Lei Dong

With the increasing congestion in orbital environments, on-orbit observation has become critical for spacecraft safety. This study investigated the observation performance of space-based inverse synthetic aperture lidar (ISAL) for monitoring on-orbit targets and space debris in geostationary Earth orbit (GEO) and low Earth orbit (LEO). Using STK simulations, the performances under fly-around and fly-by scenarios were evaluated based on three key parameters: minimum imaging time, pulse repetition frequency (PRF), and signal-to-noise ratio (SNR). The results reveal that while the GEO provided a high PRF and SNR for fly-around observations, longer imaging times limited its practical application, making the fly-by mode more suitable. In contrast, the LEO provided stable fly-around observations with lower system requirements, but the fly-by mode suffered from high PRF demands and a low SNR due to the high relative angular velocity of the target. This study further simulated fly-by observations for actual space debris in both the GEO and LEO, validating ISAL’s performance under different conditions. These findings offer valuable insights into the selection of observation modes and the optimization of ISAL’s performance in on-orbit target and debris monitoring, serving as a foundation for future space-based monitoring systems.

Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model could distinguish between the original image and noise. The IR images lack texture and details. In IR images, the small target is hard to recognize. Traditional denoising methods based on nuclear norm minimization (NNM) treat all eigenvalues equally, which blurs the concrete details. They are unable to achieve a good denoising performance. Deep learning methods necessitate a large number of train images, which are difficult to obtain in IR image denoising. It is difficult to perform well under high noise in IR image denoising. Tracking and detection would not be possible without a proper denoising method. This article fuses the weighted nuclear norm minimization (WNNM) with an adaptive similar patch, searching based on the group sparse representation for infrared images. We adaptively selected similar structural blocks based on certain computational criteria, and we used the K-nearest neighbor (KNN) cluster to constitute more similar groups, which is helpful in recovering the complex background with high Gaussian noise. Then, we shrank all eigenvalues with different weights in the WNNM model to solve the optimization problem. Our method could recover more detailed information in the images. The algorithm not only obtains good denoising results in common image denoising but also achieves good performance in infrared image denoising. The target in IR images attains a high signal for the clutter in IR detection systems for remote sensing. Under common data sets and real infrared images, it has a good noise suppression effect with a high peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM), with higher noise and a much more complex background.