In this letter, a moving vehicle detection method is proposed based on spaceborne synthetic aperture radar images, generated by antenna azimuth steering with different azimuth angles and time lags. First, the multisquint geometry and sequential image model are established, and the variation from stationary scatters over sequence is addressed, which interferes the indication of moving signals significantly. Then, the robust principal component analysis (RPCA) is employed in the image domain as the predetection step. Next, with some spatial filters and thresholding estimators, the moving target signature is exploited to further discriminate the residual false detection. Finally, the proposed method is verified by both simulated and real images based on TerraSAR-X data. Results reveal that the clutter suppression can be effectively achieved.

HFV has nowadays attracted significant attention as aerospace vehicle, and reliable control systems are critical for ensuring its mission completion. This article explores a hypersonic flight controller designed to alleviate actuator saturation and uncertain dynamics while ensuring flexible prescribed properties, without relying on the neural approximators or disturbance observers commonly used in existing research. To accomplish this, we initially restructure the attitude dynamics into an integral chain form, which facilitates the construction of a streamlined framework for adaptive backstepping design. This method eliminates the necessity for neural approximators and disturbance observers, thereby enhancing the rapid tracking of reference instructions and reducing computational burden. Furthermore, an anti-saturation auxiliary system is integrated to manage velocity and altitude controller saturations. The state of this auxiliary system is utilized to design a readjustment term that accurately identifies the dynamic potential of error fluctuations and autonomously modifies the prescribed envelope at the appropriate times, thus preventing flight safety issues that may arise under similar conditions in current methodologies. Finally, three simulation scenarios are performed to validate the proposed design.

Spaceborne synthetic aperture radar (SAR) can provide ground area monitoring with large coverage. However, achieving a wide observation scope comes at the cost of resolution reduction owing to the trade-off between these parameters in conventional SAR. In low-resolution imaging, the moving target appears unresolved, weakly scattered, and slow moving in the image sequence, which can be generated by the subaperture technique. This article proposes a novel moving target detection method. First, interferometric phase statistics are combined with the generalized likelihood ratio test detector. A pixel tracking strategy is further exploited to determine whether a motion signal is present. These methods rely on the approximation of both clutter and noise statistics using Gaussian distributions in a low-resolution scenario. In addition, the motion signals are imaged with a subpixel offset. The proposed method is primarily validated using four real image sequences from TerraSAR-X data, which represent two types of homogeneous areas. The results reveal that moving targets can be detected in nearby areas using this strategy. The method is compared with the stack averaged coherence change detection and particle-filter-based tracking strategies.