Radar automatic target recognition based on feature extraction for complex HRRP

• Published in: Science China. Information sciences Vol. 51; no. 8; pp. 1138 - 1153
• Main Authors: Du, Lan, Liu, HongWei, Bao, Zheng, Zhang, JunYing
• Format: Journal Article
• Language: English
• Published: Heidelberg SP Science in China Press 01.08.2008; Springer Nature B.V
• Subjects: Algorithms; Automatic target recognition; Computer Science; Feature extraction; Feature recognition; Information Systems and Communication Service; Normal distribution; Phases; Radar; Statistical analysis; Target recognition; 特征提取; 自动靶点识别; 高分辨率; minimum Euclidean distance classifier; adaptive Gaussian classifier (AGC); complex high-resolution range profile (HRRP); feature extraction; radar automatic target recognition (RATR)
• ISSN: 1009-2757; 1674-733X; 1862-2836; 1869-1919
• DOI: 10.1007/s11432-008-0087-0

Radar high-resolution range profile （HRRP） has received intensive attention from the radar automatic target recognition （RATR） community. Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity in this paper, only the amplitude information in the complex HRRP, called the real HRRP in this paper, is used for RATR, whereas the phase information is discarded. However, the remaining phase information except for initial phases in the complex HRRP also contains valuable target discriminant information. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector, referred to as the complex feature vector with difference phases, contains the difference phase information between range cells but no initial phase information in the complex HRRR According to the scattering center model, the physical mechanism of the proposed complex feature vector is similar to that of the real HRRP, except for reserving some phase information independent of the initial phase in the complex HRRP. The recognition algorithms, frame-template establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. Moreover, the components in the complex feature vector with difference phases approximate to follow Gaussian distribution, which make it simple to perform the statistical recognition by such complex feature vector. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are properly selected.