Nowadays, there is a remarkable world trend in employing UAVs and drones for diverse applications. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. Nevertheless, they depend on positioning systems that may be vulnerable. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming to improve navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. However, its computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a low-cost Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence (HACI) -the cascaded use of Fuzzy C-Means Clustering (FCM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithms -that have been shown able to improve considerably the accuracy of current positioning estimation systems for real-time UAV autonomous navigation, reducing the error in approximately 300cm 2 . In addition, the proposed methodology outperformed two other Computational Intelligence methodologies -Artificial Neural Networks and Regression Models, with 18 different tested approaches -in estimating an UAV position considering the Root-Mean-Square Error against the real trajectory. The generated Fuzzy Inference System has proved to be effective in providing an improved positioning estimation with a low computational burden, about 600 times faster than the fastest embedded sensor refresh rate.

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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.

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