In recent years, rapidly advanced space technology has resulted in a growing number of objects in orbit, including both operational and decommissioned spacecraft, as well as space debris, which pose potential threats to China’s on-orbit spacecraft. To ensure the safe operation of spacecraft in near-Earth and high-orbit environments, a key research focus in the field of space technology is the high-resolution and high-accuracy detection and perception of environmental objects under strong backlight conditions. Such capabilities are critical for providing timely early warnings to on-orbit spacecraft. However, a plethora of studies have demonstrated marked challenges in imaging dark and weak targets under strong light backgrounds. It is essential to analyze the challenges of current backlight imaging technologies, summarize the working principles of existing backlight detection methods, and explore future development trends.

iant planet in our solar system. Currently, numerous engineers are engaged in efforts to deploy probes into the Jovian system, and some key technical research studies are also carried out for future Jovian system exploration missions. However, capturing into another planet’s system is a costly process, particularly for the Jovian system. The presence of 4 Galilean moons renders the gravity-assist technique a highly effective means of modifying the velocity of probes relative to Jupiter. However, if a Jovian moon is expected to be visited, the perijove of the spacecraft must be lower than the orbital radius of the moon. There are disadvantages to capturing with a low perijove. Firstly, the spacecraft is severely affected by the strong radiation of Jupiter, which is harmful to the electronic components on the spacecraft when it gets close to Jupiter. Secondly, an amount of fuel is consumed to raise the perijove to satisfy the requirements of subsequent objectives. In order to alleviate the disadvantages of low-perijove capture, one solution is to raise the perijove more efficiently by leveraging the solar gravity perturbation (SGP).

Absolute ages provide vital information in establishing the geological history of the Solar System’s solid bodies. For extraterrestrial bodies, only the Moon has been sampled in a few areas for radiometric dating in the laboratory. For the unsampled areas on the Moon, the widely used surface dating method is based on the crater size–frequency distribution (CSFD) technique. However, there are no returned Martian samples, so the above method cannot be directly applied for Mars. The Martian isochrons have usually been derived based on the production function (PF) and chronology function (CF) of the Moon. Many uncertainties cast a shadow on the reliability of the current Martian isochrons (e.g., the maximum age difference is up to ~1.0 Ga from meteor flux ratios of 2.04 and 3.0). Thus, obtaining the Martian crater PF directly from remote sensing observations is a necessary and significant task. 

Relevant technological advancements in recent years have established solar sailing as an attractive propulsion system for terrestrial and interplanetary space missions, particularly missions that require low-thrust propulsion for long periods of time. The initial stages in designing solar sail missions involve a thorough analysis of possible transfer trajectories. Traditional approaches primarily focus on deterministic scenarios where the spacecraft dynamics is precisely defined and fully characterized. Whereas, space trajectories are inevitably subject to various sources of uncertainty, including modeling errors in the dynamics, inaccurate measurements, and unpredictable environmental effects, such as fluctuations in solar irradiance. These uncertainties necessitate the development of robust control strategies that can effectively handle stochastic effects. In recent years, reinforcement learning (RL) offers a promising framework for addressing optimization problems characterized by significant uncertainty, which can be challenging to handle using traditional methods. Meanwhile, deep reinforcement learning (DRL) utilizes neural networks (NNs) to approximate complex policies, i.e. control laws. 

Einstein’s general theory of relativity reveals that spacetime can be warped by mass and energy. As a conjecture in this context, the evolution of space-time curvature affected by gravitation will propagate through the vast universe like ripples. There remains a wealth of mysteries about astrophysics that have not been explored yet, and these “ripples” called gravitational waves may revolutionize the investigation for this. Space-based gravitational-wave observations, which broadly adopt heliocentric, geocentric, or libration-point architectures, can extend the scope to celestial bodies that generate valuable millihertz frequency waves and escape the Earth-bound noise. The heliocentric gravitational-wave detection relying on laser interferometry faces dual challenges: 1) the attitude errors couple into the interferometric arm-length measurements, potentially corrupting the picometer-level displacement sensitivity, making the attitude accuracy exceptionally rigorous, while 2) the ultra-long interferometric arms (and thus million-kilometer-scale intersatellite communication distances) introduce significant communication delays. As a key to space-based gravitational-wave observations, the past few years have witnessed the development of attitude-orbit control technique, and numerous control methodologies have sprung up for various space missions. However, current communication delay models and their corresponding compensation methods prove inadequate for heliocentric gravitational-wave detection scenarios. Additionally, in line with adaptive SMC, there are few investigations on attitude-orbit coordinated control for heliocentric gravitational-wave detection spacecraft formation.