Orbit Research Articles

An improved prediction method for BDS-3 SISA parameters and the preliminary performance evaluation

Abstract The ephemeris accuracy parameters broadcast in the ephemeris messages are commonly utilized as an alarm threshold for the signal-in-space ranging errors (SISRE) of the Global Navigation Satellite System (GNSS). Accurately predicting the orbit and clock errors of broadcast ephemeris, and converting the prediction errors into broadcast ephemeris accuracy parameters using a robust model, is important for the integrity service of GNSS. The BeiDou Navigation Satellite System (BDS-3) broadcasts four ephemeris accuracy parameters, namely SIS A oe , SIS A ocb , SIS A oc1 , and SIS A oc2 , to represent the standard deviations of the satellite orbit and clock prediction errors in the broadcast ephemeris. The probability that the broadcast ephemeris SISRE exceeds 4.42 times the signal-in-space accuracy (SISA) without triggering an alarm in time is expected to be less than 1 × 10−5. In addition to accurately enveloping the broadcast ephemeris SISRE, SISA should also reduce the over-envelopment margin over SISRE to accurately express the system service accuracy. Thus, an accurate prediction of orbit and clock errors in the broadcast ephemeris is crucial for calculating the SISA. An orbit and clock error prediction method based on the Long Short-Term Memory (LSTM) neural network is proposed in this study. This method uses historical satellite orbit and clock errors, derived from the difference between precise and broadcast ephemeris, to train an LSTM neural network. The trained LSTM model is then used to predict the orbit and clock errors for the next 24 h. The predicted errors are used to calculate the corresponding SISA for the broadcast ephemeris. The results indicate that the 24 h accuracies of the predicted orbit and clock errors are less than 0.06 m and 0.25 m, respectively. The calculated SISA can completely envelop the actual broadcast ephemeris SISRE, achieving 100% coverage while also decreasing the over-envelopment margin by 80%. Furthermore, the horizontal protection level and vertical protection level calculated using the corresponding SISA are reduced by approximately 14%, meeting the integrity requirements set by the International Civil Aviation Organization for Approach with Vertical Guidance I. The proposed method enhances the timeliness of SISA parameter calculation and the integrity and availability of the BDS-3 service, serving as a crucial reference value for safety critical applications such as civil aviation services.

Stellar stripping efficiencies of satellites in numerical simulations: the effect of resolution, satellite properties and numerical disruption

Abstract The stellar stripping of satellites in cluster haloes is understood to play an important role in the production of intracluster light. Increasingly, cosmological simulations have been utilised to investigate its origin and assembly. However, such simulations typically model individual galaxies at relatively coarse resolutions, raising concerns about their accuracy. Although there is a growing literature on the importance of numerical resolution for the accurate recovery of the mass loss rates of dark matter (DM) haloes, there has been no comparable investigation into the numerical resolution required to accurately recover stellar mass loss rates in galaxy clusters. Using N-body simulations of satellite galaxies orbiting in a cluster halo represented by a static external potential, we conduct a set of convergence tests in order to explore the role of numerical resolution and force softening length on stellar stripping efficiency. We consider a number of orbital configurations, satellite masses and satellite morphologies. We find that stellar mass resolution is of minor importance relative to DM resolution. Resolving the central regions of satellite DM halos is critical to accurately recover stellar mass loss rates. Poorly resolved DM haloes develop cored inner profiles and, if this core is of comparable size to the stellar component of the satellite galaxy, this leads to significant over-stripping. To prevent this, relatively high DM mass resolutions of around MDM ∼ 106 M⊙, better than those achieved by many contemporary cosmological simulations, are necessary.

Single laser based novel wavelength shift keying scheme for ground to satellite bidirectional links

Abstract Laser communication in space is gaining more recognition due to its ability to provide a much broader and unregulated data transmission capacity compared to traditional radio frequency systems, while also significantly decreasing the size, power requirements, and weight of the communication system. In this work, a novel technique for the generation of a wavelength shift keying (WSK) signal for the bidirectional transmission of 10 Gbps data between the ground station and low Earth orbit (LEO) satellite is presented. Our proposed scheme requires a single laser source to transmit WSK signal, that generally requires two wavelength sources to transmit the binary data bits. Furthermore, the scheme allows us to remodulate the optical signal received at the LEO satellite for downlink signal transmission. Therefore, the requirement of a laser source at the LEO satellite is eliminated, achieving the much desirable goals of low power consumption, weight and cost. The free space optical (FSO) channel is modelled using the gamma-gamma channel model. The performance of the proposed link is also compared with a conventional On-Off keying (OOK) based modulation scheme by using bit-error-rate (BER) as the performance metric.

Quantifying the Environmental Impacts of Manufacturing Low Earth Orbit (LEO) Satellite Constellations

Quantifying the Environmental Impacts of Manufacturing Low Earth Orbit (LEO) Satellite Constellations

Simulation of wind-temperature-density measurement in near space by spaceborne Fe resonance fluorescence Doppler lidar

Spaceborne resonance fluorescence Doppler lidar uses metal atoms as tracers to detect atmospheric temperature, wind speed, and metal atom number density from the top of the mesosphere to the bottom of the thermosphere in the global atmosphere. This study proposes a concept of spaceborne Fe resonance fluorescence Doppler lidar (spaceborne Fe lidar). To theoretically analyze the feasibility of this technology, key parameters of the lidar were designed. Starting from the lidar equation, the echo signal intensity of spaceborne Fe lidar and the random error of atmospheric parameters were simulated and analyzed. Satellite orbit height is designed to be 400 km, monopulse energy is 300 mJ, and vertical and horizontal distance resolution is 2 km and 230 km, respectively. The simulation calculation shows that the random error of temperature detection of the spaceborne Fe lidar is 3.32 K, the line of sight wind speed is 1.67 m/s, and the horizontal wind speed is 3.34 m/s at 90 km. At the same time, the atmospheric density and temperature of 30-70 km can be detected at night, and the error is less than 10%. The conclusion of this paper has reference value for developing spaceborne resonance fluorescence Doppler lidar to detect atmospheric parameters in near space.

The CNN-LSTM-attention Model for short term prediction of the Polar Motion

Abstract The accuracy of polar motion prediction significantly impacts the fields of coordinate frame transformation, satellite orbit determination, and deep space exploration. The present study develops two short term forecasting models based on the EOP 14C04 series. One hybrid approach incorporates convolutional neural networks (CNN) and long short-term memory networks (LSTM), augmented with an attention mechanism; whereas another baseline model comprises CNN and LSTM. The first model, in contrast to the second model, incorporates an attention mechanism module for a more comprehensive integration of temporal information at each time step. In the initial short-term forecasting experiment, we conducted 360 repeated predictions, and the findings revealed that the parameters suitable for PMX forecasting may not necessarily be applicable to PMY forecasting. In the second experiment, the two models generated a total of 500 forecasts, each encompassing short-term predictions ranging from 1 to 30 days. The experimental results demonstrate that the first model exhibits mean absolute error (MAE) range of 0 ~ 7.72 mas for PMX and 0 ~ 4.73 mas for PMY, while the second model shows MAE range of 0 ~ 7.88 mas for PMX and 0 ~ 4.78 mas for PMY. After two exploratory experiments, we discovered the following results: the first model exhibits marginally superior predictive accuracy compared to the second model. Furthermore, this study substantiates the robustness of both models in short-term prediction and affirms the significance of assigning distinct weights to past temporal intervals in forecasting, thereby offering a novel perspective for polar motion prediction research.

Latency‐Sensitive Service Function Chains Intelligent Migration in Satellite Communication Driven by Deep Reinforcement Learning

ABSTRACTSatellite communication technology solves the problem that the traditional wired network infrastructure is difficult to achieve global communication coverage. However, factors such as satellite orbits introduce frequent changes to the network topology, and challenges like satellite failures and communication link interruptions are prevalent. In the face of these issues, service function chain (SFC) migration becomes a crucial method for swiftly adjusting SFCs during faults, maintaining service continuity and availability. This article proposes a latency‐sensitive SFC migration algorithm tailored to satellite networks. The algorithm first models the satellite network as a multi‐domain virtual network, capturing the constraints faced during SFC migration. Subsequently, a deep reinforcement learning algorithm integrated attention mechanism is designed to more accurately capture and understand the complex network environment and dynamic satellite network topology and derive optimal SFC migration strategies for superior performance. Finally, through experimentation and evaluation of the deep reinforcement learning‐driven latency‐sensitive service function chain intelligent migration algorithm (LS‐SFCM) in satellite communication, this study validates the effectiveness and superior performance of the algorithm in latency‐sensitive scenarios. It provides a new avenue for enhancing the service quality and efficiency of satellite communication networks.

Design of satellite orbits in the sun-earth-moon system

Design of satellite orbits in the sun-earth-moon system

Utility optimization for computation offloading and splitting in time-varying HAP and LEO satellite integrated MEC networks

To provide ubiquitous and low-latency communication and computation services for remote and disaster areas, high altitude platform (HAP) and low earth orbit (LEO) satellite integrated multi-access edge computing (HLS-MEC) networks have emerged as a promising solution. However, most current studies directly assume that the number of connected satellites is fixed and neglect the modeling of the time-varying multi-satellite computing process. Motivated by this, we establish an M/G/K(t) queuing model to illustrate task computation on satellites. To evaluate the efficiency and quality of computation offloading and splitting, we develop a utility model. This model is defined as a difference between a value function that assesses the trade-offs of task offloading, considering latency reductions and energy savings, and a cost function that quantifies expenses related to latency and energy consumption. After formulating the utility maximization problem, we propose the deep reinforcement learning-based offloading and splitting (DBOS) scheme that can overcome the time-varying uncertainties and high dynamics in the HLS-MEC network. Specifically, the DBOS scheme can learn the best computation offloading and splitting policy to maximize the utility by sensing the number of connected satellites, the distance between the HAP and satellites, the available computing resources, and the task arrival rate. Finally, we evaluate and validate the computational complexity and convergence property of the DBOS scheme. Numerical results show that the DBOS scheme outperforms the other three benchmarks and maximizes the utility under time-varying dynamics.

Modeling of Solar Radiation Pressure for BDS-3 MEO Satellites with Inter-Satellite Link Measurements

As the largest non-gravitational force, solar radiation pressure (SRP) causes significant errors in precise orbit determination (POD) of the BeiDou global navigation satellite system (BDS-3) medium Earth orbit (MEO) satellite. This is mainly due to the imperfect modeling of the satellite’s cuboid body. Since the BDS-3’s inter-satellite link (ISL) can enhance the orbit estimation of BDS-3 satellites, the aim of this study is to establish an a priori SRP model for the satellite body using 281-day ISL observations to reduce the systematic errors in the final orbits. The adjustable box wind (ABW) model is employed to refine the optical parameters for the satellite buses. The self-shadow effect caused by the search and rescue (SAR) antenna is considered. Satellite laser ranging (SLR), day-boundary discontinuity (DBD), and overlapping Allan deviation (OADEV) are utilized as indicators to assess the performance of the a priori model. With the a priori model developed by both ISL and ground observation, the slopes of SLR residual for the China Academy of Space Technology (CAST) and Shanghai Engineering Center for Microsatellites (SECM) satellites decrease from −0.097 cm/deg and 0.067 cm/deg to −0.004 cm/deg and −0.009 cm/deg, respectively. The standard deviation decreased by 21.8% and 26.6%, respectively. There are slight enhancements in the average values of DBD and OADEV, and a reduced β-dependent variation is observed in the OADEV of the corresponding clock offset. We also found that considering the SAR antenna only slightly improves the orbit accuracy. These results demonstrate that an a priori model established for the BDS-3 MEO satellite body can reduce the systematic errors in orbits, and the parameters estimated using both ISL and ground observation are superior to those estimated using only ground observation.

Inter-beam handover schemes for LEO satellites in 5G satellite–terrestrial integrated networks

Unlike terrestrial handover schemes, the handover schemes in 5G satellite–terrestrial integrated network (STIN) face several challenges, such as large propagation delay, complex channel environment, and fast satellite movement. Therefore, the handover schemes designed in the 5G terrestrial network is not suitable for the 5G STIN. In view of this, this paper considers the inter-beam handover problem for the 5G STIN with a low earth orbit satellite and a ground user. We establish the satellite channel model, which includes path loss, rain attenuation, and multi-beam antenna gain. To model the mobile feature of the user, we consider a two-dimensional random walk model. Then, we propose A5 event-based conditional handover (CHO) scheme, time factor-based CHO scheme, and load factor-based CHO scheme to achieve efficient inter-beam handover. Considering that there may be multiple beams that meet the triggering condition in each handover scheme, we also propose three kinds of beam selection schemes, namely random beam selection scheme, maximum power-based beam selection scheme, and minimum distance-based beam selection scheme. To evaluate the performance of the proposed handover schemes, key performance indicators, such as handover frequency, ping-pong handover rate, unnecessary handover rate, handover failure rate, and average transmission rate are analyzed. Simulation results verify the superiority of the proposed handover schemes by comparing them with the benchmark scheme.

Moving target detection based on multi-satellite joint passive microwave imaging

Using satellite formations to form a space-based passive interferometric imaging system can achieve high spatial resolution, but the sparse detection baseline will cause aliasing in the inversion image, affecting the detection of single-pixel point targets. Considering the slowly varying characteristics of the observation area, a target detection method based on image sequence is proposed. The background is estimated by multi-frame images, and the background of the inversion image is eliminated; energy is gathered based on sidelobe characteristics, the noise in the target area is estimated, and candidate targets are selected; the motion trajectory of the target is obtained by combining the temporal motion characteristics, and the detection of moving point targets is realized. Taking the formation system composed of 10 geostationary orbit satellites as an example, 300 simulation experiments are carried out for the detection of 50 ship point targets. The results show that the proposed method can realize point target detection in a large field of view, with an average false alarm rate 14.5%and an average missed alarm rate 19.5%.

Impact of different range bias corrections on orbit and earth rotation parameters determination using BDS-3 satellite laser ranging observations

Abstract Satellite Laser Ranging (SLR) is one of the important techniques that determine geodetic parameters, whose observation processing often calibrates range bias corrections to offset systematic errors. However, the impact of different range bias calibration methods on estimating BDS-3 satellite orbit and Earth Rotation Parameters (ERP) has not been fully studied. This study aims to explore the impact of employing different SLR range bias corrections on the accuracy of SLR-based BDS-3 satellite orbit and ERP. Through experimental analysis of eight months, it is found that the station-satellite-pair-dependent range bias correction leads to the optimal orbit accuracy. Regarding orbit differences relative to precise ephemeris and overlap differences, the 3D Root-Mean-Squares (RMSs) of satellites manufactured by the China Academy of Space Technology (CAST) are 1.00 and 0.94 m, respectively. And the corresponding values of satellites manufactured by the Shanghai Engineering Center for Microsatellites (SECM) are 0.98 and 0.90 m, respectively. The station-satellite-pair-dependent range bias correction performs best in pole coordinates accuracy. The RMSs of XP and YP differences relative to the International Earth Rotation and Reference Systems Service (IERS) 20 C04 product are 1.32 mas and 1.41 mas, respectively. The solution using satellite-dependent range bias corrections has the optimal the Length of Day (LOD) accuracy, with a 44.92 μs RMS of the LOD difference. However, due to the apparent satellite-related error characteristic reflected in the SLR residual, the station-dependent range bias correction is unsuitable for simultaneously processing SLR observation of all BDS-3 satellites.

Ionospheric TEC and its irregularities over Egypt: a comprehensive study of spatial and temporal variations using GOCE satellite data

Abstract This study delves into ionospheric characteristics during solar cycle 24 using data from the Global Positioning System (GPS) and Low Earth Orbit (LEO) satellites, GOCE. The present study fills the gap of not assessing and studying GOCE satellite data before in Egypt. Focusing on spatial and temporal variations of ionospheric Total Electron Content (TEC) over Egypt and the Middle East across a 4-year dataset, monthly average Vertical Total Electron Content (VTEC) variations are scrutinized, emphasizing extremes during heightened and reduced solar and geomagnetic activities. Results TEC typically decreases with latitude’s increase, with peak ionization during equinoxes and troughs during solstices. Notably, VTEC values consistently reach maximum values on days of heightened solar and geomagnetic activities. GOCE data is evaluated against International Reference Ionosphere (IRI) model and NeQuick2 model by selecting 10 days from all data by using a statistical comparison via t-tests. There is not significant difference between them except for two days between GOCE-IRI. The values of Root Mean Square Error (RMSE) are 3.7403 and 4.4655 for GOCE – NeQuick2 model and GOCE – IRI model, respectively. Ionospheric scintillation, signifying rapid electron density fluctuations, is assessed through amplitude scintillation index (S4), S4 proxy, and Rate Of TEC Index (ROTI). A robust correlation between S4 and S4 proxy is noted, thus scintillation can be studied by using S4 proxy instead of S4. Temporal variations indicate heightened scintillation during geomagnetic storms and peak solar activity, contrasting with reduced activity during solar minimum. Throughout the study period the maximum scintillation index values for ROTI, S4, and S4 proxy are 0.3, 0.15, and 0.05, indicating minimal scintillation. This comprehensive analysis, rooted in GOCE data, illuminates spatial and temporal dynamics of ionospheric TEC and elucidates ionospheric scintillation characteristics in the Egypt region. These findings are crucial for refining ionospheric models, improving scintillation prediction, and enhancing satellite communication and navigation systems, especially in the study region.

Enhancing Satellite Link Security Against Drone Eavesdropping Through Cooperative Communication

ABSTRACTIntegrated satellite terrestrial networks (ISTNs) are emerging as a promising next‐generation communication technology, for example, B5G and 6G, with low‐earth orbit (LEO) satellites playing a growing role. However, the complex and unique characteristics of ISTNs make them more susceptible to cyberattacks. Recently, the use of drones for public and private services has increased the risk of eavesdropping on LEO satellite links. Such scenario presents an extremely challenging environment due to dynamic nature of LEO satellite and drone along with atmospheric attenuation at sub‐THz frequencies. This study proposes a novel adaptive power‐bandwidth cooperative scheme designed to mitigate the likelihood of eavesdropping attacks on LEO satellite links communicating with a ground station when a drone is within the line of sight. The mathematical algorithm dynamically adapts the resources to maximize the normalized secrecy capacity in this challenging scenario while maintaining a reasonable signal‐to‐noise ratio (SNR) at the legitimate receiver. The adaptive scheme involves strategic cooperation with a nearby terrestrial third party to amplify and forward the satellite signal to the ground station receiver. The simulation results demonstrate the effectiveness of the proposed algorithm, showing significant improvements (> 70%) compared to the non‐adaptive scheme over a wide range of elevation angles.

Simulation case study of precise orbit determination for IGSO satellites using onboard Ka/GNSS observations

Abstract Inter-satellite links can provide a more accurate space reference for IGSO satellites than onboard GNSS side-lobe signals. In this study, POD quality is analyzed based on onboard Ka/GNSS observations by utilizing a simulated IGSO satellite orbit. Considering the similar orbit altitude, we first select three IGSO satellites from BDS-3 as target satellites. These satellites have actual Ka-band measurements available, along with other BDS-3 satellites and ground anchor stations, allowing us to assess the POD performance. The Ka-band availability of these IGSO satellites can serve as a means to evaluate the maximum utilization of BDS Ka-band resources. In terms of the POD results, the root mean square (RMS) of the Ka-band residuals can reach 10.9 cm, and the orbit consistency in the 3D direction is better than 0.35 m. In addition, simulations are conducted for onboard GNSS, inter-satellite link (ISL), and ground anchor-satellite link (GSL), considering the different constraints on the Ka-band resources. With only GNSS measurements, the 3D orbit accuracy is approximately 1.5 m, but when additional ISL and GSL measurements are considered, the POD accuracy can surpass 0.28 m. These results illustrate that incorporating Ka-band measurements can effectively enhance the POD accuracy for high-orbit satellites.

A Review of Satellite-Based CO2 Data Reconstruction Studies: Methodologies, Challenges, and Advances

Carbon dioxide is one of the most influential greenhouse gases affecting human life. CO2 data can be obtained through three methods: ground-based, airborne, and satellite-based observations. However, ground-based monitoring is typically composed of sparsely distributed stations, while airborne monitoring has limited coverage and spatial resolution; they cannot fully reflect the spatiotemporal distribution of CO2. Satellite remote sensing plays a crucial role in monitoring the global distribution of atmospheric CO2, offering high observation accuracy and wide coverage. However, satellite remote sensing still faces spatiotemporal constraints, such as interference from clouds (or aerosols) and limitations from satellite orbits, which can lead to significant data loss. Therefore, the reconstruction of satellite-based CO2 data becomes particularly important. This article summarizes methods for the reconstruction of satellite-based CO2 data, including interpolation, data fusion, and super-resolution reconstruction techniques, and their advantages and disadvantages, it also provides a comprehensive overview of the classification and applications of super-resolution reconstruction techniques. Finally, the article offers future perspectives, suggesting that ideas like image super-resolution reconstruction represent the future trend in the field of satellite-based CO2 data reconstruction.

Numerical Modeling and Analysis of Spatial Monotonic Approach Conditions of Two Satellites in Mars Orbit

Numerical Modeling and Analysis of Spatial Monotonic Approach Conditions of Two Satellites in Mars Orbit

Fusion propulsion for exploring the solar system and beyond

Fusion propulsion for exploring the solar system and beyond Dr Kelvin F Long, Aerospace Engineer and Astrophysicist, leads the Interstellar Research Centre, a division of Stellar Engines Ltd. He argues that fusion propulsion will enable the full exploration of the solar system and beyond. Beginning from the last century with the first orbital satellites placed into space, humanity explored all of the planets of our solar system using robotic probes. This includes the launch of the Voyager 1 and 2 probes in 1977 which are now far outside of the solar system and headed out into interstellar space. It is feasible that in the future we may construct probes that can go much faster and further.

Decision Transformer-Based Efficient Data Offloading in LEO-IoT.

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people's attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%.