Research on Information Fusion of Computer Vision and Radar Signals in UAV Target Identification

As one of the crucial sensing methods, multisensor fusion recognition aids the Internet of Things (IoT) in connecting things through ubiquitous perceptual terminals. The small size, sluggish flying speed, low flight altitude, and low electromagnetic intensity of unmanned aerial vehicles (UAVs) have put enormous strain on air traffic management and airspace security. It is urgent to achieve effective UAV target detection. The radio monitoring method, acoustic detection scheme, computer vision, and radar signal detection are commonly used technologies in this field. The radio monitoring approach has low accuracy, the acoustic detection strategy has a limited detection range, computer vision is limited by weather conditions, and the radar signals at low altitudes are influenced by ground clutter. To address these issues, this paper proposes an information fusion strategy based on two levels of fusion: data-level fusion and decision-level fusion. In this strategy, Computer vision and radar signals complement each other to improve the detection accuracy. For each level, the method of information fusion is introduced in detail. Furthermore, the effectiveness of the method has been demonstrated by a series of comprehensive experiments. The results show that the accuracy of the fusion method is improved, and the proposed method can still work even when the single method loses function.