SPACE/COSMOS
AI enabled launch vehicles: Next potential disruptive technology after reusability
Tsinghua University Press
The global space industry has entered a new phase, characterized by the construction of large-scale satellite constellation construction, human lunar exploration and development and others. As the prerequisite and foundation of all space activities, space transportation systems are experiencing rapid growth in the scale of access to space. Especially with the increasing commercialization, it is expected that within the next 20 years, the scale of access to space will exceed several hundred thousand tons, with annual launch numbers reaching tens of thousands, and this number will continue to rise. Space transportation systems face the challenge of low-cost, high-frequency, and highly reliable airline-flight-mode development.
The rapidly developing reusable technology can effectively solve the current issue of high launch costs. However, high-frequency launches and highly reliable flights still remain significant challenges. In recent years, artificial intelligence (AI) technology has rapidly developed and has become important across various industries. Enabled space transportation with AI technology holds great promise in addressing the challenges of high-frequency launches and highly reliable flights in the era of large-scale access to space. It is expected to become the next disruptive technology in the field of space transportation, following reusability.
AI can be enabled in four scenarios to achieve agile testing and launch preparation, significantly improve flight reliability, achieve rapid maintenance and efficient safety operation & control.
Based on AI methods, the authors’ goal is to build an intelligent space transportation system that includes smart test launches, high-reliability flight, agile maintenance assessment, and efficient operation control, aiming to achieve test, inspection, and decision-making times for large launch vehicles at the hour level. By integrating return-to-launch-site (RTLS) technology, the system will enable hour-level re-launches after recovery. The autonomous fault location time for test launches will reach the minute level, and the flight reliability will improve by 1-2 orders of magnitude even in case of non-fatal faults. Meanwhile reliable and agile health assessment and maintenance strategies are established to support the optimization of reusable lifespan of launch vehicles. Besides, the integrated space-earth situational awareness, flight operation management scheduling, and coordinated control are realized to support the reliable and safe operation management and control of the entire space-earth system.
Original Source
Haipeng Chen, Xiaowei Wang, Feng Zhang. AI Enabled Launch Vehicle: Next Potential Disruptive Technology After Reusability [J]. Chinese Journal of Aeronautics, 2025, 38(10): 103756, https://doi.org/10.1016/j.cja.2025.103756.
About Chinese Journal of Aeronautics
Chinese Journal of Aeronautics (CJA) is an open access, peer-reviewed international journal covering all aspects of aerospace engineering, monthly published by Elsevier. The Journal reports the scientific and technological achievements and frontiers in aeronautic engineering and astronautic engineering, in both theory and practice. CJA is indexed in SCI (IF = 5.7, Q1), EI, IAA, AJ, CSA, Scopus.
Journal
Chinese Journal of Aeronautics
Article Title
AI Enabled Launch Vehicle: Next Potential Disruptive Technology After Reusability
ACA-SIM: A robust way to decode satellite signals over complex waters
image:
The spatial distribution of field-measured data used in this study.
view moreCredit: Journal of Remote Sensing
A new study introduces ACA-SIM (atmospheric correction based on satellite–in situ matchup data), a neural-network-based atmospheric correction algorithm that uses real satellite–Aerosol Robotic Network-Ocean Color (AERONET-OC) matchups to improve the accuracy of atmospheric correction over coastal waters. By learning from real-world satellite radiance and in-situ reflectance data, ACA-SIM significantly reduces errors and striping artifacts in ocean color products, outperforming existing models in turbid water and complex-aerosol conditions such as the Bohai Sea, North Africa dust zones, and Australian bushfire regions.
Coastal waters are among the most dynamic and complex environments on Earth, where suspended sediments, dissolved organic matter, and aerosols make satellite observation extremely challenging. Traditional atmospheric correction methods often rely on simplified assumptions that fail in turbid or dusty regions, resulting in inaccurate retrievals of ocean color and water quality parameters. The difficulty is further amplified by sensor noise and striping artifacts. Due to these challenges, it is essential to develop a robust atmospheric correction approach that can adapt to real-world conditions in coastal environments, improving the reliability of global ocean color observations.
Researchers from Xiamen University published (DOI: 10.34133/remotesensing.0886) their findings in the Journal of Remote Sensing on October 16, 2025. Their paper presents ACA-SIM (atmospheric correction based on satellite–in situ matchup data)—an atmospheric correction algorithm based on satellite–Aerosol Robotic Network-Ocean Color (AERONET-OC) matchup data—which uses neural networks trained on real satellite and field measurements. The method addresses the persistent problem of retrieving accurate remote-sensing reflectance (Rrs) from coastal waters affected by aerosols, dust, and smoke, offering a promising alternative to existing NASA approaches.
The ACA-SIM algorithm employs a multilayer neural network trained on more than 8,800 satellite–AERONET-OC matchups spanning diverse water and aerosol types worldwide. Unlike previous models that relied on simulated data, ACA-SIM directly incorporates real-world sensor effects—such as striping and stray light—into its learning process. When tested against independent field data, ACA-SIM achieved an average mean absolute percentage difference (MAPD) of ~15% in blue spectral bands, compared with ~32% for OC-SMART and >50% for NASA's standard algorithm. It maintained stable performance even under high solar angles, strong glint, or absorbing aerosol conditions, demonstrating its resilience and general applicability to various coastal environments.
To construct the training dataset, the team combined satellite top-of-atmosphere reflectance (ρt) from MODIS-Aqua with in-situ Rrs from more than 40 AERONET-OC stations worldwide. These matchups captured a wide range of observation geometries, turbidity levels, and aerosol types—from marine and continental aerosols to strongly absorbing dust and smoke. A four-layer multilayer perceptron (512–256–128–64 neurons) was optimized to predict Rrs at 12 MODIS wavelengths between 412 and 869 nm. Validation against AERONET-OC and ship-based datasets confirmed that ACA-SIM consistently outperforms NASA and OC-SMART methods in both accuracy and robustness. When applied to MODIS-Aqua imagery over the Bohai and Yellow Seas, West Africa dust regions, and Australian bushfire zones, ACA-SIM eliminated negative Rrs values, minimized striping artifacts, and preserved realistic water-mass patterns—delivering clean, physically consistent ocean color maps even in challenging atmospheric conditions.
“Our goal was to let the algorithm learn from reality rather than simulation,” said Prof. Zhongping Lee, corresponding author of the study. “By training ACA-SIM on genuine satellite–field matchups, we allowed it to capture subtle sensor behaviors and atmospheric effects that synthetic datasets cannot reproduce. The outcome is a smarter, more reliable correction system that ensures accurate monitoring of coastal ecosystems under even highly complex atmospheric scenarios.”
The team compiled global AERONET-OC data (2002–2024) and matched them with MODIS-Aqua satellite radiance within a ±1-hour window to ensure temporal consistency. A rigorous data-quality screening excluded contaminated pixels while preserving moderately glinted or hazy cases to enhance model generalization. The neural network was trained using 80% of the matchups and validated on the remaining 20%, with early-stopping criteria to prevent overfitting. Statistical metrics—including coefficient of determination (R²), MAPD, and bias—were used to benchmark ACA-SIM against NASA Standard and OC-SMART algorithms.
The success of ACA-SIM highlights the power of data-driven approaches in Earth observation. The researchers plan to extend this framework to other satellite sensors such as VIIRS and Sentinel-3, aiming for a unified, cross-platform atmospheric correction model. By generating consistent and accurate ocean color products, ACA-SIM could transform long-term coastal monitoring, improve assessments of algal blooms, sediment transport, and carbon fluxes, and ultimately support sustainable management of marine environments in an era of accelerating climate change.
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References
DOI
Original Source URL
https://doi.org/10.34133/remotesensing.0886
Funding information
We are grateful for the financial support from the National Key Research and Development Program of China (2022YFC3104901), the National Natural Science Foundation of China (#42430107), Fujian Ocean Innovation Laboratory, Fujian Satellite Data Development, Co., Ltd., and Fujian Haisi Digital Technology Co., Ltd.
About Journal of Remote Sensing
The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.
Journal
Journal of Remote Sensing
Subject of Research
Not applicable
Article Title
Atmospheric Correction of Coastal Waters Based on Satellite–AERONET-OC Matchups
When satellites fall short: How 5G and GNSS team up for reliable urban positioning
Aerospace Information Research Institute, Chinese Academy of Sciences
image:
Deeply integrated fusion of commercial 5G NR multi-beam signals and GNSS.
view moreCredit: Satellite Navigation
Finding accurate locations in dense cities has long challenged satellite-based navigation, where tall buildings and signal blockage often lead to large errors or complete service loss. A new study presents a deeply integrated positioning approach that combines commercial 5G New Radio (NR) signals with Global Navigation Satellite Systems (GNSS) to overcome these limitations. By strengthening 5G signal tracking and deeply fusing it with satellite measurements, the method significantly improves both ranging stability and positioning accuracy. Real-world tests show that the system maintains reliable performance even in heavily obstructed environments and when only a few satellites are available, offering a promising solution for robust urban navigation.
GNSS technologies underpin modern navigation, from smartphones to autonomous vehicles, but their reliability drops sharply in urban canyons, shaded streets, and complex outdoor environments. Traditional solutions often rely on inertial sensors or vision-based systems, which add cost, computational burden, or sensitivity to environmental conditions. Meanwhile, commercial 5G networks are rapidly expanding worldwide, providing dense infrastructure and wide bandwidth that are attractive for positioning. However, practical challenges—including multipath interference, clock bias, and complex multi-beam transmissions—have limited their real-world use for navigation. Based on these challenges, there is a clear need for an integrated approach that can fully exploit 5G signals while compensating for the weaknesses of satellite navigation.
In a study published (DOI: 10.1186/s43020-025-00183-8) in Satellite Navigation in 2025, researchers from Wuhan University, The Chinese University of Hong Kong (Shenzhen), and Shandong University of Science and Technology report a new positioning framework that tightly integrates commercial 5G NR signals with GNSS. The approach is designed to improve navigation reliability in urban environments where satellite signals are frequently degraded. By combining advanced 5G signal tracking with real-time fusion of satellite and cellular measurements, the system delivers more accurate and stable positioning under real-world conditions.
The research introduces a deep fusion strategy that enhances both signal tracking and positioning estimation. At the signal level, the team developed a method that jointly uses 5G synchronization and reference signals to stabilize tracking in environments affected by multipath interference. This design substantially reduces ranging errors compared with conventional 5G-only approaches. To further improve performance under weak signal conditions, a phase-stabilized Kalman filter was incorporated into the tracking loop, helping maintain accurate carrier phase estimates even when signal quality drops.
Beyond signal processing, the study applies an extended Kalman filter to deeply fuse 5G and GNSS measurements. Unlike loosely and tightly coupled methods, the fused position and clock estimates are fed back to correct the 5G tracking process in real time. Field experiments conducted in complex outdoor environments show that this deep integration significantly improves positioning accuracy in both horizontal and vertical directions. Importantly, the system continues to deliver reliable positioning when satellite visibility is severely limited—conditions under which conventional GNSS solutions often fail. These results demonstrate the practical value of combining 5G and satellite navigation in real urban settings.
“This work shows that commercial 5G signals can do much more than support communication,” an author of the study said. “By deeply integrating 5G with GNSS and addressing real-world challenges such as multipath and clock errors, we demonstrate a practical pathway toward reliable urban positioning. The key advantage is robustness—our system continues to work when satellite-only solutions struggle. This opens new possibilities for navigation in cities, where reliable positioning is often the hardest to achieve.”
The findings point to a wide range of applications in urban navigation and location-based services. Autonomous vehicles, delivery robots, smart transportation systems, and connected infrastructure could all benefit from more reliable positioning in challenging environments. Because the method leverages existing commercial 5G networks, it can be deployed without major changes to current infrastructure. As cities become more connected and navigation demands increase, deeply integrated 5G–GNSS solutions like this one may play a key role in enabling safe, accurate, and continuous positioning for next-generation intelligent systems.
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References
DOI
Original Source URL
https://doi.org/10.1186/s43020-025-00183-8
Funding information
The National Natural Science Foundation of China (42171417);
Medium- and Long-term Scientific and Technological Planning Projects for Radio, Television and Audio-Visual Networks;
Guangxi Science and Technology Major Project (AA22068072);
Major Science and Technology Project of Jiangsu Province (No.BG2024003).
About Satellite Navigation
Satellite Navigation (E-ISSN: 2662-1363; ISSN: 2662-9291) is the official journal of Aerospace Information Research Institute, Chinese Academy of Sciences. The journal aims to report innovative ideas, new results or progress on the theoretical techniques and applications of satellite navigation. The journal welcomes original articles, reviews and commentaries.
Journal
Satellite Navigation
Subject of Research
Not applicable
Article Title
5G NR robust tracking and positioning with GNSS assistance
Article Publication Date
8-Dec-2025
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