Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

Arwin Datumaya Wahyudi Sumari, Dimas Eka Adinandra, Arie Rachmad Syulistyo, Sandra Lovrencic


A hostile or unfriendly aircraft will mostly fly at low-level altitude or hide behind natural obstacles to avoid Radar detection. One of the ways to detect and recognize while at the same time identifying such aircraft is to perform air observation from the ground. A technique called Visual Aircraft Recognition (VACR) has been practiced in training soldiers to recognize and find an incoming aircraft from a distance using binoculars. Remembering so many types of aircraft have their challenge. To ease the task, we have designed and developed an intelligent military aircraft recognition and identification system using the combination of Back Propagation Neural Networks (BPNN) and Information Fusion to speed up the recognition and identification. We use 13 aircraft features fused into five primary ones as the inputs to the BPNN for the recognition, while the identification uses Hamming Distance to the recognition results. With 155 data consisting of 85 military aircraft and helicopters and 70 civilian aircraft and helicopters and applying the 80:20 scheme for the training and test data, our system can obtain 95.33% and 87% accuracy at the training phase and the test phase. It also succeeds in recognizing and identifying a new military aircraft that is not in the dataset, while the Information Fusion can speed up the recognition and identification by up to 6 seconds. This impacts the acceleration of aircraft recognition and identification.


Artificial intelligence; backpropagation network; hamming distance; information fusion; military aircraft; recognition and identification.

Full Text:



T. Nallusamy and P. Balaji, “Optimization of NOE Flights Sensors and Their Integration,†in Advances in Human and Machine Navigation Systems, IntechOpen, 2019. doi: 10.5772/intechopen.86139.

Avionics Department, Electronic Warfare and Radar Systems Engineering Handbook 2013 NAWCWD TP 8347 Fourth Edition. Point Mugu: Naval Air Warfare Center Weapons Division, 2013. Accessed: Jan. 23, 2022. [Online]. Available:

C. Bowman, M. Deyong, and T. Eskridge, “Role of Neural Networks for Avionics,†1995. [Online]. Available:

P. Scharre, “Military Applications of Artificial Intelligence: Potential Risks to International Peace and Security,†2019.

I. Szabadföldi, “Artificial Intelligence in Military Application – Opportunities and Challenges,†Land Forces Academy Review, vol. 26, no. 2, pp. 157–165, Jun. 2021, doi: 10.2478/raft-2021-0022.

F. E. Morgan et al., Military Applications of Artificial Intelligence: Ethical Concerns in an Uncertain World. 2020.

Christian H. Heller, “Near-Term Applications of Artificial Intelligence,†Naval War College Review, vol. 72, no. 4, pp. 73–100, 2019, doi: 10.2307/26775520.

Zachary Davis, “Artificial Intelligence on the Battlefield,†PRISM, vol. 8, no. 2, pp. 114–131, 2019, doi: 10.2307/26803234.

S. Pradeep and Y. K. Sharma, “Deep Learning based Real Time Object Recognition for Security in Air Defense,†in 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), 2019, pp. 295–298.

Z. Ozkan, E. Bayhan, M. Namdar, and A. Basgumus, “Object Detection and Recognition of Unmanned Aerial Vehicles Using Raspberry Pi Platform,†in 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2021, pp. 467–472. doi: 10.1109/ISMSIT52890.2021.9604698.

W. Budiharto, E. Irwansyah, J. S. Suroso, and A. A. S. Gunawan, “Design of object tracking for military robot using pid controller and computer vision,†ICIC Express Letters, vol. 14, no. 3, pp. 289–294, 2020, doi: 10.24507/icicel.14.03.289.

Z. Yang et al., “Deep transfer learning for military object recognition under small training set condition,†Neural Computing and Applications, vol. 31, no. 10, pp. 6469–6478, 2019, doi: 10.1007/s00521-018-3468-3.

Guo Qian, Wang Haipeng, and Xu Feng., “Research Progress on Aircraft Detection and Recognition in SAR Imagery,†Journal of Radars, vol. 9, no. 3, pp. 497–813, 2020, doi:

Y. Li, Y. Chang, Y. Ye, X. Zou, S. Zhong, and L. Yan, “Category-Aware Aircraft Landmark Detection,†IEEE Signal Processing Letters, vol. 28, pp. 61–65, 2021, doi: 10.1109/LSP.2020.3045623.

L. Chen, L. Zhou, and J. Liu, “Aircraft recognition from remote sensing images based on machine vision,†Journal of Information Processing Systems, vol. 16, no. 4, pp. 795–808, Aug. 2020, doi: 10.3745/JIPS.02.0136.

D. Grosgeorge, M. Arbelot, A. Goupilleau, T. Ceillier, and R. Allioux, “Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images,†in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 276–279. doi: 10.1109/IGARSS39084.2020.9323338.

Q. Liu, X. Xiang, Y. Wang, Z. Luo, and F. Fang, “Aircraft detection in remote sensing image based on corner clustering and deep learning,†Engineering Applications of Artificial Intelligence, vol. 87, Jan. 2020, doi: 10.1016/j.engappai.2019.103333.

L. Tao, T. Hong, Y. Guo, H. Chen, and J. Zhang, “Drone identification based on CenterNet-TensorRT,†in 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2020, pp. 1–5. doi: 10.1109/BMSB49480.2020.9379645.

F. Azam, A. Rizvi, W. Z. Khan, M. Y. Aalsalem, H. Yu, and Y. B. Zikria, “Aircraft Classification Based on PCA and Feature Fusion Techniques in Convolutional Neural Network,†IEEE Access, vol. 9, pp. 161683–161694, 2021, doi: 10.1109/ACCESS.2021.3132062.

J. Liu, J. Gangwu, X. Wang, B. Xu, and P. Yu, “Feature Extraction and Identification of Military Aircraft Based on Remote Sensing Image,†in PervasiveHealth: Pervasive Computing Technologies for Healthcare, Dec. 2020, pp. 128–133. doi: 10.1145/3445815.3445837.

B. Zhao, W. Tang, Y. Pan, Y. Han, and W. Wang, “Aircraft type recognition in remote sensing images: Bilinear discriminative extreme learning machine framework,†Electronics (Switzerland), vol. 10, no. 17, Sep. 2021, doi: 10.3390/electronics10172046.

W. T. Alshaibani et al., “Airplane Type Identification Based on Mask RCNN and Drone Images,†, 2021, Accessed: Jan. 23, 2022. [Online]. Available:

Y. Choi, S. Seol, and I. Choi, “Radar target identification using a DTV-based passive radar in South Korea,†in 2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL), 2017, pp. 892–895. doi: 10.1109/PIERS-FALL.2017.8293260.

Arwin Datumaya Wahyudi Sumari, Afifah Millatina Nugraheni, and Yoppy Yunhasnawa, “A Novel Approach for Recognition and Identification of Low-Level Flight Military Aircraft using Naive Bayes Classifier and Information Fusion,†International Journal of Artificial Intelligence Research (IJAIR), vol. 6, no. 2, p. In Press, Jan. 2022.

Headquarters Department of the Army, “TC 3-01.80 Visual Aircraft Recognition Visual Aircraft Recognition,†2017. [Online]. Available:

P. Svenmarck, L. Luotsinen, M. Nilsson, and J. Schubert, “Possibilities and Challenges for Artificial Intelligence in Military Applications,†2018.

M. A. Abuzneid and A. Mahmood, “Enhanced human face recognition using LBPH descriptor, multi-KNN, and back-propagation neural network,†IEEE Access, vol. 6, pp. 20641–20651, Apr. 2018, doi: 10.1109/ACCESS.2018.2825310.

P. Li and Q. Zhang, “Face Recognition Algorithm Comparison based on Backpropagation Neural Network,†in Journal of Physics: Conference Series, Apr. 2021, vol. 1865, no. 4. doi: 10.1088/1742-6596/1865/4/042058.

W. Wang, H. Liu, W. Lin, Y. Chen, and J. A. Yang, “Investigation on Works and Military Applications of Artificial Intelligence,†IEEE Access, vol. 8, pp. 131614–131625, 2020, doi: 10.1109/ACCESS.2020.3009840.

S. Anam, “Rainfall prediction using backpropagation algorithm optimized by Broyden-Fletcher-Goldfarb-Shanno algorithm,†in IOP Conference Series: Materials Science and Engineering, Aug. 2019, vol. 567, no. 1. doi: 10.1088/1757-899X/567/1/012008.

U. Andayani, R. F. Rahmat, M. F. Syahputra, A. Lubis, and B. Siregar, “Identification of Lung Cancer Using Backpropagation Neural Network,†in Journal of Physics: Conference Series, Dec. 2019, vol. 1361, no. 1. doi: 10.1088/1742-6596/1361/1/012060.

U. Andayani et al., “Identification of Retinoblastoma Using Backpropagation Neural Network,†in Journal of Physics: Conference Series, Jul. 2019, vol. 1235, no. 1. doi: 10.1088/1742-6596/1235/1/012093.

N. Sevani, I. A. Soenandi, and F. Saputra, “Implementation of backpropagation artificial neural network for early detection of vitamin and mineral deficiency,†in IOP Conference Series: Materials Science and Engineering, May 2020, vol. 847, no. 1. doi: 10.1088/1757-899X/847/1/012043.

C. Hayat, I. A. Soenandi, S. Limong, and J. Kurnia, “Modeling of prediction bandwidth density with backpropagation neural network (BPNN) methods,†in IOP Conference Series: Materials Science and Engineering, Jul. 2020, vol. 852, no. 1. doi: 10.1088/1757-899X/852/1/012127.

B. Zhao, W. Shi, D. Sun, J. Li, F. Li, and J. Tan, “Extending velocity sensor bandwidth by compensating temperature dependency based on BP neural network,†IEEE Access, vol. 7, pp. 154889–154898, 2019, doi: 10.1109/ACCESS.2019.2948261.

Q. Zhang, Y. Guo, and Z. Song, “Dynamic Curve Fitting and BP Neural Network with Feature Extraction for Mobile Specific Emitter Identification,†IEEE Access, vol. 9, pp. 33897–33910, 2021, doi: 10.1109/ACCESS.2021.3060794.

A. G. Pertiwi, A. P. Wibawa, and U. Pujianto, “Partus referral classification using backpropagation neural network,†in Journal of Physics: Conference Series, Apr. 2019, vol. 1193, no. 1. doi: 10.1088/1742-6596/1193/1/012010.

R. Andrian, B. Hermanto, and R. Kamil, “The Implementation of Backpropagation Artificial Neural Network for Recognition of Batik Lampung Motive,†in Journal of Physics: Conference Series, Oct. 2019, vol. 1338, no. 1. doi: 10.1088/1742-6596/1338/1/012062.

X. Zhang et al., “A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network,†IEEE Access, vol. 8, pp. 71708–71720, 2020, doi: 10.1109/ACCESS.2020.2987717.

H. Bian et al., “Multiple kinds of pesticides detection based on back-propagation neural network analysis of fluorescence spectra,†IEEE Photonics Journal, vol. 12, no. 2, Apr. 2020, doi: 10.1109/JPHOT.2020.2973653.

X. Shen, Y. Zheng, and R. Zhang, “A Hybrid Forecasting Model for the Velocity of Hybrid Robotic Fish Based on Back-Propagation Neural Network with Genetic Algorithm Optimization,†IEEE Access, vol. 8, pp. 111731–111741, 2020, doi: 10.1109/ACCESS.2020.3002928.

Johanes Roisa Prabowo, Rukun Santoso, and Hasbi Yasin, “Implementasi Jaringan Syaraf Tiruan Backpropagation Dengan Algoritma Conjugate Gradient untuk Klasifikasi Kondisi Rumah,†Jurnal Gaussian, vol. 9, no. 1, pp. 41–49, 2020, Accessed: Jan. 23, 2022. [Online]. Available:

A. S. Ahmad and A. D. W. Sumari, “Cognitive artificial intelligence: Brain-inspired intelligent computation in artificial intelligence,†in Proceedings of Computing Conference 2017, 2018, vol. 2018-Janua. doi: 10.1109/SAI.2017.8252094.

M. A. Becerra, C. Tobón, A. E. Castro-Ospina, and D. H. Peluffo-Ordóñez, “Information quality assessment for data fusion systems,†Data, vol. 6, no. 6, Jun. 2021, doi: 10.3390/data6060060.

A. D. W. Sumari and Adang Suwandi Ahmad, “Cognitive Artificial Intelligence: Concept and Applications for Humankind,†in Intelligent System, A. S. A. E.-C. Wongchoosuk, Ed. Rijeka: IntechOpen, 2018, pp. 475–502. doi: 10.5772/intechopen.72764.

A. Laksamana, “Sistem Pakar Mendiagnosa Penyakit Kolera Menerapkan Metode Hybrid Case Based,†2020.

C. G. Hadis, R. Saptono, and A. Azis, “Tourism Recomendation System By Using Positive Negative Apriori and Binary Hamming Distance Algorithm,†ITSMART: Jurnal Ilmiah Teknologi dan Informasi, vol. 8, no. 2, pp. 58–64, 2019.

M. D. Samirbhai, S. Chen, and K. S. Low, “A Hamming Distance and Spearman Correlation Based Star Identification Algorithm,†IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 1, pp. 17–30, 2019, doi: 10.1109/TAES.2018.2845198.

X. Li et al., “Image Retrieval Using a Deep Attention-Based Hash,†IEEE Access, vol. 8, pp. 142229–142242, 2020, doi: 10.1109/ACCESS.2020.3011102.

M. Ahmad, M. Abdullah, H. Moon, S. J. Yoo, and D. Han, “Image classification based on automatic neural architecture search using binary crow search algorithm,†IEEE Access, vol. 8, pp. 189891–189912, 2020, doi: 10.1109/ACCESS.2020.3031599.

K. bin Park and K. S. Chung, “Iterative Pseudo-Soft-Reliability-Based Majority-Logic Decoding for NAND Flash Memory,†IEEE Access, vol. 9, pp. 74531–74538, 2021, doi: 10.1109/ACCESS.2021.3079939.

W. Li, H. Liu, Y. Wang, Z. Li, Y. Jia, and G. Gui, “Deep Learning-Based Classification Methods for Remote Sensing Images in Urban Built-Up Areas,†IEEE Access, vol. 7, pp. 36274–36284, 2019, doi: 10.1109/ACCESS.2019.2903127.

A. M. Ali, E. Uzundurukan, and A. Kara, “Assessment of Features and Classifiers for Bluetooth RF Fingerprinting,†IEEE Access, vol. 7, pp. 50524–50535, 2019, doi: 10.1109/ACCESS.2019.2911452.

M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, and R. Budiarto, “Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking,†IEEE Access, vol. 8, pp. 90847–90861, 2020, doi: 10.1109/ACCESS.2020.2994222.

H. B. Bae, D. Pak, and S. Lee, “Dog nose-print identification using deep neural networks,†IEEE Access, vol. 9, pp. 49141–49153, 2021, doi: 10.1109/ACCESS.2021.3068517.

Marthin Luter Laia and Yudi Setyawan, “Perbandingan Hasil Klasifikasi Curah Hujan Menggunakan Metode SVM dan NBC,†Jurnal Statistika Industri dan Komputasi, vol. 5, no. 2, pp. 51–61, 2020.

R. Rakhman Wahid, F. Tri Anggraeni, and B. Nugroho, “Implementasi Metode Extreme Learning Machine untuk Klasifikasi Tumor Otak pada Citra Magnetic Resonance Imaging,†2020.

W. Castro, J. Oblitas, M. De-La-Torre, C. Cotrina, K. Bazan, and H. Avila-George, “Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces,†IEEE Access, vol. 7, pp. 27389–27400, 2019, doi: 10.1109/ACCESS.2019.2898223.

A. U. Haq et al., “Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings,†IEEE Access, vol. 7, pp. 37718–37734, 2019, doi: 10.1109/ACCESS.2019.2906350.

T. Wong and P. Yeh, “Reliable Accuracy Estimates from k-Fold Cross Validation,†IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 8, pp. 1586–1594, 2020, doi: 10.1109/TKDE.2019.2912815.

M. Rangga, A. Nasution, and M. Hayaty, “Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter,†Jurnal Informatika, vol. 6, no. 2, pp. 212–218, 2019, [Online]. Available:

C. Qi, J. Diao, and L. Qiu, “On Estimating Model in Feature Selection with Cross-Validation,†IEEE Access, vol. 7, pp. 33454–33463, 2019, doi: 10.1109/ACCESS.2019.2892062.

V. K. Gupta, A. Gupta, D. Kumar, and A. Sardana, “Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model,†Big Data Mining and Analytics, vol. 4, no. 2, pp. 116–123, Jun. 2021, doi: 10.26599/BDMA.2020.9020016.

P. A. Pattanaik, M. Mittal, and M. Z. Khan, “Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images,†IEEE Access, vol. 8, pp. 94936–94946, 2020, doi: 10.1109/ACCESS.2020.2996022.

N. Li, F. He, W. Ma, R. Wang, and X. Zhang, “Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm,†IEEE Access, vol. 8, pp. 68874–68882, 2020, doi: 10.1109/ACCESS.2020.2985381.

E. T. Institute, “Aircraft Performance Database,†EUROCONTROL Training Institute, Mar. 04, 2021. (accessed Mar. 04, 2021).

A. D. W. Sumari, A. I. Wuryandari, M. Darusman, and N. I. Utama, “The Performance of Supervised and Unsupervised Neural Networks in Performing Aircraft Identification Tasks,†in Seminar Radar Nasional III, 2009, pp. 16–22.

E. F. Nakamura, A. A. F. Loureiro, and A. C. Frery, “Information fusion for wireless sensor networks: Methods, models, and classifications,†ACM Computing Surveys, vol. 39, no. 3, Sep. 2007, doi: 10.1145/1267070.1267073.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development