Asst. Prof. H. Birkan Yılmaz receives BAP-SUP grant with his proposal titled "System for Localizing Drone Signals and Remote Control Units (CONNECT)" Drones, i.e., small Unmanned Aerial Vehicles (UAVs) are used in many areas including search & rescue applications, geographic mapping, disaster management, precision agriculture, wildlife monitoring, shipping, delivery, and aerial photography. However, there are also undesired/malicious usages of drones such as: terrorist attacks, surveillance for restricted/critical zones, and smuggling across borders/prisons. Therefore, there are regulations for drone usage to identify these threats.
The CONNECT project aims to develop a localization system to prevent undesired/malicious use of drones. In the literature, there are proposals and solutions focusing on single sensor type. CONNECT will be a multi-sensor platform including sound, radio frequency (RF), and optic sensors to overcome the weaknesses of each sensing mechanism. These kinds of measurement systems with multiple types of sensors are called multimodal acquisition systems. CONNECT will consist of three sensing stations and each of them will be equipped with RF-based, acoustic, and optical sensing devices. For complex phenomena like drone localization, it is not expected to have a single modality that provides complete and robust knowledge. Therefore, each modality introduces a diversity that can be exploited. In addition to drone detection and localization, CONNECT will also aim to localize the remote controller units, If there is a communication link between the drone and the control unit.
Model-driven approaches require realistic models of the underlying processes and they always come with some assumptions, which may not be plausible. For complex and multimodal systems, model-driven approaches may not be the best choice for revealing the relationships between modalities. On the other hand, Machine Learning (ML) algorithms and artificial neural networks architectures are frequently used to perform detection, classification, and tracking within the data-driven framework. Data-driven approaches have been successful in human motion tracking, object detection, astrophysics, audiovision, and more. By focusing on data-driven approaches, we plan to reduce the number of assumptions and external inputs. We will compare datadriven and model-driven approaches within the modalities and we aim to improve the localization accuracy of the CONNECT by incorporating ML-based approaches to the existing techniques within the modalities and the fusion
The CONNECT project aims to develop a localization system to prevent undesired/malicious use of drones. In the literature, there are proposals and solutions focusing on single sensor type. CONNECT will be a multi-sensor platform including sound, radio frequency (RF), and optic sensors to overcome the weaknesses of each sensing mechanism. These kinds of measurement systems with multiple types of sensors are called multimodal acquisition systems. CONNECT will consist of three sensing stations and each of them will be equipped with RF-based, acoustic, and optical sensing devices. For complex phenomena like drone localization, it is not expected to have a single modality that provides complete and robust knowledge. Therefore, each modality introduces a diversity that can be exploited. In addition to drone detection and localization, CONNECT will also aim to localize the remote controller units, If there is a communication link between the drone and the control unit.
Model-driven approaches require realistic models of the underlying processes and they always come with some assumptions, which may not be plausible. For complex and multimodal systems, model-driven approaches may not be the best choice for revealing the relationships between modalities. On the other hand, Machine Learning (ML) algorithms and artificial neural networks architectures are frequently used to perform detection, classification, and tracking within the data-driven framework. Data-driven approaches have been successful in human motion tracking, object detection, astrophysics, audiovision, and more. By focusing on data-driven approaches, we plan to reduce the number of assumptions and external inputs. We will compare datadriven and model-driven approaches within the modalities and we aim to improve the localization accuracy of the CONNECT by incorporating ML-based approaches to the existing techniques within the modalities and the fusion
