TY - JOUR
T1 - 用于 HRRP 多类目标识别的 D 距离分类器
AU - Yao, Lu
AU - Han, Lei
AU - Yang, Lei
AU - Chai, Xiaofei
N1 - Publisher Copyright:
© 2022 Beijing Institute of Technology. All rights reserved.
PY - 2022/11
Y1 - 2022/11
N2 - In the field of Radar Automatic Target Recognition (RATR), in order to ensure that the target recognition algorithm based on High-Resolution Range Profile (HRRP) still has excellent recognition performance when performing small-sample and multi-class target recognition, it is necessary to propose a recognition algorithm with excellent generalization performance and low computational complexity. Use the ratio to calculate a ratio distance between two vectors, and apply the ratio distance to a distance classifier, which is called D distance classifier. Then, the D distance classifier is compared with some other RATR statistical models using the measured data of eight ground targets, and its recognition accuracy in small samples and multi-class targets is analyzed respectively. The final result verifies that the D distance classifier still has excellent generalization performance and low computational complexity when recognition is performed with small-sample and multi-class target.
AB - In the field of Radar Automatic Target Recognition (RATR), in order to ensure that the target recognition algorithm based on High-Resolution Range Profile (HRRP) still has excellent recognition performance when performing small-sample and multi-class target recognition, it is necessary to propose a recognition algorithm with excellent generalization performance and low computational complexity. Use the ratio to calculate a ratio distance between two vectors, and apply the ratio distance to a distance classifier, which is called D distance classifier. Then, the D distance classifier is compared with some other RATR statistical models using the measured data of eight ground targets, and its recognition accuracy in small samples and multi-class targets is analyzed respectively. The final result verifies that the D distance classifier still has excellent generalization performance and low computational complexity when recognition is performed with small-sample and multi-class target.
KW - high-resolution range profile
KW - multi-class target recognition
KW - radar automatic target recognition
KW - ratio distance
KW - small-sample
UR - http://www.scopus.com/inward/record.url?scp=85163446613&partnerID=8YFLogxK
U2 - 10.15918/j.tbit1001-0645.2021.318
DO - 10.15918/j.tbit1001-0645.2021.318
M3 - 文章
AN - SCOPUS:85163446613
SN - 1001-0645
VL - 42
SP - 1144
EP - 1149
JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
IS - 11
ER -