ArticleSensors (Basel, Switzerland)2022
Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Distinguishing a Drone from Birds Based on Trajectory Movement and Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Composite Filament Materials for 3D-Printed Drone Parts: Advancements in Mechanical Strength, Weight Optimization and Embedded Electronics.Materials (Basel, Switzerland) · 2025Review
- Bio-inspired motion detection models for improved UAV and bird differentiation: a novel deep learning framework.Scientific reports · 2025Article
- Multimodal image fusion for enhanced vehicle identification in intelligent transport.PeerJ. Computer science · 2025Article
- Camera-Radar Fusion with Radar Channel Extension and Dual-CBAM-FPN for Object Detection.Sensors (Basel, Switzerland) · 2024Article
- Securing Your Airspace: Detection of Drones Trespassing Protected Areas.Sensors (Basel, Switzerland) · 2024Review
- An Application of Analytic Wavelet Transform and Convolutional Neural Network for Radar Intrapulse Modulation Recognition.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The paper focuses on the problem of detecting unmanned aerial vehicles that violate restricted airspace. The main purpose of the research is to develop an algorithm that enables the detection, identification and recognition in 3D space of a UAV violating restricted airspace. The proposed method consists of multi-sensory data fusion and is based on conditional complementary filtration and multi-stage clustering. On the basis of the review of the available UAV detection technologies, three sensory systems classified into the groups of passive and active methods are selected. The UAV detection algorithm is developed on the basis of data collected during field tests under real conditions, from three sensors: a radio system, an ADS-B transponder and a radar equipped with four antenna arrays. The efficiency of the proposed solution was tested on the basis of rapid prototyping in the MATLAB simulation environment with the use of data from the real sensory system obtained during controlled UAV flights. The obtained results of UAV detections confirmed the effectiveness of the proposed method and theoretical expectations.
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Registered trials
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