Emerging Satellite Technologies for Next-Generation Earth Observation and Navigation

Author

Wash Patrick Madugu, Otu Simon Achu, Mustafa Ibrahim Ahmed, Amanda-Roy C. Oguejiofo, Bello Muazu Maccido, Safwan Sani Toro, Awobotu Olaide Marcus

Abstract

Context: Global Navigation Satellite Systems (GNSS) remain the backbone of modern positioning, yet their fragility in urban canyons, forests, tunnels, and jammed environments has catalyzed a broad shift toward multi-sensor fusion architectures. Scope: This paper synthesizes 23 peer-reviewed studies published between 2024 and 2026, spanning precision agriculture, wildfire and flood mapping, GNSS-denied navigation, autonomous robotics, on-orbit satellite computing, and integrated satellite communication-navigation (ICAN) systems. Method: Through thematic synthesis and comparative analysis, we identify three converging threads: (i) the retirement of single-sensor systems in favor of redundant satellite-aerial-ground pipelines; (ii) the gradual displacement of classical Kalman filtering by learned fusion operators including graph neural networks, attention mechanisms, and deep reinforcement learning; and (iii) the emergence of LEO mega-constellations as dual-purpose communication-navigation infrastructure. Insight: We propose a six-layer reference architecture (sensing, edge and on-orbit pre-processing, adaptive fusion, AI-driven intelligence, communication and networking, and application-level decision-making), tied together by a continuous feedback loop rather than a one-way pipeline. The review closes with a gap-frequency analysis, a technology-readiness assessment, and an honest appraisal of persistent unsolved problems: cold-start sensitivity, on-board computational limits, non-line-of-sight signal degradation, and the absence of shared cross-domain benchmarks.

Keywords

sensor fusion; GNSS-denied navigation; LEO constellations; integrated communication and navigation; Earth observation; deep learning; Kalman filter; on-orbit computing

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References

• Al Saim, A., & Aly, M. (2025). Enhancing tree species mapping in Arkansas' forests through machine learning and satellite data fusion: A Google Earth Engine-based approach. Journal of Geovisualization and Spatial Analysis, 9(1), 1–21.
• Alghamdi, S., Alahmari, S., Yonbawi, S., Alsaleem, K., Ateeq, F., & Almushir, F. (2025). Autonomous navigation systems in GPS-denied environments: A review of techniques and applications. In 2025 11th International Conference on Automation, Robotics, and Applications.
• Avioz, D., Linker, R., Raveh, E., Baram, S., & Paz-Kagan, T. (2025). Multi-scale remote sensing for sustainable citrus farming: Predicting canopy nitrogen content using UAV-satellite data fusion. Smart Agricultural Technology, 11, 100906.
• Declaro, A., & Kanae, S. (2024). Enhancing surface water monitoring through multi-satellite data-fusion of Landsat-8/9, Sentinel-2, and Sentinel-1 SAR. Remote Sensing, 16(17), 3329.
• Dong, Y., & Yuan, H. (2026). China's 1 km daily surface soil moisture fusion dataset (2000–2025) based on explainable machine learning. Advances in Atmospheric Sciences, 43(7), 1317–1334.
• Durlevic, U., Ilic, V., & Valjarevic, A. (2025). Wildfire susceptibility mapping using deep learning and machine learning models based on multi-sensor satellite data fusion: A case study of Serbia. Fire, 8(10), 407.
• Ekolama, S. M. (2026). AI-enhanced multi-constellation satellite fusion for centimeter-level positioning in signal-degraded environments. Journal of Optoelectronics and Communication, 8(3), 1–14.
• Fawakherji, M., & Hashemi-Beni, L. (2025). Flood detection and mapping through multi-resolution sensor fusion: Integrating UAV optical imagery and satellite SAR data. Geomatics, Natural Hazards and Risk, 16(1), 2493225.
• Feng, X., Qiu, M., Wang, T., Yao, X., Cong, H., & Zhang, Y. (2025). Noise-adaptive GNSS/INS fusion positioning for autonomous driving in complex environments. Vehicles, 7(3), 77.
• Guo, X., Hu, J., Hu, J., Bao, H., & Zhang, G. (2025). SGFormer: Satellite-ground fusion for 3D semantic scene completion. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
• Hashima, S., Gendia, A., Hatano, K., Muta, O., Nada, M. S., & Mohamed, E. M. (2025). Next-gen UAV-satellite communications: AI innovations and future prospects. IEEE Open Journal of Vehicular Technology, 6, 1998–2036.
• Jarraya, I., Al-Batati, A. S., Kadri, M. B., Abdelkader, M., Ammar, A., Boulila, W., & Koubaa, A. (2025). GNSS-denied unmanned aerial vehicle navigation: Analyzing computational complexity, sensor fusion, and localization methodologies. Satellite Navigation, 6(1), 1–32.
• Kugusheva, A., Bull, H., Moschos, E., Ioannou, A., Le Vu, B., & Stegner, A. (2024). Ocean satellite data fusion for high-resolution surface current maps. Remote Sensing, 16(7), 1182.
• Sharma, S. (2026). NGPS: GPS-denied aerial geo-localization and 2.5D reconstruction via deep satellite image matching and multi-rate sensor fusion. In 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
• Susanti, V., Mahmud, M. S. A., & Saputra, R. P. (2026). Sensor fusion technology advancement in GPS-aided localization for autonomous mobile robots: A comprehensive survey. Jurnal Teknologi (Sciences & Engineering), 88(1), 165–189.
• Ušinskis, V., Nowicki, M., Dziedzickis, A., & Bučinskas, V. (2025). Sensor-fusion based navigation for autonomous mobile robot. Sensors, 25(4), 1248.
• Vitale, A., & Lamonaca, F. (2025). Advancing built-up area monitoring through multi-temporal satellite data fusion and machine learning-based geospatial analysis. Remote Sensing, 17(11), 1830.
• Wang, G., Wan, G., Su, Z., Wang, Y., Jia, Y., Li, G., & Liang, S. (2025a). High-performance on-orbit intelligent computing and real-time services for remote sensing satellites based on large-scale computing power in space. IEEE Access, 13, 12890–12909.
• Wang, L., Wang, P., Zhang, Y., Wang, Y., & Chen, B. (2025c). Multi-source collaborative positioning for ultra-wide swath rotating scanning satellite imagery based on multi-source data fusion. Sensors, 25(3), 850.
• Wang, M., Nardin, A., Ma, R., Wang, R., Dovis, F., Garello, R., & Liu, G. (2025b). Integrated communication and navigation based on LEO satellite networks: A survey. IEEE Access, 13, 46232–46258.
• Wang, S., & Ahmad, N. S. (2025). A comprehensive review on sensor fusion techniques for localization of a dynamic target in GPS-denied environments. IEEE Access, 13, 2246–2280.
• Yuan, Y., Yu, F., & Zong, H. (2025). Multisensor integrated autonomous navigation based on intelligent information fusion. Journal of Spacecraft and Rockets, 62(4), 1–11.
• Zheng, B., et al. (2024). An autonomous navigation method for planetary rover based on multi-modal fusion and multi-factor graph optimization. Journal of Physics: Conference Series, 2762, 012002.