TY - GEN
T1 - Target Detection Within Sea Clutter Based on Combined Time-Frequency Characteristics
AU - Chen, Duo
AU - Liu, Yanyang
AU - Fan, Yifei
AU - Gong, Yanyun
AU - Su, Jia
AU - Liu, Xiangyang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - To conquer the disadvantages of conventional target detection based on a statistical distribution, this paper focuses on the combined time-frequency characteristics of sea clutter and its application in the field of target detection. Due to the complex characteristics of sea clutter, it is difficult to detect targets based on a single feature parameter. Therefore, this paper focused on the combined time-frequency characteristics of sea clutter, where the normalized energy feature of third-order Intrinsic Mode Function (IMF3) and Tsallis Entropy (TE) of spectrum are regarded as two-dimensional characteristics for target detector. Then, the combined time-frequency characteristics of clutter and target are analyzed in detail, and Support Vector Machines (SVM) is taken to train the two-dimensional feature parameters. Finally, through research of X-band real sea clutter datasets, the proposed method improves the target detection accuracy compared with the conventional CFAR method.
AB - To conquer the disadvantages of conventional target detection based on a statistical distribution, this paper focuses on the combined time-frequency characteristics of sea clutter and its application in the field of target detection. Due to the complex characteristics of sea clutter, it is difficult to detect targets based on a single feature parameter. Therefore, this paper focused on the combined time-frequency characteristics of sea clutter, where the normalized energy feature of third-order Intrinsic Mode Function (IMF3) and Tsallis Entropy (TE) of spectrum are regarded as two-dimensional characteristics for target detector. Then, the combined time-frequency characteristics of clutter and target are analyzed in detail, and Support Vector Machines (SVM) is taken to train the two-dimensional feature parameters. Finally, through research of X-band real sea clutter datasets, the proposed method improves the target detection accuracy compared with the conventional CFAR method.
KW - IMF
KW - Sea clutter
KW - Target detection
UR - http://www.scopus.com/inward/record.url?scp=85141220477&partnerID=8YFLogxK
U2 - 10.1109/ICEICT55736.2022.9909245
DO - 10.1109/ICEICT55736.2022.9909245
M3 - 会议稿件
AN - SCOPUS:85141220477
T3 - 2022 IEEE 5th International Conference on Electronic Information and Communication Technology, ICEICT 2022
SP - 586
EP - 589
BT - 2022 IEEE 5th International Conference on Electronic Information and Communication Technology, ICEICT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022
Y2 - 21 August 2022 through 23 August 2022
ER -