Shuailong Zhao

Accurate thermospheric mass density is crucial for precision orbit determination, trajectory prediction, and collision warning in the increasingly congested Low Earth Orbit (LEO) region. Traditional high-precision instantaneous inversion methods rely on dedicated onboard accelerometers (e.g., CHAMP and GRACE satellites), which are costly, scarce, and unable to meet the expansive monitoring needs of modern mega-constellations. In recent years, orbit data inversion methods based on high-precision orbit determination data have been extensively studied. Among them, the interpolation–differentiation method, although capable of retrieving instantaneous atmospheric density, severely amplifies position errors and measurement noise, significantly limiting retrieval accuracy. To address this gap, this study proposes a novel atmospheric density retrieval scheme for accelerometer-free LEO satellites, introducing an orthogonal polynomial (Legendre and Chebyshev) sliding-window fitting approach. The proposed method is evaluated using high-precision simulated orbit data. Under instantaneous inversion conditions, the Legendre method yields an average relative error (MRE) of 16.55%, a normalized root mean square deviation (NRMSD) of 12.80%, and a correlation coefficient R of 0.9370. To further evaluate its potential in climatological modeling, this paper also proposes a local solar time (LST) bin-averaging method to assess atmospheric inversion accuracy. Under this LST averaging approach, the inversion performance achieves an MRE of 6.83% and a correlation coefficient R exceeding 0.98. This demonstrates the feasibility of the inversion method for analyzing diurnal effects on atmospheric field evolution.

Aerodynamic manoeuvring technology for spacecraft actively utilizes aerodynamic forces to alter orbital trajectories. This approach not only substantially reduces propellant consumption but also expands the range of accessible orbits, representing a key technological pathway to address the demands of increasingly complex yet cost-effective space missions. The theoretical prototype of this technology was proposed by Howard London. Over the course of more than half a century of development, it has evolved into four distinct modes: aeroglide, aerocruise, aerobang, and aerogravity assist. These modes have been engineered and applied in scenarios such as in-orbit manoeuvring of reusable vehicles, rapid response to space missions, and interplanetary exploration. Our research centers on two core domains: trajectory optimization and control guidance. Trajectory optimization employs numerical methods such as pseudo-spectral techniques and sequential convex optimization to achieve multi-objective optimization of fuel and time under constraints, including heat flux and overload. Control guidance focuses on standard orbital guidance and predictive correction guidance, progressively evolving into adaptive and robust control to address atmospheric uncertainties and the challenges of strong nonlinear coupling. Although breakthroughs have been achieved in deep-space exploration missions, critical challenges remain, including constructing high-fidelity models, enhancing real-time computational efficiency, ensuring the explainability of artificial intelligence methods, and designing integrated framework architectures. As these technical hurdles are progressively overcome, this technology will find broader engineering applications in diverse space missions such as lunar return and in-orbit servicing, driving continuous innovation in the field of space dynamics and control.

The exponential growth of space objects in near-Earth and geostationary orbits has posed severe threats to space environment safety, with debris clouds from spacecraft breakup events being a critical concern. Debris cloud tracing, as a key technology for locating breakup points, faces dual challenges of insufficient precision in analytical methods and excessive computational load in numerical methods. To balance traceability accuracy with computational efficiency, this paper proposes a breakup time determination method integrating a clustering algorithm and the minimization of average relative distance. The method first calculates the average relative distance between fragment pairs and preliminarily estimates the breakup epoch using a golden section step-size optimization strategy. Subsequently, the density peak clustering (DPC) algorithm is introduced to eliminate abnormal fragments. The breakup epoch is then refined based on the cleansed fragment dataset, achieving high-precision localization. Validation through simulations of real breakup events demonstrates that this method significantly improves localization accuracy. It establishes a highly reliable temporal benchmark for space collision tracing, debris diffusion prediction, and orbital safety management.

The poor accuracy of the single solution of the initial orbit determination (IOD) affects the precise orbit determination, target matching and arc-segment correlation. This paper proposes a method for analyzing the uncertainty of the IOD results based on the Minimum Radar Admissible Region (MRAR), which describes the IOD results in the form of probability in the region bounded by the minimum admissible region. The MRAR can contain the real orbit state with a probability greater than 99.7%; the weight coefficients of each sampling point in the MRAR are calculated by using the Bayesian formula, and the probability density function of the IOD is obtained by using the weighted kernel density estimation method. The simulation experiment proves that the proposed method can well describe the possibility of each point in the MRAR to be the real orbit state, and can provide a better initial value for the subsequent space mission analysis.

With the help of specific emitter identification (SEI), the control efficiency of the satellite communication systems can be effectively improved by discriminating the individual satellite. In recent years, deep learning has been introduced into SEI to enhance identification performance because of its powerful classification capability. However, classical real-valued neural networks exhibit some limitations in extracting the radio frequency fingerprint (RFF) features from complex signals, limiting the improvement of identification accuracy. Thus, we proposed a complex-valued conditional adversarial generative network (CC-GAN) which can directly deal with complex signals. Through adversarial learning between the generator and the discriminator, the generator implements direct mapping from the dynamic noisy signals to the noise-free signals. In addition, an auxiliary classifier is introduced into the discriminator to make the discriminator able to label the sample, which effectively compress the proposed model. The experimental results for a signal dataset collected in a real environment demonstrated that the proposed model is superior to the traditional denoising methods in denoising performance, which effectively improves the identification accuracy under dynamic noises. Furthermore, the proposed model outperforms other deep learning models in terms of identification performance under various SNRs, which can effectively improve the robustness and adaptability of the SEI system for communication satellites in dynamic noisy environments.

Strategy selection is critical for constellation deployment missions, both in terms of energy consumption and time cost. The different effects of impulse thrust and continuous thrust on orbit elements lead to a different choice of strategy. With impulse thrust, constellation types are differentiated according to high and medium-low inclinations. Constellations with high inclination are deployed using a strategy that controls the inclination. Constellations with medium-low inclination are deployed using a strategy that controls the semi-long axis. With continuous thrust, constellations are classified according to high, medium, and low inclination. High inclination constellations are deployed with a strategy of controlling inclination. Medium inclination constellations are deployed with a strategy that controls the semi-long axis. Low inclination constellations are deployed with a strategy of directly applying continuous thrust.

Continuous thrust spacecraft in circular orbits have had a great influence on the identification and cataloging of space targets. Gaussian-type orbital element variational equations are simplified and approximated. Ground-based radar observation datasets are transformed into orbit elements datasets. The initial thrust and orbit elements are obtained by optimally solving the spatial parameter error sum of squares minimization problem with the Levenberg–Marquardt method. The simulation analysis is carried out under the high-precision orbit model, and the solution error of tangential acceleration is around 5 × 10⁻⁷ m/s², and that of normal acceleration is around 3 × 10⁻⁶ m/s²; the accuracy of the semi-major axis is 350 m, and the accuracy of inclination is 0.095°. The method is applicable to the preliminary identification of thrust and orbit elements for circular orbit continuous thrust spacecraft and can provide reliable initial values for the subsequent precision orbit determination of such spacecraft.

Satellite constellation optimization has gradually developed from simple single-objective optimization to complex multi-objective optimization. Among them, the high-dimensional and multi-objective dynamic optimization problem poses a great challenge to the performance of the algorithm. To improve convergence speed and optimization performance, researchers continue to improve modern optimization algorithms. Firstly, the construction process of the optimization model is reviewed. The model takes the coverage performance, network performance, and system cost as objective functions or constraints. Secondly, the general forms of multi-objective constellation optimization design models under various constraints are given, and the applications of modern optimization algorithms such as genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-2), multi-objective particle swarm optimization algorithm (MOPSO), differential evolution algorithm (DE) in different constellation design tasks, as well as their characteristics and development trends are introduced. Finally, it is pointed out that in the era of vigorous development of Leo mega-constellations, the combination of modern optimization algorithms and machine learning is the future research direction to solve high-dimensional and multi-objective dynamic optimization problems.