An automated accurate sound-based amateur drone detection method based on skinny pattern

Nowadays, the popularity of amateur drones (ADr) has increased marginally, and many research areas, such as machine learning, security systems, and the military industry, have been interested in ADrs. The prime goals of this paper are to present a new sound dataset for ADr detection and present an accurate classification model for ADr detection. This research proposes a novel classification model using the proposed skinny pattern and iterative neighborhood component analysis (INCA) feature selector using ADr sounds. The skinny pattern is a new nonlinear pattern that uses the skinny cipher's substitution box (S-Box). Tunable Q-factor wavelet transform (TQWT) and skinny pattern are used together to extract features in multilevel. The most informative features from the generated features are selected using the INCA selector. 16 classifiers were used in five categories to illustrate the general success of the presented model, and these categories are decision tree (DT), discriminant (D), support vector machine (SVM), k nearest neighborhood (kNN), and ensemble classifiers (EC). An ADr sound dataset was collected, and the presented skinny pattern and INCA-based model are applied to the gathered sound dataset to implement the presented model. The best classification accuracy was calculated as 99.72% using Fine kNN. The proposed model was also compared to other ADr detection/classification models, and it outperformed. These obtained results demonstrate the success of the skinny pattern and INCA-based ADr detection model.