Trusted Execution Environments (TEEs) employ hardware-based isolation mechanisms to safeguard the confidentiality and integrity of sensitive code and data. One such prevalent implementation is Arm TrustZone, which partitions the system into the secure and normal (non-secure) worlds. However, this partitioning results in the secure world having very limited visibility into the operating information of the normal world, creating a semantic gap between these two worlds. Specifically, the secure world lacks an effective user identity authentication when receiving data requests from the normal world. Consequently, malicious Client Applications (CAs) in the normal world can deceive Trusted Applications (TAs) in the secure world by utilizing elaborate request parameters, compromising the sensitive data stored by other CAs. We systematically classify these Semantic Gap Vulnerabilities (SGVs) and propose a mate system for the TEE calledMaTEEto defend against SGVs.MaTEEutilizes Arm Pointer Authentication (PA) to bind each request to the corresponding CA's identity and then verifies the identity when the CA accesses sensitive data, thereby preventing malicious request forgery. In particular,MaTEEisolates sensitive data of different CAs without modifying existing CAs and TAs. Our evaluation demonstrates thatMaTEEsuccessfully defends against SGVs with a minimal runtime overhead (2.19%).

Most research on sparsity-driven synthetic aperture radar (SAR) imaging has been carried out in$\ell _{1}$-norm regularization and considers that the SAR image contains only targets and noise, which ignores the clutter and seriously degrades classical algorithms. To address this problem, we propose an integrated detection and imaging algorithm for radar sparse targets with constant false alarm rate (CFAR) regularization by alternating direction method of multipliers (ADMM), called CFAR-ADMM, and we further introduce total variation (TV) regularization and propose the more robust CFAR-TV-ADMM. First, a more complete echo signal model, which considers targets, the clutter, and the noise simultaneously, is established. Then, inspired by the CFAR detection, a novel regularization with sparse target awareness is proposed. The proposed regularization can obtain the statistical characteristics of clutter and noise region by region, and distinguish whether the current cell contains the target effectively and accurately. Benefiting from this novel regularization, CFAR-ADMM and TV-CFAR-ADMM can not only realize the sparse imaging but also detect sparse targets simultaneously, which can reduce the propagation error caused by cascading processing and improve the solution accuracy. Finally, the proposed algorithm is verified by simulation data results, phase transition analysis, and real data experiments.

Radar imaging for ships is hard because of the unpredictable motion states of ships. Existing ship radar imaging methods usually do not take the effects of different motion states into account, which leads to a degraded imaging result when the utilized imaging algorithm cannot match the target motion state. To solve this problem, a motion state judgment and radar imaging algorithm selection method is proposed, whose keys are to estimate the motion parameters of scatter points on the ship, judge the target motion state based on the space-variant features of the estimated motion parameters, and further choose a proper imaging algorithm to achieve the radar imaging result with higher quality. In this article, the radar imaging model of ship is first constructed, and the spatial variance features of motion parameters are quantitatively analyzed. Next, an improved motion parameter estimation method utilizing generalized Radon–Fourier transform (GRFT) modified by sidelobe-learning particle swarm optimization (SSLPSO) and relaxation (RELAX) technique is proposed, which can solve the performance reduction caused by the unnecessary values introduced by the method based on traditional GRFT and realize accurate motion parameter estimation. Then, based on the estimated motion parameters, a motion state judgment and radar imaging algorithm selection method, which takes the targets’ motion state, theoretical resolutions, as well as the effect of high-order phase error into consideration, is proposed to obtain a high-quality and high-resolution radar image. Finally, computer simulation and experimental results of GaoFen-3 (GF-3) satellite single channel data validate the proposed method.

Translational motion compensation plays an important role in inverse synthetic aperture radar (ISAR) imaging. However, existing translational motion compensation algorithms cannot work well when the signal-to-noise ratio (SNR) is low and the target has complex motion at the same time, as the algorithms usually assume that these two situations do not occur simultaneously. To address this problem, an improved parametric translational motion compensation algorithm based on signal phase order reduction (SPOR) and minimum entropy is proposed. The key is to decrease the phase order of the signal, which has a nonlinear phase and corresponds to the complex motion, and then obtain the signal with a linear phase corresponding to the noncomplex motion. Subsequently, the signal is transformed into the Doppler domain to generate the SPOR result. Obviously, when the translational motion is well compensated, the SPOR result will be coherently accumulated and has the best quality, which means that the SPOR result has good robustness against the low SNR. Thus, the translational motion is modeled as a polynomial model, the entropy of the SPOR result is taken as the optimizing target, and the relationship between the translational motion compensation parameters and the entropy is established. Finally, coarse search and particle swarm optimization (PSO) are sequentially performed to optimize the entropy of the SPOR result and estimate the translational motion compensation parameters accurately and efficiently. Computer simulation results and experimental results based on unmanned aerial vehicle (UAV) radar validate the proposed algorithm.

Due to the extremely high orbital height and long synthetic aperture time, the geosynchronous synthetic aperture radar (GEO SAR) will inevitably suffer from different types of undesired errors, including atmosphere, orbital measurement error, antenna vibration, and scenery height fluctuation; moreover, because of the extremely large imaging swath, these undesired errors also have severe 2-D spatial variance. Thus, the autofocus processing plays a very important role in GEO SAR. However, current autofocus algorithms cannot handle all of the aforementioned complicated and 2-D spatial-variant errors simultaneously. In this article, an autofocus back projection (BP) method for GEO SAR based on minimum entropy is proposed. First, the BP algorithm based on a digital elevation model (DEM) is adopted to deal with the scenery height fluctuation. Then, the 2-D image segmentation is conducted to solve the spatial variance of the undesired errors. Subsequently, without the assumption of error type and considering both the amplitude error and phase error, the autofocus processing based on minimum entropy and adaptive moment estimation (Adam) is conducted to estimate the undesired errors iteratively and precisely. Moreover, the aperture division and sub-aperture fusion will also be utilized to alleviate the image quality degradation or even defocus, which could also improve the precision of error estimation. Finally, computer simulation results validate the effectiveness of the proposed method.

Traditional synthetic aperture radar (SAR) moving target imaging algorithms usually deal with the enlovpe and phase of the signal seperately, which is under a high signal-to-noise ratio (SNR) condition. In this paper, an improved imaging method for moving targets based on GRFT under low SNR is proposed, and the Doppler ambiguity and motion parameter estimation can be solved simultaneously. Computer simulations are conducted to validate the effectiveness of the proposed method.

Geosynchronous synthetic aperture radar (GEO SAR)-related technologies are being mature, and the first GEO SAR satellite is expected to launch in the next ten years. Under this circumstance, some equivalent experiments should be conducted at the current stage to validate some key characteristics or parameters, which could significantly increase the success possibility of the GEO SAR project. To validate the feasibility of GEO SAR imaging with long integration time, which is the most important and fundamental characteristic of GEO SAR, the first helicopter platform-based equivalent GEO SAR experiment with long integration time was performed in Qianxi County of China on May 22, 2019. The integration time of it is 80 s, which is carefully designed to maintain the consistence between itself and the integration time of the GEO SAR. Furthermore, the azimuth signal-to-noise ratio gain with long synthetic aperture time is analyzed. Moreover, the 2-D space-variant motion error introduced by the complex helicopter trajectory and the performances of different imaging algorithms are analyzed to choose the proper imaging algorithms; to overcome the flaws and unclarities of existing algorithms, some improvements are proposed to obtain the well-focused SAR image. What is more, the equivalence of this experiment is also analyzed detailedly to demonstrate the effectiveness of this experiment. At last, the imaging result with synthetic aperture time of 100 s and the comparison between itself and the optic photograph validate the success of this equivalent experiment and the feasibility of GEO SAR imaging with long integration time.