TY - JOUR
T1 - Chaff jamming recognition and suppression based on semi-realistic dataset and Pre-Decluttering Dual-Stage UNet
AU - Xu, Qinwen
AU - Fu, Xiongjun
AU - Li, Mingling
AU - Zhao, Congxia
AU - Dong, Jian
N1 - Publisher Copyright:
© 2024 The Authors. IET Radar, Sonar & Navigation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
PY - 2024
Y1 - 2024
N2 - Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi-realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre-Decluttering Dual-Stage UNet (D2UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D2UNet, the authors also design a multi-stage loss function to achieve progressive training. Extensive experimental results demonstrate that D2UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal-to-jamming ratio, 0.9952 structure similarity index measure) on the semi-realistic dataset. Its practical effect is further verified on measured data.
AB - Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi-realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre-Decluttering Dual-Stage UNet (D2UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D2UNet, the authors also design a multi-stage loss function to achieve progressive training. Extensive experimental results demonstrate that D2UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal-to-jamming ratio, 0.9952 structure similarity index measure) on the semi-realistic dataset. Its practical effect is further verified on measured data.
KW - electronic countermeasures
KW - image processing
KW - interference suppression
KW - jamming
KW - radar signal processing
KW - radar target recognition
UR - http://www.scopus.com/inward/record.url?scp=85191182075&partnerID=8YFLogxK
U2 - 10.1049/rsn2.12569
DO - 10.1049/rsn2.12569
M3 - Article
AN - SCOPUS:85191182075
SN - 1751-8784
JO - IET Radar, Sonar and Navigation
JF - IET Radar, Sonar and Navigation
ER -