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.

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.

Linear array (LA) multiple-input–multiple-output (MIMO) synthetic aperture radar (SAR) has the capacity of achieving 3-D images in a single pass. If processed by matched filtering-based linear 3-D imaging, sidelobe suppression is often required for image quality enhancement. However, in the case of imaging a large target in a short range, sidelobes of the target will become spatially variant and curved, and traditional sidelobe suppression methods will fail. This article proposes a new spatially variant curved sidelobe suppression method for LA MIMO SAR short-range 3-D imaging. The key technique is the employment of a new pseudopolar coordinate system where both the spatial variance and the curvature of 3-D sidelobes can be removed. Specifically, a new 3-D spatially variant apodization (SVA) kernel is applied for sidelobe suppression to maintain the resolution performance. The validity of the presented approach has been demonstrated via computer simulations, the tower crane experiment, and the unmanned ground vehicle experiment.

This paper discussed the size of 3 D-printed personalized titanium plates that can gain maximum stability of mandibular fracture and minimize stress shielding through finite element analysis. A 3 D virtual model of mandible with mandibular angle fracture was created from the CT data of patient. 3 D-printed personalized titanium plates varying in length and thickness were designed, and finite element analysis was performed under different loading conditions and fracture healing periods. After that, the stress distribution and deformation of the mandible with gonial fracture could be observed, and the stress shielding rate could be obtained. Finally, SPSS21.0 was used for statistical analysis. The results of finite element analysis indicated that as the thickness of titanium plates and the healing time decreased, the maximum displacement increased, under a certain condition in which the pore size, the width, the hole distance and the bridge spacing were 2.0 mm, 4.0 mm, 6.0 mm, 12.0 mm, respectively. What’s more, in this condition, the thicker the titanium plate and the shorter the healing time were, the higher the stress shielding was at central occlusion. When the thickness of the personalized 3 D-printed titanium plate was 1.0 mm, the maximum displacement tended to be stable and the stress shielding was minimized. It can not only improve the bone stability after tension band fixation, but also minimize the stress shielding, which is expected to expand the indications of tension band fixation.

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.

Phase filtering is one of the core signal processing steps in interferometric synthetic aperture radar (InSAR). In recent years, InSAR phase filtering algorithms have evolved from traditional solutions to deep learning(DL) methods, significantly improving the processing efficiency. However, most DL-based phase filtering techniques originate from optical filtering methods, and these methods inevitably entail a trade-off between noise suppression and detail preservation. To resolve this contradiction and fully take into account the characteristics of InSAR phase, a multi-scale phase filtering network (MPFNet) based on multi-look information fusion is proposed. Firstly, the network adopts the multi-scale structure to balance noise suppression and detail preservation, where the multi-scale information is obtained through multi-look interferograms of varying numbers of looks. Secondly, drawing on the mechanism of super-resolution, the network incorporates the residual feature distillation blocks (RFDB) to restore the scale of interferograms. Finally, in response to the demand for complex phase filtering, a loss function based on cosine similarity is constructed, which avoids the discontinuity at ±π affecting the filtering results. Computer simulation and experiments based on real InSAR data verified the effectiveness of the proposed method.