Authors:
Joaquín García-Gómez
;
Marta Bautista-Durán
;
Roberto Gil-Pita
;
Inma Mohíno-Herranz
;
Miguel Aguilar-Ortega
and
César Clares-Crespo
Affiliation:
Department of Signal Theory and Communications, University of Alcalá, Alcalá de Henares 28805 and Spain
Keyword(s):
Drone Detection, Smart Sound Processing, Feature Extraction, Feature Selection, Evolutionary Computation, Cost Constraints.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Evolutionary Computation
;
Feature Selection and Extraction
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
Sometimes, drones lead to problems of invasion of privacy or access to restricted areas. Because of that, it is important to develop a system capable of detecting the presence of these vehicles in real time in environments where they could be used for malicious purposes. However, the computational cost associated to that system must be limited if it has to work in an autonomous way. In this manuscript an algorithm based on Smart Sound Processing techniques has been developed. Feature extraction, cost constrained feature selection and detection processes, typically implemented in pattern recognition systems, are applied. Results show that it is possible to detect the presence of drones with low cost feature subsets, where MFCCs and pitch are the most relevant ones.