crater detection advancements using machine learning techniques (primarily in methods using CNNs), and makes recommendations for the path toward greater automation. [ABSTRACT FROM AUTHOR]

The present paper studies the powered Swing-By maneuver when performed in an elliptical system of primaries. It means that there is a spacecraft travelling in a system governed by the gravity fields of two bodies that are in elliptical orbits around their center of mass. The paper particularly analyzes the effects of the parameters relative to the Swing-By (Vinf-,rp,ψ), the orbit of the secondary body around the primary one (e,ν) and the elements that specify the impulse applied (δV,α) to the spacecraft. The impulse is applied when the spacecraft passes by the periapsis of its orbit around the body, where it performs the Swing-By, with different magnitudes and directions. The inclusion of the orbital eccentricity of the primaries in this problem makes it closer to reality, considering that there are many known systems with eccentricities different from zero. In particular, there are several moons in the Solar System which orbits are not circular, as well as some smaller bodies, like the dwarf planet Haumea and its moons, which have eccentricities of 0.25 or even larger. The behavior of the energy variation of the spacecraft is shown in details, as well as the cases where captures and collisions occur. The results show the conditions that optimize this maneuver, according to some given parameters, and how much can be obtained in terms of gains or losses of energy using the best conditions found by the algorithm developed here. [ABSTRACT FROM AUTHOR]

The separation of clouds from snow is fundamentally very challenging because of their similar spectral signature. A new algorithm was proposed to detect clouds from snow in Landsat 8 imagery. Taking the Hetian District region, where there is frequent cloud and snow cover, in northwestern China as one of the typical case areas. The typical case is presented in detail to illustrate the approach produces and results. A band math method for cloud and snow discrimination index (CSDI) was firstly conducted in this paper, fractal digital number-frequency (DN-N) algorithm and hotspot analyses were applied to determine the threshold of the CSDI and eliminate false anomalies. The results showed that an overall accuracy exceeding 95% in areas with very bright land surfaces, which indicate that this algorithm is effective for detecting clouds in specific situations where the ground objects have some reflectance characteristics similar to cloud. [ABSTRACT FROM AUTHOR]

Abstract A robust model and a Multi-objective Binary-encoding Differential Evolution (MBDE) algorithm are proposed in this paper for agile earth observation satellite proactive scheduling considering satellite resource failure and emergency task insertion. Firstly, considering that the reserved slack time in schedule can absorb uncertainties, two indicators are proposed: schedule profits and slack time, based on which, a robust model of proactive scheduling is established. Secondly, to solve the multi-objective model, solutions are represented via binary-encoding, mutation, crossover, and selection operators are re-designed, besides, an external archive update strategy is adopted to store elitist solutions in the evolution process, moreover, non-dominated Pareto set evaluation metrics are improved. Finally, computational results have demonstrated that, compared with NSGA-II and SPEA-2, the MBDE algorithm is able to obtain well-distributed solutions with good convergence more efficiently. The study can provide the method support for proactive scheduling of agile earth observation satellites. [ABSTRACT FROM AUTHOR]

Abstract Future Mars missions may require improved landed accuracy to facilitate the landing site selection and finally access a region of complex terrain with high scientific return. This paper is to develop a novel, robust, and precision entry guidance algorithm for Mars entry vehicles with low lift-to-drag ratios. In the presence of large uncertainties, the entry terminal point controller algorithm can encounter severe performance degradation due to: (1) the small perturbation assumption, and (2) theoretically ignoring deviations in the atmospheric-density model, aerodynamic-force model, etc. Based on numerical technologies and the classical variation method (VM), this work develops a semi-analytical (SA) algorithm, in which combined effects of several dynamic uncertainties now can be addressed. The terminal-downrange error is predicted by a numerical predictor such that the dependence on the reference trajectory can be reduced and then the issue caused by the small perturbation assumption can be addressed. Such a predicted terminal-downrange error is finally corrected by an analytical corrector, which is designed by the VM. It is indicative that there can be no numerical iterations in the SA algorithm. Numerical results demonstrate the effectiveness of the SA algorithm. [ABSTRACT FROM AUTHOR]

Abstract Launch opportunity search is crucial for preliminary design of interplanetary trajectories. However, it is difficult to obtain the optimum solution efficiently when the range of search is wide. In this paper, a new fast search algorithm based on a modified hierarchical approach is proposed. At the top level, a simplified Pork-Chop diagram is plotted by sampling interpolation to delimit the candidate regions of the optimal solution. Each candidate region contains only one optimal solution, which fundamentally avoids the local optimal problem of genetic algorithms. At the bottom level, a hybrid optimization approach combining the genetic algorithm and the conjugate directions method is used to solve the accurate optimal solution of each candidate region and obtain the global optimal launch opportunity in the whole wide range of search. Simulation results show that the proposed hierarchical approach can quickly and robustly find the global optimal launch opportunity in a wide search space. [ABSTRACT FROM AUTHOR]

Abstract The geometric dilution of precision (GDOP) is an important index with positioning, navigation and timing (PNT) applications. In multi-GNSS positioning, the system time offsets between different systems can be determined at user level, namely, calculated by the receiver at the cost of at least one additional satellite from each additional time reference frame. In this paper, we focus on some characteristics of the GDOP at user level (short for GDOP U) in multi-GNSS positioning, including the impact of the number of satellites on the GDOP U , and the calculation of the GDOP U minimum. The theoretical derivations show that the GDOP U decreases with respect to the number of satellites. Moreover, the detailed expression of GDOP U minimum together with the optimal condition leading to the minimum is also given. The experimental results using real data validate these discussions. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the entry trajectory optimization problem of lifting type re-entry vehicle with path constraints is solved using Pigeon Inspired Optimization (PIO). Entry trajectory optimization problem involves finding the control profiles, bank angle, and angle of attack to guide the vehicle safely to the destination. The proposed approach parametrizes the bank angle to be a linear function of energy while the angle of attack is considered to be a monotonic function of Mach number. Thus, the problem of finding control profiles is transformed into three parameter search problem. The PIO algorithm is used to find the values of these parameters that minimizes the objective function. The terminal heading angle offset is minimized using traditional bank reversal logic. A new approach is proposed in which the bank angle is modulated to eliminate the oscillations observed in the altitude profile of an entry vehicle with a high lift to drag ratio (L/D). A methodology to satisfy the given load factor constraint is also proposed, as an alternative to traditional penalty factor approach used for incorporating path constraints in PIO algorithm. The proposed approach is further validated by considering sub-cases with different load factor limits and bank angle as the only control variable. The angle of attack profile obtained from the previous case is considered as the nominal profile. The proposed trajectory optimization strategy using PIO algorithm is simulated for Common Aero Vehicle with high L/D ratio (CAV-H) with different load factor constraint limits. The results show that the obtained angle of attack profile minimizes the peak heat rate experienced by the vehicle and bank angle modulation eliminates the oscillations in the altitude profile as well as makes the entry trajectory satisfy the load factor constraint. [ABSTRACT FROM AUTHOR]

Highlights • Obtain the optimal solution of far rapid cooperative rendezvous in general case. • Design QPSO-SQP and proving the advance of QPSO-SQP. • Solve the complex optimal control problem with 29 variables. Abstract Focused on far-distance rapid cooperative rendezvous between two spacecraft under continuous large thrust, this paper presents a series of artificial intelligence algorithms for fuel and time optimization. The process of far-distance rapid cooperative rendezvous was optimized by a type of hybrid algorithm-integrated Quantum-behaved Particle Swarm Optimization (QPSO) and Sequential Quadratic Programming (SQP). The convergent co-state vectors were obtained by QPSO and subsequently set as the initial values of SQP to search for the exact solutions in a smaller area. Applications of non-coplanar cooperative rendezvous are provided to demonstrate that the QPSO-SQP algorithm has better performance than other popular algorithms in less time consumption, faster convergence rate and highly stable solutions. [ABSTRACT FROM AUTHOR]

Attitude estimation is a critical component of the Attitude Determination and Control System (ADCS) of any satellite. It is used to convert the sensor observation data to an estimated attitude using filtering algorithms. However, in the presence of sensor faults, the ADCS fails to achieve the desired attitude accuracy. In this paper, the Fault Tolerant Extended Kalman Filter (FTEKF) is proposed to handle this imperfection. In accordance, various filtering steps are included in the FTEKF design to enhance both attitude estimation and sensor fault detection. The developed algorithm can detect and isolate any unexpected sensor faults in real time, which provides a reliable attitude estimation. A comparative study with the classical and robust Kalman filters is performed through numerical simulations in order to validate the effectiveness of the adopted filter in case of magnetometer fault data. [ABSTRACT FROM AUTHOR]

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM 10 and PM 2.5 exceeded 400 μg/m 3 and 150 μg/m 3 , respectively, the ratio PM 2.5 /PM 10 being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km −1  × sr −1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China. [ABSTRACT FROM AUTHOR]

This paper presents an improved algorithm for automatic detection of Martian dust devils that successfully extracts tiny bright dust devils and obscured large dust devils from two subtracted landscape images. These dust devils are frequently observed using visible cameras onboard landers or rovers. Nevertheless, previous research on automated detection of dust devils has not focused on these common types of dust devils, but on dust devils that appear on images to be irregularly bright and large. In this study, we detect these common dust devils automatically using two kinds of parameter sets for thresholding when binarizing subtracted images. We automatically extract dust devils from 266 images taken by the Spirit rover to evaluate our algorithm. Taking dust devils detected by visual inspection to be ground truth, the precision, recall and F-measure values are 0.77, 0.86, and 0.81, respectively. [ABSTRACT FROM AUTHOR]

The HF propagation model, North Ionospheric Model and Ray Tracing (NIM-RT) was developed and tested for a number of years by comparing measured vertical and oblique ionograms over a number of radio links (especially in high latitude area) with the simulated ionograms. The present paper extends the model in order to include: (a) Implementation of the data retrieved from the International Reference Ionosphere (IRI-2012) model into the software for radio channel modeling. (b) The algorithm for IRI data optimization to the real time condition. (c) Results of comparison between simulated and measured ionograms. Based on these updates, a new software tool called North Ionospheric Model with IRI and Ray Tracing (NIMIRI-RT) was developed, and a number of vertical ionograms corresponding to multiple ionospheric reflections was simulated. The vertical ionograms observed at various ionosondes were compared with the synthesized ionograms, generated by applying NIM-RT in conjunction with initial and optimized IRI data. The ionogram structure simulated by NIMIRI-RT based on the data retrieved from optimized IRI is more reminiscent to the observations than ionograms synthesized with the initial NIMIRI-RT without parameters optimization. [ABSTRACT FROM AUTHOR]

Limited by the low precision of small satellite sensors, the estimation theories with high performance remains the most popular research topic for the attitude estimation. The Kalman filter (KF) and its extensions have been widely applied in the satellite attitude estimation and achieved plenty of achievements. However, most of the existing methods just take use of the current time-step’s priori measurement residuals to complete the measurement update and state estimation, which always ignores the extraction and utilization of the previous time-step’s posteriori measurement residuals. In addition, the uncertainty model errors always exist in the attitude dynamic system, which also put forward the higher performance requirements for the classical KF in attitude estimation problem. Therefore, the novel robust double gain unscented Kalman filter (RDG-UKF) is presented in this paper to satisfy the above requirements for the small satellite attitude estimation with the low precision sensors. It is assumed that the system state estimation errors can be exhibited in the measurement residual; therefore, the new method is to derive the second Kalman gain K k 2 for making full use of the previous time-step’s measurement residual to improve the utilization efficiency of the measurement data. Moreover, the sequence orthogonal principle and unscented transform (UT) strategy are introduced to robust and enhance the performance of the novel Kalman Filter in order to reduce the influence of existing uncertainty model errors. Numerical simulations show that the proposed RDG-UKF is more effective and robustness in dealing with the model errors and low precision sensors for the attitude estimation of small satellite by comparing with the classical unscented Kalman Filter (UKF). [ABSTRACT FROM AUTHOR]

Abstract: In a large majority of lunar and planetary surface images, impact craters are the most abundant geological features. Therefore, it is not surprising that crater detection algorithms (CDAs) are one of the most studied subjects of image processing and analysis in lunar and planetary science. In this work we are proposing an Integrated CDA, consisting of: (1) utilization of DEM (digital elevation map)-based CDA; (2) utilization of an optical-based CDA; (3) re-projection of used datasets and crater coordinates from normal to rotated view and back; (4) correction of the brightness and contrast of a used optical image; and (5) tile generation for the optical-based CDA and an assembling of results with an elimination of multiple detections, in combination with a pyramid approach down to the resolution of the available DEM image; and (6) a final integration of the results of DEM-based and optical-based CDAs, including a removal of duplicates. The proposed CDA is applied to one specific asteroid-like body, the small Martian moon Phobos. The experimental evaluation of the proposed CDA is done by a manual verification of crater-candidates and a search for uncatalogued craters. The evaluation has shown that the proposed CDA was used successfully for cataloging Phobos craters. The major result of this paper is the PH9224GT – currently the most complete global catalogue of the 9224 Phobos craters. The possible applications of the new catalogue are: (1) age estimations for any selected location; and (2) comparison/evaluation of the different chronology and production functions for Phobos. This confirms the practical applicability of the new Integrated CDA – an additional result of this paper, which can be used in order to considerably extend the current crater catalogues. [Copyright &y& Elsevier]

Abstract: In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32MHz, which is better than 0.34MHz from BPNN, 0.35MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78MHz, which is better than 0.81MHz from BPNN, 0.81MHz from SVR and 1.37MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper. [Copyright &y& Elsevier]

Abstract: This paper proposes a comprehensive approach to associate origins of space objects newly discovered during optical surveys in the geostationary region with spacecraft breakup events. A recent study has shown that twelve breakup events would be occurred in the geostationary region. The proposed approach utilizes orbital debris modeling techniques to effectively conduct prediction, detection, and classification of breakup fragments. Two techniques are applied to get probable results for origin identifications. First, we select an observation point where a high detection rate for one breakup event among others can be expected. Second, we associate detected tracklets, which denotes the signals associated with a physical object, with the prediction results according to their angular velocities. The second technique investigates which breakup event a tracklet would belong to, and its probability by using the k-nearest neighbor (k-NN) algorithm. In this paper we conduct optical observations of breakup fragments to verify the proposed approach. We selected a well-known breakup event of the rocket body US Titan 3C Transtage (1968-081E) as the primary observation target, and then we conduct optical observations by campaign between two sensors in Taiwan and one sensor in Japan. While three nights observations, we detect 96 tracklets that are uncorrelated with the Space Surveillance Network catalogue at the observation epochs, and finally 50 tracklets among them are associated with the breakup event of 1968-081E. [Copyright &y& Elsevier]

Abstract: More and more attention is paid to Geosynchronous Orbit (GEO) Synthetic Aperture Radar (SAR) in recent years due to its fine temporal resolution and large coverage, so that GEO SAR will play an important role in monitoring natural disasters, but its imaging is more difficult compared to lower Earth orbit (LEO) SAR because of the increase of orbit height. This paper mainly studies the coverage property and focusing method in GEO SAR. As is known to us, the coverage of a GEO SAR satellite can reach 1/3 of the whole Earth, and the revisiting time can reduce to 2h, which will remarkably improve the capability in ocean applications, earth dynamics and natural hazards management and so on. Compared with the imaging in LEO SAR, a problem in GEO SAR is that the linear trajectory model can bring a big error due to the long synthetic aperture time, so that the classical imaging algorithms in LEO SAR cannot be directly applied in GEO SAR. Using the Norm method and Taylor expansion method, this paper gains an accurate slant range model, which can resolve the big error of the linear trajectory at perigee and collapse of the linear trajectory at apogee. Furthermore, this paper deduces a novel imaging algorithm for GEO SAR by means of series reversion, which realizes the focusing on large scene under the synthetic aperture time of 100s. Finally, the simulation results at apogee and perigee prove the correction of the slant range model and imaging algorithm. [Copyright &y& Elsevier]

Surface exploration is an important way to improve the understanding of small bodies. Considering the irregular and weak gravity field near a small body, the movement of the surface explorer is generally achieved by hopping. In this paper, a guidance algorithm method based on convex optimization approach for pinpoint hopping movement on a small body is developed in order to improve the stability and accuracy of surface exploration. We formulate a fuel-optimal control problem for the single pinpoint hopping and convert it into a second-order cone programming (SOCP) problem which can be solved effectively by primal-dual-interior points method. A multi-hopping scenario is also proposed for the long-distance transfer. To certificate the performance of the proposed guidance algorithm, a full set of simulations are conducted and the effectiveness are analyzed. [ABSTRACT FROM AUTHOR]