In a synthetic aperture radar (SAR) system, the three-dimensional rotation of ship targets in the presence of medium to high sea states could cause Doppler frequency shifts and image defocusing, and even the defocusing phenomenon is space-variant along the range direction would occur. These issues would adversely affect the subsequent interpretation of ship targets in SAR images. This paper proposes a refocusing method based on a phase error estimation network (PEE-Net) to address the refocusing problem of three-dimensional rotating ship targets. The proposed method transforms the defocused complex-valued SAR ship image into the range-Doppler domain, estimates the phase error by range unit using PEE-Net, and compensates for space-variant phase errors. To train the network in an unsupervised manner and avoid the challenging task of obtaining labeling samples of non-cooperative ships, the proposed method introduces the image entropy loss function based on the minimum entropy criterion.

In recent years, the complex-valued convolutional neural network (CV-CNN) for processing complex data has made great use in the field of SAR data processing. In this paper, a complex-valued activation enhancement method named CRIA is constructed based on the cross-fusion of real and imaginary activation in the activation layer of CV-CNN, the core of which is to cross-combine the real and imaginary parts of the activation output of the two activation functions to enhance the overall processing of complex data, to enhance the ability of the network to parse complex value information. By conducting classification experiments on ship slices in SAR images of complex data, the experimental results show that the CRIA method in the activation layer can accelerate the network convergence speed and enhance the network classification performance.

Synthetic Aperture Radar (SAR) has advantages such as all-weather, high-resolution, and large mapping bands, which make up for the shortcomings of other surveillance and reconnaissance methods such as optics and infrared. It has good detection and imaging effects on stationary targets. However, SAR imaging of moving targets often results in positional shift and defocusing. The main reason for these problems is that SAR has poor velocity resolution and cannot obtain effective velocity information of moving ship targets. In this case, the paper intends to use High-Frequency Ground Wave Radar (HFSWR) with high velocity resolution to assist SAR [1]. Using the velocity information extracted from HFSWR to achieve position compensation and refocusing of moving ship targets in SAR.

Space-time adaptive processing (STAP) is widely used for clutter suppression[1]. The key of space-time adaptive processing is the accuracy of clutter covariance matrix estimation. In the airborne radar environment, besides the target of interest, there are many clutter and interference targets in different directions. The existence of these echoes makes the training samples used to estimate the clutter covariance matrix uneven. In this case, the paper proposes a sparse recovery STAP algorithm based on prior knowledge for sample selection to solve existing problems.

MEO spaceborne transmitter/missile-borne receiver (MEO/missile-borne) bistatic synthetic aperture radar (BiSAR) system has a number of advantages over other spaceborne bistatic SAR systems and monostatic missile-borne systems. It not only has wide coverage and short revisit time, but it can also achieve multi-missile guidance, small detection probability and forward-looking imaging. However, the imaging of the MEO/missile-born BiSAR is more difficult than other bistatic SAR modes due to the complex characteristics of spatial variance. In this paper, we develop a new BiSAR model with the MEO satellite transmitter and the missile receiver. The accurate geometric model for MEO/Missile-born BiSAR is established by using motion parameter vectors. Then we analyze the phase characteristics in MEO/missile-borne BiSAR with the highly squinted angle in the large scene. The paper lays a foundation for the imaging algorithm of MEO/missile -borne BiSAR system in large scene.