Multi-Modal Signal Processing Techniques for Drones
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".
Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 414
Special Issue Editors
Interests: UAV; ensemble learning; deep learning; acoustic sensors; (mobile) communications; human–machine interface; cloud edge; ubiquitous computing
Special Issue Information
Dear Colleagues,
The application of unmanned aerial vehicles (UAVs)—drones—is rapidly increasing. In addition to classical applications, such as search-and-rescue and monitoring tasks, their high maneuverability and variable price range allow drones to be customized for further tasks, such as delivery, infrastructure inspection, and autonomous flight taxis. Beyond that, they can be equipped with a wide range of different sensors, enabling drones to serve as a sensor platform for various advanced applications.
These features—together with the possibility of combining or fusing different sensor channels to increase accuracy, reliability, or performance—give drones a broad set of advantages against classical techniques. Additionally, their ability to fly at low altitudes and in narrow surroundings favor drones for use in urban and industrial areas, where communication between drones and humans or in between drones extends the range of applications. Regardless, the maintenance of flight characteristics and the small energy capacity pose new challenges for sensor hardware, the analysis system, and communication infrastructure.
With this Special Issue, we will compile the state-of-art research that addresses various aspects of multi-modal signal processing for drones. Potential topics include but are not limited to the following:
- New concepts, ideas, and technology of multi-modal signal processing for UAVs;
- Evaluation of current advanced signal processing for UAVs;
- Swarm-intelligence improvements based on multi-modal data;
- Autonomous maneuvers supported by (multi-modal) environmental sensing;
- Signal processing to reduce UAVs’ noise emissions;
- Multi-modal UAV tracking, challenges, and applications;
- Multi-modal UAV signature detection or suppression;
- Signal fusion methods in UAV applications;
- Speech and sound processing at UAVs.
Dr. Ingo Siegert
Prof. Oliver Jokisch
Guest Editors
Manuscript Submission Information
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