Jie Liu

Impulse ultrawideband (UWB) synthetic aperture radar (SAR) combines high-azimuth-range resolution with robust penetration capabilities, making it ideal for applications such as through-wall detection and subsurface imaging. In such systems, the performance of UWB antennas is critical for transmitting high-power, large-bandwidth impulse signals. However, two primary factors degrade radar imaging quality: (1) inherent limitations in antenna radiation efficiency, which lead to low-frequency signal loss and subsequent time-domain ringing artifacts; (2) impedance mismatch at the antenna terminals, causing standing wave reflections that exacerbate the ringing phenomenon. This study systematically analyzes the mechanisms of ringing generation, including its physical origins and mathematical modeling in SAR systems. Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. The method effectively enhances imaging quality while balancing the trade-off between ringing suppression and image fidelity. Validation through numerical simulations and experimental measurements demonstrates significant suppression of time-domain ringing and improved target clarity. The proposed approach holds critical importance for advancing impulse UWB SAR systems, particularly in scenarios requiring high-resolution imaging.

Determining match pairs among disordered images is a prerequisite for constructing tie points and control networks for the bundle adjustment of planetary remote sensing images. The efficiency and accuracy of determining image match pairs greatly affect the performance of generating planetary mapping products and three-dimensional models. However, existing methods for determining image match pairs in the field of planetary mapping generally use exhaustive methods (such as the USGS ISIS software), which are extremely inefficient when processing a large number of images. This paper proposes an efficient method of image overlapping relationship analysis based on the spatial index of KD tree fast search for disordered and large-scale asteroid images. The algorithm first calculates the three-dimensional coordinates of the image center point, constructs a KD tree data structure based on the three-dimensional coordinates of each image center, and then uses the KD tree fast search algorithm to quickly find the image overlapping relationships within the spec-ified spatial range. For situations where the entire celestial body only occupies a local area of the remote sensing image during the approach stage of asteroids, the center of the effective pixels in the image is calculated by the ray intersection of sampling points. In this study, the image data from asteroid exploration missions such as Bennu, Vesta, and Ryugu are used for experiments, and the proposed image match pairs determination algorithm is systematically compared with the corre-sponding modules of USGS ISIS in order to evaluate its performance in terms of efficiency and accuracy. The results show that when processing more than a thousand images, the proposed method is two to three orders of magnitude higher in efficiency than the corresponding module in USGS ISIS while ensuring the correctness of the image overlapping relationships and the accuracy of bundle adjustment, with the advantage becoming clearer as the amount of data increases. At the same time, according to the obtained image match pairs, images that meet the requirements of Stereo Photoclinometry can be quickly and preferably selected, effectively improving the quality of 3D reconstruction models of asteroid images.

The measurement of the target azimuth angle using forward-looking radar (FLR) is widely applied in unmanned systems, such as obstacle avoidance and tracking applications. This paper proposes a semi-supervised support vector regression (SVR) method to solve the problem of small sample learning of the target angle with FLR. This method utilizes function approximation to solve the problem of estimating the target angle. First, SVR is used to construct the function mapping relationship between the echo and the target angle in beamspace. Next, by adding manifold constraints to the loss function, supervised learning is extended to semi-supervised learning, aiming to improve the small sample adaptation ability. This framework supports updating the angle estimating function with continuously increasing unlabeled samples during the FLR scanning process. The numerical simulation results show that the new technology has better performance than model-based methods and fully supervised methods, especially under limited conditions such as signal-to-noise ratio and number of training samples.