Real-time orbit determination of Low Earth orbit satellite based on RINEX/DORIS 3.0 phase data and spaceborne GPS data

The improvement of BDS Observation Geometry with LEO constellations in Orbit Determination

: This paper represents orbit propagation and determination of Low Earth Orbit(LEO) satellites, which is immediate orbit satellite positioning. Beiseds, satellite global positioning system (GPS) configured satellite receiver provides position and velocity measures by navigating the filter to get the coordinates of the orbit propagation (OP), while the satellite orbit coordinates is an important basis for the task operation. The main contradictions in real time orbit which is determined by the problem are: orbit positioning accuracy and the amount of calculating two indicators; how to balance these two indicators. This article is dedicated to solving the problem of tradeoffs. To plan to use a non-linear filtering method for immediate orbit tasks requires more precise satellite orbit state parameters in a short time. Although the traditional extended Kalman filter (Extended Kalman Filter, EKF) method is widely used, its linear approximation of the drawbacks in dealing with nonlinear problems was especially evident, without compromising Kalman filter (Unsented Kalman Filter, UKF). As a new non-linear estimation method, it is measured at the estimated measurements on more and more applications. This paper will be the first study to UKF microsatellites in LEO orbit in real time, trying to explore the real-time precision orbit determination techniques.

Gravity field recovery of inter-satellite links between Beidou navigation satellite system (BDS) and LEO based on geodesy and time reference in space (GETRIS)

Low earth orbit (LEO) satellite system becomes an emerging non-terrestrial network solution for future radio communications. To maximize the system capacity, an LEO satellite could utilize circularly polarized planar antenna array with the frequency reuse technique to form multiple earthmoving beams. With the increasing number of LEO satellites, inline interference from the geosynchronous equatorial orbit (GEO) satellite is an important issue to be resolved when the spectrum resource is shared. With the frequency hopping phenomena in earth-moving LEO networks, the inline interference could be minimized if the GEO users also perform the similar frequency hopping via coordination between LEO and GEO satellite networks. This paper proposes eight spectrum sharing schemes for GEO satellite networks to produce the frequency hopping sequence in earth-moving LEO network. We evaluate the system capacity, fairness between LEO and GEO users, and time costs of all schemes. Simulation results validate the advantage obtained from coordination, and show that the simple greedy scheme performs as good as the optimal scheme due to fixed frequency hopping patterns.

An opportunistic navigation framework using low Earth orbit (LEO) satellites is analyzed. This framework, termed simultaneous tracking and navigation (STAN), estimates a navigating vehicle’s state along with the states of orbiting LEO satellites. STAN employs an extended Kalman filter (EKF) to fuse measurements from satellite receivers and an inertial navigation system (INS). The navigation performance is analyzed due to: (i) the vehicle being equipped with (1) different inertial measurement unit (IMU) grades: consumer, industrial, and tactical and (2) different receiver clock quality: temperature-compensated crystal oscillators (TCXO) and oven-controlled crystal oscillators (OCXO) and (ii) the LEO satellites being equipped with different transmitter clock quality: OCXO and chip-scale atomic clock (CSAC). Additionally, the effect of utilizing a large number of LEO satellites for navigation is investigated. This analysis provides insight into the achievable performance of STAN, which can serve as an alternative navigation system in global navigation satellite system (GNSS)-denied environments. The performance predictions from simulations are compared with experimental results with real signals from the Orbcomm LEO constellation. A close match between the simulation and experimental results is demonstrated for an unmanned aerial vehicle (UAV) navigating via the STAN framework with signals from two Orbcomm LEO satellites for 160 seconds, the last 35 seconds of which are without GNSS signals. The UAV’s position root-mean squared error (RMSE) from simulations was 8.6 m, while the experimental position RMSE was 10 m.

Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.

6G non-terrestrial networks (NTNs) will use satellites for global internet access, where low earth orbit (LEO) satellites will be used frequently due to the lower latency and higher link budget that can be provided compared to higher altitude satellites. However, LEO satellites require many satellites due to their fast movement and limited coverage, leading to frequent handovers (HOs). This paper presents an intelligent LEO satellite conditional handover (ILCHO) scheme that improves the NTN performance by addressing the frequent HO issues while reducing the signal strength variation between cells and outdated measurement values. The ILCHO scheme derives the LEO satellite's state information (position and orbital path), which is used to obtain the conditions for a LEO satellite to be accessible to enable optimized target LEO satellite selection that will maximize the overall network service performance while minimizing the number of HOs. The ILCHO scheme uses multi-agent reinforcement learning (MARL) to maintain an optimized performance through early preparation by incorporating a distance-based event in the execution phase. The simulation results show that the proposed ILCHO scheme can increase the throughput and signal to noise ratio (SNR) performance compared to other HO algorithms, thereby providng improved and more stable communication conditions.

Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communications but also for various machine learning applications. However, a ground station (GS) may be incapable of downloading such a large volume of raw sensing data for centralized model training due to the limited contact time with LEO satellites (e.g. 5 minutes). Therefore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated learning</i> (FL) has emerged as the promising solution to address this problem via on-device training. Unfortunately, enabling FL on LEO satellites still face three critical challenges: i) heterogeneous computing and memory capabilities, ii) limited downlink/uplink rate, and iii) model staleness. To this end, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedSN</b> as a general FL framework to tackle the above challenges. Specifically, we first present a novel sub-structure scheme to enable heterogeneous local model training considering different computing, memory, and communication constraints on LEO satellites. Additionally, we propose a pseudo-synchronous model aggregation strategy to dynamically schedule model aggregation for compensating model staleness. Extensive experiments with real-world satellite data demonstrate that FedSN framework achieves higher accuracy, lower computing, and communication overhead than the state-of-the-art benchmarks.

Machine learning (ML) frameworks are investigated for use in simultaneous tracking and navigation (STAN) with low Earth orbit (LEO) satellites. STAN is a navigation paradigm that utilizes specialized LEO receivers to extract navigation observables (e.g., pseudorange and Doppler) from LEO satellite signals. Two neural network architectures are compared: Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN). Additionally, two ML-based orbit determination frameworks are compared: ephemeris propagation and residual error propagation. The objective of the comparison is to select an approach with the lowest open-loop propagation error as well as computational cost. Based on simulation results, a nonlinear autoregressive neural network with exogenous inputs (NARX) embedded within the residual error modeling framework is selected as the best ML approach among the compared candidates. Experimental results are presented demonstrating a ground vehicle navigating for a total of 258 seconds, while receiving signals from two Orbcomm LEO satellites. Global navigation satellite system (GNSS) signals were artificially cut off for the last 30 seconds, during which the vehicle traversed a trajectory of 871 m. Two navigation frameworks are compared to estimate the vehicle’s trajectory: (i) LEO signal-aided inertial navigation system (INS) STAN framework using Simplified General Perturbation (SGP4) as its propagator and (ii) the proposed LEO signal-aided INS STAN framework using ML as its propagator. The STAN with SGP4 achieved a three-dimensional (3-D) position root-mean squared error (RMSE) of 30 m. In contrast, the proposed STAN with SGP4+NARX framework achieved a 3-D position RMSE of 3.6 m.

The atmosphere reflects the distinct signatures associated with long-term changes due to solar cycle, short-term changes related to solar phenomena such as solar flares, coronal mass ejections (CMEs), high solar wind streams (HSS) and corotating interaction region (CIRs), as well as changes related to latitude variation. The orbital trajectory of low Earth orbiting satellites (LEOSs) are affected accordingly, as direct consequence of the variability of atmospheric temperature (T) and density (ρ). Atmospheric perturbation increases drag on LEOSs leading to accelerated orbital decay. In this chapter we present a compact review of atmospheric responses to solar activity and how they impact the orbital trajectory of low Earth orbiting (LEO) satellites. This review includes the recent progress we made in the studies related to space weather effects on the ionosphere and LEO satellites trajectory.

Low earth orbit (LEO) satellite has the advantages of global coverage, low propagation delay and low path losses, etc.Combined with orthogonal frequency division multiplexing (OFDM) transmission, LEO satellite system can support very high capacity suitable for wideband multimedia traffic.This paper analyzes the channel model and throughput for LEO OFDM satellite communication system.First, several channel models for LEO satellite system are expounded with simulations because channel model is the basis of throughput analysis.Second, the beam model and the signaling model for LEO satellite are introduced.Considering high speed movement of LEO satellite, channel fading and the channel quality information (CQI), link throughput is simulated.Finally, compared with MF-TDMA system, simulation results demonstrate that OFDM system outperforms the former because its feasibility and superiority in throughput.

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Abstract&amp;lt;/strong&amp;gt; Global Navigation Satellite Systems (GNSS), which are fundamental to Positioning, Navigation, and Timing (PNT), play also a critical role for the determination of ITRF and the monitoring of global geodetic parameters, such as geocenter and Earth Rotation Parameters (ERP), thanks to the globally distributed network. The Low Earth Orbit (LEO) satellites with onboard GNSS receivers as moving stations can significantly improve the observation geometry of a ground tracking network, and thus enhance the solutions. Previous studies have demonstrated that integrated processing of ground GNSS stations and LEO satellites can improve the GNSS satellite orbits and the determination of the geocenter, especially for spare networks. In this study, we further investigate the contribution of having five LEO satellites (two GRACE-FO satellites and three SWARM satellites) to a rather robust ground GNSS network, i.e., 120 stations. We focus not only on the GNSS satellite orbits, and the geocenter and ERP, but also on some other parameters like ambiguities. We further investigate the impact of the LEO orbit modelling and the weighting strategies of the LEO observations in order to properly consider the fact that onboard GNSS observations can be modelled more precisely than that from ground stations due to the differences in station environment and atmosphere.&amp;lt;/p&amp;gt;

For real-time precise orbit determination (POD) of low earth orbit (LEO) satellites, high-accuracy global navigation satellite system (GNSS) orbit and clock products are necessary in real time. Recently, the Japanese multi-GNSS advanced demonstration of orbit and clock analysis precise point positioning (PPP) service and the new generation of the Australian/New Zealand satellite-based augmentation system (SBAS)-aided PPP service provide free and precise GNSS products that are directly broadcast through the navigation and geostationary earth orbit satellites, respectively. With the high quality of both products shown in this study, a 3D accuracy of centimeters can be achieved in the post-processing mode for the reduced-dynamic orbits of small LEO satellites having a duty cycle down to 40% and at sub-dm to dm level for the kinematic orbits. The results show a promising future for high-accuracy real-time POD onboard LEO satellites benefiting from the precise free-of-charge PPP corrections broadcast by navigation systems or SBAS.

Recently many companies and consortia have considered launching LEO satellites for such projects as remote sensing, transportation and mining operations. When the multiple low Earth orbit (LEO) satellites are transmitting they can interfere with existing terrestrial microwave and satellite earth‐stations in the fixed and mobile service. The interference problem is related to the number of proposed LEOs and the altitude and consequently the time to orbit the earth. These systems usually consist of many small satellites, and each satellite stays in the beam of a terrestrial station for up to 72 seconds in each 222 minute orbit. Earth coverage could be obtained by 48 (LEO) satellites 1500 km above the earth in polar orbit, and hence at least one LEO would always be interfering with terrestrial networks. A technical evaluation would then be required to determine the resultant BER (bit error rate) effecting existing terrestrial services. A determination can then be made to support such a LEO system or object via official channels such as the ITU.

The precise point positioning (PPP) is widely used as a typical representative of GNSS. But the main challenge of dual-frequency PPP is that it requires quite a long time to succeed in the ambiguity-fixed so it takes more than 30 min to obtain centimeter-level accuracy. A combination of GPS and low earth orbit (LEO) satellites can improve the diversity of available satellites' geometric distribution and reduce the correlation of observation data between epochs. Then the accuracy of float solution will be promoted and the ambiguity of GPS will take less time to get fixed. The purpose of this paper is to reduce the convergence time of PPP by the contribution of LEO satellites which have low earth orbit and move speedily. We adopt a free-ionosphere PPP model to combine GPS and LEO satellites. Numerous experimental results show that GPS/LEO PPP requires much less convergence time and gets higher positioning accuracy than that of PPP with GPS alone. In addition, an analysis is also made to compare GPS/LEO PPP and GPS/GLONASS PPP. The results indicate that GPS/LEO PPP also requires much less convergence time and gets higher positioning accuracy than that of GPS/GLONASS PPP when increasing the same number of available satellites. This proved that LEO satellites have more contribution to PPP performance than MEO.