Geospatial Intelligent Analysis to Support Indonesian Airspace Defense

Authors

  • Ilvan Dino Rahmandhala Sensing Technology Study Program, Faculty of Defense Technology and Science, Indonesian Defense University
  • Asep Adang Supriyadi Sensing Technology Study Program, Faculty of Defense Technology and Science, Indonesian Defense University
  • Yosef Prihanto National Research and Innovation Agency
  • Muhammad Samingan Defense Economics Study Program, Faculty of Defense Management, Indonesian Defense University
  • Rudy Agus Gemilang Gultom Sensing Technology Study Program, Faculty of Defense Technology and Science, Indonesian Defense University

DOI:

https://doi.org/10.55927/fjst.v3i9.11555

Keywords:

GEOINT, GeoAI, Air Defense, Geospatial Technology, Geospatial Infrastructure

Abstract

This study highlights the importance of Geospatial Intelligence (GEOINT) analysis in supporting Indonesia's airspace defense. In modern military operations, especially air defense, the role of GEOINT is crucial as it enables real-time detection, mapping, planning, surveillance, and analysis of aerial threats. This research aims to analyze the role and potential of GEOINT in supporting Indonesia's airspace defense and to provide strategic recommendations for strengthening air defense. The findings of this study are expected to guide policymakers in enhancing the effectiveness and efficiency of air defense through the use of GEOINT, as well as supporting the development of necessary geospatial infrastructure. This study employs the SEIM (Sensor Effector Information Management) hierarchical approach to integrate various data sources and analytical tools to enhance detection, mapping, planning, and surveillance capabilities in the context of air defense. The study finds that GEOINT enables deeper tactical and strategic situation analysis and can optimize weapon assignments to increase efficiency in military conflicts. To effectively implement GEOINT, investments in technology, data infrastructure development, personnel training, and international cooperation are necessary. The implementation of remote sensing satellites capable of providing real-time, high-resolution imagery will enhance threat detection and monitoring capabilities.

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References

R. Pierdicca and M. Paolanti, “GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data,” Geoscientific Instrumentation, Methods and Data Systems, vol. 11, no. 1. 2022. doi: 10.5194/gi-11-195-2022.

K. Gao, H. Xiao, L. Qu, and S. Wang, “Optimal interception strategy of air defence missile system considering multiple targets and phases,” Proc Inst Mech Eng O J Risk Reliab, vol. 236, no. 1, 2022, doi: 10.1177/1748006X211022111.

A. Naseem and Y. Ahmad, “Critical Success Factors for Neutralization of Airborne Threats,” Sage Open, vol. 10, no. 3, 2020, doi: 10.1177/2158244020963066.

J. Dangermond and M. F. Goodchild, “Building geospatial infrastructure,” Geo-Spatial Information Science, vol. 23, no. 1, 2020, doi: 10.1080/10095020.2019.1698274.

J. Smith and M. Jones, “Geospatial Intelligence in Modern Defense Systems,” Journal of Military Technology, vol. 45, no. 3, pp. 123–145, 2020.

A. Brown and L. Green, “The Role of Remote Sensing in Air Defense,” International Journal of Geospatial Science, vol. 12, no. 4, pp. 215–230, 2019.

R. Clarke and T. Peterson, “Integrating ADS-B Data in Military Surveillance Systems,” Journal of Aviation Safety, vol. 27, no. 2, pp. 98–110, 2018.

R. Setiawan, “Memahami Class Diagram Lebih Baik,” Dicoding. Accessed: Jun. 10, 2024. [Online]. Available: https://www.dicoding.com

P. Touzopoulos and K. C. Zikidis, “Physical Optics Radar Cross Section predictions for an anti-ship cruise missile,” Journal of Defense Modeling and Simulation, 2021, doi: 10.1177/15485129211033039.

M. Guanglei, Z. Runnan, W. Biao, Z. Mingzhe, W. Yu, and L. Xiao, “Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat,” International Journal of Aerospace Engineering, vol. 2021, 2021, doi: 10.1155/2021/9558838.

Q. Xu, J. Ge, T. Yang, and X. Sun, “A trajectory design method for coupling aircraft radar cross-section characteristics,” Aerosp Sci Technol, vol. 98, 2020, doi: 10.1016/j.ast.2019.105653.

V. P. Riabukha, “Radar Surveillance of Unmanned Aerial Vehicles (Review),” Radioelectronics and Communications Systems, vol. 63, no. 11, 2020, doi: 10.3103/S0735272720110011.

D. Radočaj, J. Obhođaš, M. Jurišić, and M. Gašparović, “Global open data remote sensing satellite missions for land monitoring and conservation: A review,” Land, vol. 9, no. 11. 2020. doi: 10.3390/land9110402.

P. Partsinevelos and H. Su, “Special Section Guest Editorial: Unmanned Systems and Satellites: a Synergy for Added-value Possibilities,” J Appl Remote Sens, vol. 16, no. 02, 2022, doi: 10.1117/1.jrs.16.022201.

S. Dogru and L. Marques, “Pursuing Drones with Drones Using Millimeter Wave Radar,” IEEE Robot Autom Lett, vol. 5, no. 3, 2020, doi: 10.1109/LRA.2020.2990605.

F. S. Mobley, A. T. Wall, and S. C. Campbell, “Translating jet noise measurements to near-field level maps with nearest neighbor bilinear smoothing interpolation,” J Acoust Soc Am, vol. 150, no. 2, 2021, doi: 10.1121/10.0005737.

L. Miccinesi et al., “Geo-Referenced Mapping through an Anti-Collision Radar Aboard an Unmanned Aerial System,” Drones, vol. 6, no. 3. 2022. doi: 10.3390/drones6030072.

S. K. Pundir and R. D. Garg, “Development of mapping techniques for off road trafficability to support military operation,” Spatial Information Research, vol. 28, no. 4, 2020, doi: 10.1007/s41324-019-00310-z.

A. Shukla and K. Jain, “Automatic extraction of urban land information from unmanned aerial vehicle (UAV) data,” Earth Sci Inform, vol. 13, no. 4, 2020, doi: 10.1007/s12145-020-00498-x.

M. Mazzoleni, P. Paron, A. Reali, D. Juizo, J. Manane, and L. Brandimarte, “Testing UAV-derived topography for hydraulic modelling in a tropical environment,” Natural Hazards, vol. 103, no. 1, 2020, doi: 10.1007/s11069-020-03963-4.

M. Liu et al., “Location Parameter Estimation of Moving Aerial Target in Space-Air-Ground-Integrated Networks-Based IoV,” IEEE Internet Things J, vol. 9, no. 8, 2022, doi: 10.1109/JIOT.2021.3071927.

D. S. Summers, M. J. Robbins, and B. J. Lunday, “An approximate dynamic programming approach for comparing firing policies in a networked air defense environment,” Comput Oper Res, vol. 117, 2020, doi: 10.1016/j.cor.2020.104890.

L. Babel, “Coordinated flight path planning for a fleet of missiles in high-risk areas,” Robotica, vol. 41, no. 5, 2023, doi: 10.1017/S0263574722001886.

W. Li, “Big Data Precision Marketing Approach under IoT Cloud Platform Information Mining,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/4828108.

P. Praveen, C. H. J. Babu, and B. Rama, “Big data environment for geospatial data analysis,” in Proceedings of the International Conference on Communication and Electronics Systems, ICCES 2016, 2016. doi: 10.1109/CESYS.2016.7889816.

P. Boccardo and G. Gentili, “HIGH RESOLUTION DSM AND CLASSIFIED VOLUMETRIC GENERATION: AN OPERATIONAL APPROACH TO THE IMPROVEMENT OF GEOSPATIAL INTELLIGENCE,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVIII-4/W19, 2012, doi: 10.5194/isprsarchives-xxxviii-4-w19-45-2011.

S. Bhattacharya, B. Czejdo, and R. Malhotra, “Geospatial intelligence as a context for computing education (abstract only),” 2013. doi: 10.1145/2445196.2445450.

J. R. Eyre et al., “Assimilation of satellite data in numerical weather prediction. Part II: Recent years,” Quarterly Journal of the Royal Meteorological Society, vol. 148, no. 743. 2022. doi: 10.1002/qj.4228.

M. Solla, C. Casqueiro, and I. del Cuvillo, “Approach to generate 3D-printed terrain models using free software and open data sources: Application to military planning,” Computer Applications in Engineering Education, vol. 28, no. 3, 2020, doi: 10.1002/cae.22211.

E. Taghavi et al., “Geo-registration and Geo-location Using Two Airborne Video Sensors,” IEEE Trans Aerosp Electron Syst, vol. 56, no. 4, 2020, doi: 10.1109/TAES.2020.2995439.

M. Khalaf-Allah, “Emitter location with azimuth and elevation measurements using a single aerial platform for electronic support missions,” Sensors, vol. 21, no. 12, 2021, doi: 10.3390/s21123946.

A. Elgamoudi, H. Benzerrouk, G. A. Elango, and R. Landry, “A survey for recent techniques and algorithms of geolocation and target tracking in wireless and satellite systems,” Applied Sciences (Switzerland), vol. 11, no. 13, 2021, doi: 10.3390/app11136079.

M. L. Laouira, A. Abdelli, J. Ben Othman, and H. Kim, “An Efficient WSN Based Solution for Border Surveillance,” IEEE Transactions on Sustainable Computing, vol. 6, no. 1, 2021, doi: 10.1109/TSUSC.2019.2904855.

R. Adade, A. M. Aibinu, B. Ekumah, and J. Asaana, “Unmanned Aerial Vehicle (UAV) applications in coastal zone management—a review,” Environmental Monitoring and Assessment, vol. 193, no. 3. 2021. doi: 10.1007/s10661-021-08949-8.

M. Nijim, “Multitasking intelligent surveillance and first response system,” in 2016 IEEE Symposium on Technologies for Homeland Security, HST 2016, 2016. doi: 10.1109/THS.2016.7568935.

P. K. Barik, S. Shah, K. Shah, A. Modi, and H. Devisha, “UAV-Assisted Surveillance Using Machine Learning,” in PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing, 2022. doi: 10.1109/PDGC56933.2022.10053282.

X. G. Shan and J. Zhuang, “A game-theoretic approach to modeling attacks and defenses of smart grids at three levels,” Reliab Eng Syst Saf, vol. 195, 2020, doi: 10.1016/j.ress.2019.106683.

R. Setiawan, “Bagaimana Cara Membuat ERD dan Contohnya,” Dicoding. Accessed: Jun. 10, 2024. [Online]. Available: https://www.dicoding.com/

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Published

2024-10-01

How to Cite

Rahmandhala, I. D., Supriyadi, A. A. ., Prihanto, Y. ., Samingan, M. ., & Gultom, R. A. G. . (2024). Geospatial Intelligent Analysis to Support Indonesian Airspace Defense. Formosa Journal of Science and Technology, 3(9), 2149–2168. https://doi.org/10.55927/fjst.v3i9.11555