An Adaptive Neuro-Fuzzy-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

  • Ângelo de C. Paulino IEAv
  • Elcio H. Shiguemori IEAv
  • Lamartine N. F. Guimarães IEAv


The world trend in employing UAVs and drones is remarkable. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. However, they depend on positioning systems that may be fallible. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming at safe navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. Nonetheless, its high computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a Data Fusion application based on Computational Intelligence – Adaptive-Network-Based Fuzzy Inference System (ANFIS) – which is able to improve the accuracy of such position estimation systems.


Agência Força Aérea (2014). Hermes 900 participa de treinamento em Campo Grande., [accessed on Feb 17].

Agência Força Aérea (2016). Esquadrão Hórus participa da vigilância aérea nos Jogos Olímpicos., [accessed on Feb 17].

Agência Força Aérea (2017a). Dimensão 22 é o novo DNA da FAB., [accessed on Feb 21].

Agência Força Aérea (2017b). Veja o trabalho dos esquadrões especializados em reconhecimento aéreo., [accessed on Feb 21].

Al-Hmouz, A., Jun Shen, Al-Hmouz, R. and Jun Yan (2012). Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning. IEEE Transactions on Learning Technologies, v. 5, n. 3, p. 226–237.

Al-Kaff, A., Martín, D., García, F., Escalera, A. de La and María Armingol, J. (1 feb 2018). Survey of computer vision algorithms and applications for unmanned aerial vehicles. Expert Systems with Applications, v. 92, p. 447–463.

Amr, S. and Qin, S. (2017). Robust adaptive flight controller for UAV systems. In Proceedings - 2017 4th International Conference on Information Science and Control Engineering, ICISCE 2017.

Braga, J. R. G., D. C. Velho, H. F. and Shiguemori, E. H. (2015). Estimation of UAV position using LiDAR images for autonomous navigation over the ocean. In 2015 9th International Conference on Sensing Technology (ICST).

BRASIL. Ministério da Defesa. Comando da Aeronáutica. Planejamento. (2014). DCA 11-45 - Concepção Estratégica Força Aérea 100.

Cardoso, R. (2003). Integração de Sensores Via Filtro de Kalman. ITA.

Castanedo, F. (2013). A Review of Data Fusion Techniques. The Scientific World Journal, v. 2013, p. 1–19.

Centro de Comunicação Social da Aeronáutica (2018). Dimensão 22., [accessed on Feb 21].

Conte, G. and Doherty, P. (2008). An Integrated UAV Navigation System Based on Aerial Image Matching. In 2008 IEEE Aerospace Conference.

Conte, G. and Doherty, P. (7 dec 2009). Vision-Based Unmanned Aerial Vehicle Navigation Using Geo-Referenced Information. EURASIP Journal on Advances in Signal Processing, v. 2009, n. 1, p. 387308.

Cook, K. L. B. (2007). The Silent Force Multiplier: The History and Role of UAVs in Warfare. In 2007 IEEE Aerospace Conference.

Da Silva, W. (2016). Navegação Autônoma de VANT em período noturno com imagens Infravermelho Termal. INPE.

Davis, L., McNerney, M. J., Chow, J., et al. (2014). Armed and Dangerous? UAVs and U.S. Security.

Faria, L., Silvestre, C. M. and Correia, M. A. F. (2016). GPS-Dependent Systems:

Vulnerabilities to Electromagnetic Attacks. Jour. Aer. Tech. Man., v. 8, p. 423–430.

Glade, D. (2000). Unmanned Aerial Vehicles: Implications for Military Operations. Occasional Paper. Center for Strategy and Technology, Air War College., n. 16, p. 38.

Jang, J.-S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, v. 23, n. 3, p. 665–685.

Kamarian, S., Yas, M. H., Pourasghar, A. and Daghagh, M. (2014). Application of firefly algorithm and ANFIS for optimisation of functionally graded beams. Journal of Experimental and Theoretical Artificial Intelligence, v. 26, n. 2, p. 197–209.

Lacerda, M. G., Paulino, Â. C., Damião, Á. J., Shiguemori, E. H. and Lamartine N. F. Guimarães (2017). O Emprego de Árvore de Decisão para a Identificação e Classificação de ZLs e ZPHs em Imagens Obtidas por ARPs de Pequeno Porte. In XIX Simpósio de Aplicações Operacionais em Áreas de Defesa. .

Lacerda, M., Paulino, Â. C., Shiguemori, E., et al. (2018). The Employment of Unmanned Aircraft Systems for Identification and Classification of Helicopter Landing Zones and Airdrop Zones in Calamity Situations. In ICUAS 2018: International Conference on Unmanned Aircraft Systems. . World Academy of Science, Engineering and Technology.

Liggins, M. E., Hall, D. L. and Llinas, J. (2009). Handbook of multisensor data fusion : theory and practice. 2. ed. Boca Raton, FL: CRC Press.

Luo, C., McClean, S. I., Parr, G., Teacy, L. and De Nardi, R. (jul 2013). UAV Position Estimation and Collision Avoidance Using the Extended Kalman Filter. IEEE Transactions on Vehicular Technology, v. 62, n. 6, p. 2749–2762.

Nerurkar, P., Shirke, A., Chandane, M. and Bhirud, S. (1 jan 2018). Empirical Analysis of Data Clustering Algorithms. Procedia Computer Science, v. 125, p. 770–779.

Oh, S.-M. (sep 2010). Multisensor fusion for autonomous UAV navigation based on the Unscented Kalman Filter with Sequential Measurement Updates. In 2010 IEEE Conference on Multisensor Fusion and Integration. . IEEE.

Silva Filho, P. F. F. (2016). Automatic Landmark Recognition in aerial images for the autonomous naviga-tion system of Unmanned Aerial Vehicles. ITA.

Stamatescu, G., Stamatescu, I., Popescu, D. and Mateescu, C. (jun 2015). Sensor fusion method for altitude estimation in mini-UAV applications. In 2015 7th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). . IEEE.

Tsoukalas, L. H. and Uhrig, R. E. (1997). Fuzzy and Neural Approaches in Engineering. 1st. ed. New York, NY, USA: John Wiley & Sons, Inc.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, v. 8, n. 3, p. 338–353.

Zamani Sabzi, H., Humberson, D., Abudu, S. and King, J. P. (2016). Optimization of adaptive fuzzy logic controller using novel combined evolutionary algorithms, and its application in Diez Lagos flood controlling system, USA. Exp. Sys. with/ App., v. 43, p. 154–164.

PAULINO, Ângelo de C.; SHIGUEMORI, Elcio H. ; GUIMARÃES, Lamartine N. F.. An Adaptive Neuro-Fuzzy-based Multisensor Data Fusion applied to real-time UAV autonomous navigation. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 847-858. ISSN 2763-9061. DOI: