Sea clutter suppression plays an important role in improving the estimation accuracy of the motion parameters of moving ships. Based on the chaotic characteristics of sea clutter, a novel sea clutter suppression method based on complex-valued neural networks optimized by power spectral density is proposed. The complex-valued neural networks helped reduce the phase prediction error of sea clutter, so that the sea clutter prediction accuracy was significantly improved compared with that of real-valued neural networks. The power spectral density function was added to the loss function of the complex-valued neural networks to optimize the training of the networks, and the prediction accuracy was further improved. The sea clutter could be effectively suppressed by cancellation and the signal-to-clutter ratio of the echo was improved. Experimental results based on measured sea clutter data validated the proposed method.

Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms.

The ship on the sea has the phenomenon of swing under the combined action of sea waves, wind and its stability system in the case of synthetic aperture radar (SAR) imaging, which makes it difficult to focus. In order to correct the two-dimensional (2-D) spatial variance and accomplish the imaging of swing ship, this paper proposes a new imaging algorithm based on spatial variant phase gradient autofocus (SV-PGA). Firstly, the entropy difference is used to determine the specific position of the ship. Then the ship image is extracted and segmented by a variable-size segmentation strategy. PGA is used to focus each sub image, and a well-focused ship image is obtained. The results of simulation experiments and real data show that the algorithm has good focusing performance.

Speckle is a significant problem in synthetic aperture radar (SAR) image processing, as it deteriorates the texture details and thus limits the precision of its high-accuracy applications. The impact of speckle on SAR images is related to their structural characteristics, which usually classify the images into homogeneous regions, heterogeneous regions, and extremely heterogeneous regions. In the latter two regions, structures such as edges and strong point targets generate a more complex speckle distribution which differs from that in homogeneous regions. Despeckling filters based on multiplicative models perform well in homogeneous regions but exhibit poor performance in the other two types of regions. To solve this problem, a collaborative optimization for SAR image despeckling with structure preservation is proposed in this paper, which leverages the coupling between the despeckling module and the structure extraction module. Guided by structure information, the proposed method achieves better preservation of edges and texture details while removing speckle. Experimental results based on real SAR images demonstrate that the proposed method effectively enhances the despeckling performance in all three types of regions.

Sea clutter usually greatly affects the target detection and identification performance of marine surveillance radars. In order to reduce the impact of sea clutter, a novel sea clutter suppression method based on chaos prediction is proposed in this paper. The method combines a generator trained by Generative Adversarial Networks (GAN) with a Long Short-Term Memory (LSTM) network to accomplish sea clutter prediction. By exploiting the generator’s ability to learn the distribution of unlabeled data, the accuracy of sea clutter prediction is improved compared with the classical LSTM-based model. Furthermore, effective suppression of sea clutter and improvements in the signal-to-clutter ratio of echo were achieved through clutter cancellation. Experimental results on real data demonstrated the effectiveness of the proposed method.

Factors such as wind and rotors make the unmanned aerial vehicle (UAV) move away from the ideal flight trajectory, introducing complex relative movement between synthetic aperture radar (SAR) and target during long integration time, resulting in the two-dimension (2-D) space-variant defocusing characteristics of imaging. In order to form well-focused images, a motion error model based on sinusoidal series is proposed. Subsequently, through the extraction of motion error from a limited number of prominent scatters in the scene, the coefficients of the motion error model are determined, enabling precise compensation for the complex spatial variance. Validationresults based on the simulated and real data of UAV SAR show that the proposed algorithm has well focusing effect.