Multi-Sensor Fusion for UAV Classification Based on Feature Maps of Image and Radar Data
This addresses the need for improved UAV detection to mitigate malicious or accidental incidents, representing an incremental advancement in sensor fusion methods.
The paper tackles the problem of UAV detection and classification by proposing a multi-sensor fusion system using thermal, optronic, and radar data, achieving higher classification accuracy than using individual sensors alone.
The unique cost, flexibility, speed, and efficiency of modern UAVs make them an attractive choice in many applications in contemporary society. This, however, causes an ever-increasing number of reported malicious or accidental incidents, rendering the need for the development of UAV detection and classification mechanisms essential. We propose a methodology for developing a system that fuses already processed multi-sensor data into a new Deep Neural Network to increase its classification accuracy towards UAV detection. The DNN model fuses high-level features extracted from individual object detection and classification models associated with thermal, optronic, and radar data. Additionally, emphasis is given to the model's Convolutional Neural Network (CNN) based architecture that combines the features of the three sensor modalities by stacking the extracted image features of the thermal and optronic sensor achieving higher classification accuracy than each sensor alone.