TY - GEN
T1 - Transductive Multi-Prototype Network for HRRP Recognition with Missing Aspects
AU - Yuan, Mingchen
AU - Liu, Jiaqi
AU - Wang, Xinyang
AU - Li, Yang
AU - Wang, Yanhua
AU - Zhang, Liang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The High-Resolution Range Profile (HRRP) plays a pivotal role in radar automatic target recognition. However, due to its aspect sensitivity, the absence of aspects within the training data can lead to a decline in recognition accuracy. To mitigate this issue, we introduce a novel Transductive Multi-Prototype Network (TMP-Net), leveraging the intrinsic similarity within test data to propagate labels along a manifold, thereby achieving classification. TMP-Net addresses the aspect sensitivity by utilizing multiple prototypes per class. Additionally, a loss function term is proposed to ensure the existence of the manifold. Experiments on the MSATR dataset demonstrate that TMP-Net outperforms existing methods by 10.22% in recognition accuracy even with only 10% of aspects represented in the training set. Furthermore, analysis reveals the advantages of employing multiple prototypes.
AB - The High-Resolution Range Profile (HRRP) plays a pivotal role in radar automatic target recognition. However, due to its aspect sensitivity, the absence of aspects within the training data can lead to a decline in recognition accuracy. To mitigate this issue, we introduce a novel Transductive Multi-Prototype Network (TMP-Net), leveraging the intrinsic similarity within test data to propagate labels along a manifold, thereby achieving classification. TMP-Net addresses the aspect sensitivity by utilizing multiple prototypes per class. Additionally, a loss function term is proposed to ensure the existence of the manifold. Experiments on the MSATR dataset demonstrate that TMP-Net outperforms existing methods by 10.22% in recognition accuracy even with only 10% of aspects represented in the training set. Furthermore, analysis reveals the advantages of employing multiple prototypes.
KW - high-resolution range profile
KW - target recognition
KW - transductive inference
UR - http://www.scopus.com/inward/record.url?scp=86000023173&partnerID=8YFLogxK
U2 - 10.1109/ICSIDP62679.2024.10869084
DO - 10.1109/ICSIDP62679.2024.10869084
M3 - Conference contribution
AN - SCOPUS:86000023173
T3 - IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
BT - IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Y2 - 22 November 2024 through 24 November 2024
ER -