Radar-Infrared Sensor Fusion Based on Hierarchical Features Mining

• Published in: IEEE signal processing letters 2024, Vol.31, p.66-70
• Main Authors: Yang, Lihe, Feng, Wei, Wu, Yaojun, Huang, Liang, Quan, Yinghui
• Format: Article
• Language: English
• Subjects: Data mining; Electromagnetic interference; Feature extraction; Feature fusion; high resolution range profile (HRRP); Image resolution; Infrared detectors; infrared images; Infrared radar; Infrared sensors; Jamming; Radar; Radar imaging; Scattering; support vector machine (SVM); Target recognition; Weather
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2023.3341397

High resolution range profile (HRRP) provides abundant target information but is susceptible to external electromagnetic interference. While infrared sensor possesses strong anti-jamming capability, it has limited detection range and is vulnerable to weather conditions, leading to reduced imaging resolution. The integration of radar and infrared sensors can synergize their respective strengths to not only improve the reliability and robustness of the system but also enhance the credibility and accuracy of the data. However, there exist many challenges in the research on the fusion of heterogeneous data like HRRP 1D data and infrared 2D data. In this letter, a radar infrared sensor fusion method based on hierarchical features mining (HFM) is proposed to solve the problems above. The method is applied to multi-target recognition tasks to verify the effectiveness. The results demonstrate that the proposed method can enhance the information completeness of the target and improve the accuracy of target recognition.