The synthetic aperture radar (SAR) can be affected by various types of jamming during operation. Among them, the deceptive jamming generated by digital radio frequency memory (DRFM) jammers poses a serious threat to SAR imaging by creating highly realistic false targets. Moreover, with advancements in deceptive jamming technology, the generation speed of deceptive jamming has increased, rendering existing methods less effective. To address this issue, an anti-deceptive jamming method based on pulse repetition interval (PRI) variation design and multi-channel principle is proposed to mitigate the effects of deceptive jamming. First, a PRI variation strategy that will not cause the loss of echo signals in the imaging area is designed. By utilizing this strategy for imaging, deceptive jamming signals are dispersed across different ranges, resulting in preliminary suppression of the jamming. Subsequently, after azimuth non-uniform sampling reconstruction and range processing, most of the jamming signals are suppressed due to the azimuth timing differences between SAR and jamming signals. However, when the jammer uses specific retransmission intervals, such as the average PRI of the PRI sequence, the jamming signals may be concentrated at certain ranges, retaining some coherence and posing a threat to SAR imaging. To overcome this challenge, a residual jamming detection and suppression algorithm based on multi-channel principle is proposed, which can detect and filter out the channels affected by jamming. Finally, an azimuth sparse reconstruction is introduced for azimuth processing. Since the anti-jamming principle of this method relies on the differences in azimuth timing between SAR and jamming, it can suppress deceptive jamming even when the generation speed of deceptive jamming is rapid, which some other anti-deceptive jamming methods cannot achieve. Simulations of SAR imaging under deceptive jamming conditions are conducted for point target scene and complex targets scene. The simulation results show that the proposed anti-deceptive jamming method can effectively suppress deceptive jamming and enable high-quality imaging.

Tomographic synthetic aperture radar (TomoSAR) is an advanced SAR interferometric technique to retrieve 3-D spatial information. However, decorrelation effects degrade the quality of interferometric phases, resulting in errors in the reconstruction. In this paper, we propose a denoising method based on the unsupervised convolution neural network (CNN) with a loss function combining the deterministic descriptive regularization and total variation (TV) term. It can improve both the accuracy and completeness of the reconstructed 3-D point clouds, which is verified by experiments on simulated and real SAR images.

Synthetic aperture radar (SAR) images have been widely used in water body information extraction. However, SAR images suffer from speckles and the additive noise, which affect the performance of automatic information extraction. Thus, we propose the nonconvex-nonlocal total variation (NLTV) regularization to suppress speckles and the additive noise, and improve the performance of water body information extraction using the enhanced images. Experiments using Qilu-1 (QL-1) SAR data verify the effectiveness of the method.

High resolution and high quality are now the requirements in synthetic aperture radar (SAR) research. The sliding spotlight mode can obtain high azimuth resolution because of its large azimuth bandwidth. Group sparse penalty can effectively suppress azimuth ambiguities to improve image quality. Generalized mini-max concave (GMC) penalty is a kind of nonconvex penalty, which is widely used in SAR imaging. In this paper, a novel sliding spotlight SAR imaging method based on group sparsity and nonconvex regularization is proposed. Compared with matched filtering method, the proposed method can suppress noise and azimuth ambiguities. Both simulations and Qilu-1(QL-1) real SAR data experiments verify the effectiveness of the proposed method.

Sparse signal processing theory has been applied to SAR imaging. The estimation of sparsity is crucial for sparse SAR imaging. But the true value of sparsity is unknown. Adaptive parameter estimation for sparse SAR imaging can achieved by the automatic regularization parameter estimating methods. However, these methods are deduced based on measurement matrix, which will cause huge computational and memory costs. Also, the adaptive estimated sparsity is often greater than the true value due the noise and sidelobes. In this paper, we propose improved adaptive parameter estimation method for sparse SAR imaging. The complex-image-based sparse SAR imaging is adopted to pre-estimate the parameter. Then, azimuth-range decouple operators are introduced into parameter estimation method. Simulation and real data experimental results show the effectiveness of the proposed method.

Digital watermarking has been used widely for the purposes of copyright protection and copy deterrence for multimedia content. In a forensic watermarking architecture, a buyer-seller watermarking protocol can enable a seller to identify a traitor from a pirated copy, while preventing the seller from framing an innocent buyer. Existing schemes are inefficient in practice for their high bandwidth usage. This paper proposes a buyer-seller watermarking protocol that is efficient from the bandwidth usage point of view. First, multicast that is an efficient transport technology for one-to-many communication is exploited, which can reduce the bandwidth usage significantly. Second, symmetric encryption instead of public-key encryption is performed on the multimedia content, which also can reduce the complexity and communication cost.