The video synthetic aperture radar (VideoSAR) has the high frame-rate imaging capability, which can perform the ground moving target detection. The moving target often leaves the shadow in the sequence of the VideoSAR images, which can indirectly detect the moving target by detecting its shadow. In view of the high false alarm rate and high missed detection rate of existing algorithms, this paper proposes a moving target shadow detection method based on the improved ViBe in the VideoSAR images. Firstly, the shadow information and local contrast information of the VideoSAR image are extracted by using the super-pixel segmentation technology, which can achieve the background reconstruction. Besides, the ViBe algorithm is improved by modifying the definition of the distance in the image pixel classification, adding the image pixel screening, and performing the morphological filtering, which is more suitable for the moving target shadow detection in VideoSAR images. Finally, by jointly extracting the regions of interest from multiple frames' detection results, most of the false alarms are suppressed, which can obtain the excellent performance in detecting the moving target shadow. The experimental results tested on the VideoSAR data released by the Sandia National Laboratories are shown to validate the correctness and effectiveness of the proposed algorithm, for which the detection rate of moving target shadows reached 97.66%, with only 8 false alarms.

In the domain of micromotion target recognition, target characteristics can be extracted through either broadband or narrowband radar echoes. However, due to technical limitations and cost constraints in acquiring broadband radar waveform data, recognition can often only be performed through narrowband radar waveforms. To fully utilize the information embedded within narrowband radar waveforms, it is necessary to conduct in-depth research on multi-dimensional features of micromotion targets, including radar cross-sections (RCSs), time frequency (TF) images, and cadence velocity diagrams (CVDs). To address the limitations of existing identification methodologies in achieving accurate recognition with narrowband echoes, this paper proposes a graph fusion network based on a cross-modal attention mechanism, termed GF-AM Net. The network first adopts convolutional neural networks (CNNs) to extract unimodal features from RCSs, TF images, and CVDs independently. Subsequently, a cross-modal attention mechanism integrates these extracted features into a graph structure, achieving multi-level interactions among unimodal, bimodal, and trimodal features. Finally, the fused features are input into a classification module to accomplish narrowband radar micromotion target identification. Experimental results demonstrate that the proposed methodology successfully captures potential correlations between modal features by incorporating cross-modal multi-level information interactions across different processing stages, exhibiting exceptional accuracy and robustness in narrowband radar micromotion target identification tasks.

Circular synthetic aperture radar (CSAR) has advantages of the omni-directional detection and high-resolution imaging. However, when CSAR detects the dense target in the observation scene, the phenomena such as the layover effect in the CSAR image may make mutual interference between the adjacent vehicles, which leads to the poor performance of dense target detection by traditional methods. To address this problem, this paper has proposed a high-precision imaging and dense vehicle target detection method for the airborne single-pass CSAR, which includes the CSAR high-precision imaging based on the extracted digital elevation model (DEM) information and high-precision dense vehicle target detection based on the CSAR image. For the high-precision imaging, the back-projection algorithm (BPA) is firstly used for the observation scene imaging, and then the DEM information of the vehicle target is extracted using the improved sub-aperture correlation method. Finally, the extracted DEM information is combined with the BPA for the high-precision imaging. For the high-precision detection, the obtained high-precision CSAR image is used to generate the maximally stable extremal region (MSER), then the extreme region is fused using the vehicle target length and width information, and finally the high-precision detection result of the dense vehicle target is obtained. Based on the public CSAR dataset from the American Air Force Research Laboratory, the experimental results show that the proposed method can obtain the high-precision CSAR images and improve the detection rate of dense vehicle target under the premise of ensuring a low false alarm rate.

Synthetic aperture radar (SAR) provides the high- resolution imaging capability and can obtain the ocean and land target information under complex observation conditions. Dual-frequency SAR images exhibit the complementary properties in the resolution and scattering characteristics, thus fusing dual- frequency SAR images can combine these advantages to provide the richer and more comprehensive information for downstream semantic segmentation tasks. However, existing fusion algorithms typically assume preregistered images and focus on the good visual effects while neglecting the semantic requirements of high-level vision tasks. To address this problem, a joint registration and fusion network for the dual-frequency SAR images based on the semantic information is proposed, which can achieve the high-precision cross-frequency registration and high-quality image fusion. First, in the image registration stage, the microwave vision data (MVD) semantic pseudo-color maps are introduced as the auxiliary information, and the semantic-consistency loss is used to guide the registration process, finally the fusion loss is fed back to the image registration as an optimization signal. Second, in the image fusion stage, the global spatial attention mechanism is used for the feature extraction and fusion, and then the semantic segmentation loss is incorporated to guide the image fusion so that the results can better conform to the high-level semantic structures. Experimental results demonstrate the effectiveness and correctness of the proposed method, which can outperform state-of-the-art approaches in the image registration accuracy, image fusion quality and downstream semantic segmentation performance.

Tomographic synthetic aperture radar (TomoSAR) is an advanced three-dimensional (3D) synthetic aperture radar (SAR) imaging technology that can obtain multiple SAR images through multi-track observations, thereby reconstructing the 3D spatial structure of targets. However, due to system limitations, the multi-baseline (MB) monostatic SAR (MonoSAR) encounters temporal decorrelation issues when observing the scene such as forests, affecting the accuracy of the 3D reconstruction. Additionally, during TomoSAR observations, the platform jitter and inaccurate position measurement will contaminate the MB SAR data, which may result in the multiplicative noise with phase errors, thereby leading to the decrease in the imaging quality. To address the above issues, this paper proposes a MB bistatic SAR (BiSAR) 3D imaging method based on the phase error calibration that combines the phase gradient autofocus (PGA) and energy balance intensity-squared optimization autofocus (EB-ISOA). Firstly, the signal model of the MB one-stationary (OS) BiSAR is established and the 3D imaging principle is presented, and then the phase error caused by platform jitter and inaccurate position measurement is analyzed. Moreover, combining the PGA and EB-ISOA methods, a 3D imaging method based on the phase error calibration is proposed. This method can improve the accuracy of phase error calibration, avoid the vertical displacement, and has the noise robustness, which can obtain the high-precision 3D BiSAR imaging results. The experimental results are shown to verify the effectiveness and practicality of the proposed MB BiSAR 3D imaging method.

The airborne wide-angle staring synthetic aperture radar with the ground moving target indication (WasSAR-GMTI) can observe moving targets from multiple angles over extended periods. However, existing airborne WasSAR-GMTI methods exhibit the low accuracy and poor robustness in the three-dimensional (3D) trajectory reconstruction of moving targets. To address these issues, this paper has proposed a 3D trajectory reconstruction method of moving targets using the multi-channel airborne WasSAR based on the L -shaped antenna. The L -shaped antenna system consists of the along-track antenna and cross-track antenna subsystems. First, for the along-track subsystem, the clutter suppression interferometry (CSI) algorithm is applied to detect moving targets, which can extract motion parameters, mainly including the slant range, doppler center and along-track interferometric phase. These parameters are used to estimate the position of the moving target on the equivalent reference plane. Then, for the cross-track subsystem, the cross-track interferometric processing is applied to extract the cross-track phase at the same reference position. Finally, the slant range, doppler center, and cross-track phase are combined to estimate the 3D position of moving targets and then reconstruct their 3D motion trajectory. The proposed method is validated using both simulated and real WasSAR-GMTI data, and experimental results demonstrate that the proposed method has the good performance in the 3D trajectory reconstruction of moving targets.