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.

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.

Multi-baseline circular synthetic aperture radar (MB CSAR) can be applied to obtain a three-dimensional (3D) image of the observation scene. However, the phase error caused by radar platform motion or atmospheric propagation delay restricts its 3D imaging capabilities. The phase error calibration of MB CSAR data is an essential step in the 3D imaging procedure due to the limited accuracy of positioning sensors. Phase gradient autofocus (PGA) is widely utilized to estimate the phase errors but is subject to shifts in the direction perpendicular to the line of sight and long iteration time in some sub-apertures. In this paper, an improved PGA method for MB CSAR 3D imaging is proposed, which can suppress the shifts and reduce computation time. This method is based on phase gradient estimation, but the prominent units are selected with an energy criterion. Then, weighted phase gradient estimation is presented to suppress the influence of prominent units with poor quality. Finally, a contrast criterion is adopted to reach faster convergence. The experimental results based on the measured MB CSAR data (Gotcha dataset) demonstrate the validity and feasibility of the proposed phase error calibration method for MB CSAR 3D imaging.