Traditional jamming methods protect scene targets by generating barrage jamming or deceptive jamming effect, but the single jamming effect is currently difficult to meet the increasingly complex electromagnetic environment. To meet the complex electromagnetic environment, a 2-D precise controllable smart jamming method is proposed in this letter, which can produce two kinds of jamming effects: barrage jamming and deceptive jamming. Based on the synthetic aperture radar (SAR) imaging properties of linear frequency modulation (LFM), the range and azimuth modulation terms have been designed to generate precise controllable jamming. First, both the range and azimuth jamming positions can be controlled by first-order phase error modulation. Subsequently, multiple phases sectionalized modulation is performed in the range direction, quadratic phase error or periodic phase error modulation is performed in the azimuth direction. Different jamming effects can be generated by changing the jamming modulation item. Theoretical analysis and simulation results show that the proposed method can produce 2-D precise controllable smart jamming effects.

Lunar crater ejecta can provide important information regarding the impact cratering process and the properties of subsurface materials. However, the mapping of the range of the crater ejecta has been hampered by the degradation of the lunar surface’s morphological features. Polarimetric synthetic aperture radar (SAR) is an effective technique to determine the scattering characteristics of the lunar surface and subsurface, and for distinguishing the fresh crater ejecta from surrounding regions. This letter uses a three-component compact decomposition to process the Mini-RF data to identify the scattering characteristics of the crater floor, crater wall, crater ejecta of the simple fresh crater, and lunar background regolith. Then, a method for mapping the range of crater ejecta is proposed to obtain the boundary between the crater ejecta and other regions by analyzing the differences in polarimetric scattering characteristics. Using this method, the crater ejecta of six craters were extracted and verified with the range of the ejecta obtained through visual interpretation on SAR imagery, the accuracy of the ejecta range obtained by the proposed method is 82%-95%. The results show that the proposed method can effectively depict the range of the simple fresh lunar crater ejecta.

Dual-polarized inverse synthetic aperture radar (ISAR) systems can provide more extra information than single-polarized ISAR systems, which can be used to increase image quality during the imaging process. However, there is limited research on the subject of dual-polarized ISAR. In this letter, an enhanced phase gradient autofocus (PGA) method was proposed for dual-polarized ISAR imaging. The Cloude-Pottier decomposition is employed to extract the polarization parameters$H$and$\alpha $from the dual-polarized primary image. Subsequently, the PGA is enhanced by selecting high-quality prominent scatterers based on these$H$and$\alpha $parameters. By making full use of the prominent scatterers selected by polarization information, the performance of PGA was improved, and thus, the imaging quality was significantly improved. Finally, both the real measurement data of aircraft targets and ship targets verified the effectiveness of the proposed method.

In recent years, the application of deep learning for water-body detection in Synthetic Aperture Radar (SAR) images has seen extensive development. However, a significant proportion of these works primarily concentrate on enhancing and optimizing the model structure, with inadequate exploration of the potential impact of hyperparameter settings, a critical determinant of model performance. Thus, to fully exploit the power of deep learning in water-body detection from SAR images, this letter presents a diversified optimization strategy that revolves around the Dung Beetle Optimizer-Convolutional Neural Network (DBO-CNN) model, complemented by characteristic fusion and decision-level fusion. The DBO-CNN model employs DBO algorithm to search for optimal hyperparameter of CNN model for bolstering the performance of water-body detection in SAR images. To further enhance the performance, the DBO-CNN model uses unique input data which is constructed by integrating the polarimetric characteristic obtained from H/α and model-based polarization decomposition methods with backscatter characteristic. Finally, two decision-level fusion methods are proposed to optimize detection results, enhancing the recall and Intersection over Union (IoU) to 96.5% and 91.5%, respectively. In summary, Spaceborne SAR images, with the application of polarization decomposition and neural network, provides new insights and in-depth understanding for detecting water-body.

Although the multiple-phase-sectionalized-modulation (MPSM) jamming can produce barrage jamming effects in a local range, the range of barrage jamming can't be controlled. Against this disadvantage, an improved MPSM jamming method based on a non-linear frequency-modulate (NLFM) signal, i.e., MBN, is proposed in this paper. The MBN realizes the control of the jamming range in the fast time domain based on the controllable time-frequency structure of the NLFM signal. To generate the desired NLFM signal, the shaping factor is defined. Subsequently, the feasibility and effectiveness of the MBN jamming method are confirmed by simulation experiments.

The lower reaches of the ancient Yellow River (AYR) migrated very frequently, like a loong swinging its tail on the land of China. As the origin of Chinese civilization, AYR provided natural conditions for the people to thrive. However, the sediment carried by AYR still has a negative impact on local agricultural production. This letter, based on Gaofen-3, extracted scattering characteristics of archeological anomalies, detecting part of AYR during Song and Jin Dynasties, which was confirmed with field investigations. Then, an adaptive irregular convolution kernel U-Net (AICK-U-Net) was proposed to reconstruct the channel of AYR, based on the images obtained by the different polarization decomposition methods in October, and the precision and recall reached 96.21% and 94.45%, respectively. Finally, two decision-level methods were proposed to optimize the reconstruction results, improving the precision and recall to 96.39% and 97.36%, respectively. In summary, Spaceborne synthetic aperture radar (SAR), with the application of polarization decomposition and neural network, provides new insights for detecting archeological anomalies and reconstructing archaeolandscapes.

Synthetic aperture radar (SAR) often shares spectrum with other systems, such as radio, TV, cellular network, and so on, which are likely to produce radio frequency interference (RFI). Pulse RFI, which hinders SAR signal processing and image interpretation severely, is a common form of RFI and cannot be neglected. The simple and easy-to-implement frequency-domain notch filtering (FNF) method has been widely used to mitigate narrowband pulse RFIs. However, a well-known problem with abnormal sidelobe effect, which is caused by missing spectrum gaps due to the notch operation, is aroused when using FNF. In this letter, a novel time-domain notch filtering (TNF) is proposed. In the proposed method, pulse RFI occurrences are detected and notched by a simple log-ratio operator in a pulse by pulse manner. Then, missing-data iterative adaptive approach (MIAA) is performed to recover the notched signal to avoid ghosts. Experimental results via simulated and real L-band airborne SAR raw data validate the performance of the proposed method.

The equivalent rotation vector synthesized by 3-D nonuniform rotation of ship target leads to the time-varying characteristic of Doppler frequency of ship echo data, which will cause the image to be defocused in azimuth. In order to obtain high-resolution inverse synthetic aperture radar (ISAR) images of ship targets, we developed an optimal time selection algorithm based on time-frequency analysis of multiple scatterers. In this letter, we addressed the challenges of the serious cross-term interference in time-frequency analysis and measuring the stability of Doppler frequency. The time interval with a minimum variation of Doppler frequency was determined by using time-frequency analysis of multiple isolated scatterers and root mean squared error (RMSE). The effectiveness and robustness of the proposed algorithm were evaluated by both simulated and real ISAR data.

In complex electromagnetic environments, synthetic aperture radar (SAR) is severely affected by radio frequency interference (RFI) from other systems, such as ground-based radar, cellular networks, and global positioning systems, and this interference cannot be neglected. Pulse RFI (PRFI), a common form of RFI, can hinder SAR signal processing and image interpretation to varying degrees. The time-domain notch filtering method designed for mitigating PRFI can locate and mitigate the evident PRFI covered in SAR echo data, but it is helpless against PRFI hidden in a strong echo signal. In this article, a three-step approach is proposed to tackle the PRFI problem. In the proposed approach, the first step is to detect and locate PRFI; this is based on eigenvalue decomposition (EVD) and the short-time Fourier transform (STFT). The second step is to notch PRFI, and this is based on a time-domain notch filter. The third step is to recover the notched signal using a novel matrix completion strategy, which integrates with a robust low-rank matrix completion (LRMC) technique—i.e.,the singular value thresholding (SVT) algorithm—and a well-known Lagrange interpolation technique. Experimental results via simulated SAR data, Sentinel-1 level-0 raw data, and L-band airborne SAR raw data demonstrate the performance of the proposed approach.

In synthetic aperture radar (SAR) data processing, radio frequency interference (RFI) has been recognized as a challenging issue, which can significantly degrade the image quality, including amplitude, phase, geometry, and so on. Most of the RFI sources are direct waves transmitted from the ground. Recently, a new form of RFI, namely, the mutual RFI (MRFI), which is a kind of scatter-wave RFI originating from the nearby ground area simultaneously illuminated by different SAR satellites, has been reported. In this article, the signatures of MRFI are characterized, and an example case study between Chinese GaoFen-3 (GF-3) and European Sentinel-1A (S-1A) is analyzed. In order to remove the artifacts caused by MRFI on SAR images, a novel RFI detector based on spectrum energy cancellation (SEC), which has the capability of detecting MRFI, is developed. Two methods, i.e., the traditional notch filtering method and an improved eigen-subspace projection (ESP) method proposed in this article, are employed to mitigate MRFI. The former method has robust mitigation performance with a fast processing speed, whereas the latter has an improved mitigation accuracy and lower sidelobes for strong scatterers. The methods are designed for cases in which the MRFI has a different radar center frequency than the useful signal, and some of the data are free from MRFI. The conditions required for the proper use of the approach are discussed. In contrast to the conventional mitigating methods performed on the raw data domain, the proposed approach begins with the focused single-look complex (SLC) SAR data. The effectiveness of the proposed approach is demonstrated on several GF-3 SLC SAR images, which were contaminated by MRFI from the S-1A.

Currently, the spaceborne synthetic aperture radar (SAR) system transmits a great deal of data to the ground processing station and generates massive images daily, only a tiny fraction of which contains radio frequency interference (RFI). However, most of the existing RFI detection methods are based on the prior conditions in which the known image contains interference. In fact, it is difficult to learn whether SAR images contain RFIs without prescreening, so it is of great significance to the rapid and real-time screening and detection of RFI in SAR images. This paper proposes a method to screen and detect RFI from massive SAR images simultaneously. 1) We construct an approximate RFI-free background image by using the preprocessed time-series SAR images acquired in the past. 2) We generate difference images based on the image change detection method and analyze them by using an adaptive threshold, then calculate the entropy of all difference images to complete the preliminary screening of the RFI-containing images. 3) According to the preliminary results, we remove the RFI-containing parts; after reconstructing the background, we repeat step 2 with the images to be detected and obtain the final screening and detection results. Massive experimental results based on Sentinel-1 images validate the performance of the proposed method.

Synthetic aperture radar (SAR), an active microwave remote sensing equipment, can provide all-day, all-weather, and high-resolution images for Earth observation. Ship monitoring using SAR is significant to fishery application, marine transportation, maritime security, and so on. Most traditional SAR ship detection methods use the amplitude information of the SAR imagery and employ the constant false alarm rate (CFAR), deep learning, and other technologies to detect the ship targets. However, in addition to the amplitude image, the SAR single-look complex (SLC) data can also be utilized for ship detection since its complex-valued information is beneficial to characterize the ship’s feature in the transforming domains. In this article, the strong scattering feature of ships in the image domain and low-rank feature in the transforming domain is studied, and on this basis, a two-step detector is proposed to detect ship targets in SAR SLC image. The first step is to identify the presence of ships, which is based on the eigen-subspace projection (ESP) technology to detect the occurrences of the ship targets in azimuth cells of the SAR data. The second step is to locate the position of ships, which is based on the low-rank and sparse matrix decomposition (LRSMD) to obtain the instantaneous frequency information of the ship targets in the time–frequency domain. Finally, the ship targets can be located in a line-by-line manner in the azimuth direction. Experimental results via European Sentinel-1A and Chinese Gaofen-3 single-pol SAR SLC data demonstrate the performance of the proposed detector.

In order to realize the raw data simulation of high resolution squint spotlight Synthetic Aperture Radar (SAR) efficiently, an effective acceleration algorithm is proposed. This algorithm combines the time-domain raw data simulation model and its signal characteristics to compensate the range cell migration (RCM) existing in the raw data simulation process of squint spotlight SAR. An adaptive data partitioning algorithm is used, and computes partitioned data respectively in GPU. Then the partitioned data are transmitted and spliced. The algorithm improves the computational efficiency of time-domain raw data simulation, and it solves the problems of huge volume of raw data, limitation of GPU memory and data transmission. The experimental results of point target and distributed target show that the speedup ratio of this algorithm reaches 209.93, which verifies the effectiveness of the proposed method.

The propagation of spaceborne synthetic aperture radar (SAR) signal is affected by the ionosphere, which will lead to the degradation of the imaging quality, especially in the conditions of low-frequency and wide-bandwidth. This paper analyzed the influence of ionosphere on SAR imaging under different carrier frequency, different bandwidth, different ionosphere total electron content (TEC), different azimuth resolution and different scintillation strength by time-domain simulation. The results show that the ionospheric dispersion effect leads to SAR image defocusing, which is closely related to the radar carrier frequency, signal bandwidth, azimuth resolution and TEC value on the propagation path. At the same time, due to the influence of time delay, the dispersion effect also leads to the range displacement of SAR image. The ionospheric scintillation effect reduces the azimuth resolution significantly and in extreme cases imaging is not possible.

Recent developed application-level virtualization brings a groundbreaking innovation to Android ecosystem: a host app is able to load and launch arbitrary guest APK files without the hassle of installation. Powered by this technology, the so-called "dual-instance apps" are becoming increasingly popular as they can run dual copies of the same app on a single device (e.g., login Facebook simultaneously with two different accounts). Given the large demand from smartphone users, it is imperative to understand how secure dual-instance apps are. However, little work investigates their potential security risks. Even worse, new Android malware variants have been accused of skimming the cream off application-level virtualization. They abuse legitimate virtualization engines to launch phishing attacks or even thwart static detection. We first demonstrate that, current dual-instance apps design introduces serious "shared-everything" threats to users, and severe attacks such as permission escalation and privacy leak have become tremendously easier. Unfortunately, we find that most critical apps cannot discriminate between host app and Android system. In addition, traditional fingerprinting features targeting Android sandboxes are futile as well. To inform users that an app is running in an untrusted environment, we study the inherent features of dual-instance app environment and propose six robust fingerprinting features to detect whether an app is being launched by the host app. We test our approach, called DiPrint, with a set of dual-instance apps collected from popular app stores, Android systems, and virtualization-based malware. Our evaluation shows that DiPrint is able to accurately identify dual-instance apps with negligible overhead.

Interrupted FMCW (IFMCW) SAR mode, which only employs a single antenna from a single micro-satellite, was proposed by Ahmed et al., recently. However, periodical data gaps will occur in the interrupted mode, caused by the process of switching the radar transmitter on and off. To solve this problem, in this paper, a modified aperture-interpolation -based approach is proposed to fill the data gaps. The approach can recover the gapped data with excellent performance and thus significantly suppress the artifacts (or ghosts) induced by the periodical data gaps. Processing results of real data acquired with an experimental airborne FMCW SAR system, demonstrate the effectiveness of the proposed approach.

Provable security based on complexity theory provides an efficient way for providing the convincing evidences of security. In this paper, we present a definition of generic ID-based signature schemes by extending the definition of generic signature schemes, and prove the Forking lemma for that. Then we propose a new and efficient ID-based signature scheme built upon bilinear maps. We prove its security under k-CAA computational assumption in the random oracle model.