Find in Library

The existing automatic target recognition (ATR) methods for synthetic aperture radar (SAR) images mainly utilize the real-valued magnitude information while often ignoring the phase information. However, the phase information also provides important details, which can be utilized to improve the ATR performance. To address this issue, a fully complex-valued light-weight network (CVLWNet) is proposed based on complex-valued operations, such as complex-valued convolution and complex-valued batch normalization. Besides, to achieve reduced parameters and enhanced robustness of the designed network, many complex-valued blocks of operations are built, including the CMish activation function, the complex-valued residual link block (CVReLBlock), the lightweight complex-valued cross stage partial block (LC-CSPBlock). In the designed CVLWNet, the input, output, and weight parameters are all complex-valued, which makes it possible to sufficiently exploit the complex-valued characteristics of SAR data. Comparative experiments are conducted with the moving and stationary target acquisition and recognition dataset. Compared with the state-of-the-art real-valued and complex-valued models under both standard and extended operating conditions, the performance of proposed method is verified.