Kolmogorov–Arnold Networks for High-Resolution Range Profile Recognition In Radar Applications

TY - JOUR

T1 - Kolmogorov–Arnold Networks for High-Resolution Range Profile Recognition In Radar Applications

AU - Ye, Kejun

AU - Wang, Yanhua

AU - Saad, Rola

N1 - Publisher Copyright: ©2025 IEEE.

PY - 2025

Y1 - 2025

N2 - Conventional CNNs are used as the mainstream neural networks for recognizing HRRP signals, however, CNNs requires a large number of training samples. In this paper, a lightweight model combining Kolmogorov-Arnold network (KAN) and convolutional neural network (CNN) for high-resolution distance image (HRRP) target recognition is proposed. This method improves the classification accuracy in noisy environments and small sample scenarios through modular decomposition and B-Spline activation function. Experimental results demonstrate that the novel combined method exhibits superior generalization ability under small sample conditions and achieves good accuracy in the test set, providing an efficient and robust solution for HRRP target recognition.

AB - Conventional CNNs are used as the mainstream neural networks for recognizing HRRP signals, however, CNNs requires a large number of training samples. In this paper, a lightweight model combining Kolmogorov-Arnold network (KAN) and convolutional neural network (CNN) for high-resolution distance image (HRRP) target recognition is proposed. This method improves the classification accuracy in noisy environments and small sample scenarios through modular decomposition and B-Spline activation function. Experimental results demonstrate that the novel combined method exhibits superior generalization ability under small sample conditions and achieves good accuracy in the test set, providing an efficient and robust solution for HRRP target recognition.

KW - HRRP

KW - Kolmogorov-Arnold networks （ KAN）

KW - convolutional neural networks（CNN）

KW - radar signal processing

KW - target recognition

UR - https://www.scopus.com/pages/publications/105034109133

U2 - 10.1109/IGARSS55030.2025.11244029

DO - 10.1109/IGARSS55030.2025.11244029

M3 - Conference article

AN - SCOPUS:105034109133

SN - 2153-6996

SP - 7365

EP - 7369

JO - International Geoscience and Remote Sensing Symposium (IGARSS)

JF - International Geoscience and Remote Sensing Symposium (IGARSS)

T2 - 2025 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2025

Y2 - 3 August 2025 through 8 August 2025

ER -