Juan Chen

The distributed space-based coherent aperture radar (DSCAR) leverages the collaborative operation of multiple satellite-borne radars to transcend the power-aperture product limitations inherent to single radar systems, theoretically achieving detection performance on par with that of a single large-aperture antenna radar. To address the shortcomings of one-dimensional linear formations, notably low spatial utilization efficiency and insufficient topological compactness, this paper proposes a DSCAR model based on a uniform solid circular formation (USCF) configuration. Confronting the challenge that aperiodic geometric distribution methods struggle to effectively suppress grating lobe in USCF configuration, this paper introduces a multi-frequency pattern synthesis method coupled with an accelerated convergence subtraction-average-based optimizer (SABO) algorithm to search for the peak side lobe level (PSLL) global optimum. Simultaneously, to resolve the original SABO algorithm's susceptibility to local optima convergence, we propose an improved SABO algorithm that initializes population distribution using Fuchs chaotic mapping and updates population positions via the golden sine algorithm (GSA), thereby achieving PSLL global optimum. Simulation results demonstrate that the improved SABO algorithm holds a marginal advantage over multiple metaheuristic algorithms on benchmark test functions, delivers nearly 1dB average improvement in PSLL global optimum compared to the original SABO algorithm, and exhibits progressively greater enhancement with increased frequency counts or expanded frequency ranges, ultimately providing an effective parametric optimization solution for DSCAR grating lobe suppression with significant reference value.

For the issue of configuration difficulty in maintaining linear formations based on the same orbital plane for distributed space‐based coherent aperture radar (DSCAR), it is necessary to modify the linear formation model into an arc formation model. This article derives the steering vector and joint pattern expressions for DSCAR based on uniform arc formation, and designs a segmented inertial factor (IF) particle swarm optimization (PSO) to seek the optimal solution for non‐uniform spacing and random yaw angle in non‐periodic geometric distribution. Simulation analysis shows that the combination of non‐uniform spacing and random yaw angle in non‐periodic geometric formations can achieve lower peak side lobe level (PSLL) compared to single non‐uniform spacing and single random yaw angle but with wider beamwidth spread. Additionally, the segmented IF PSO proposed in this article balances convergence more quickly in the early stage of the search process and improves convergence speed to approach the optimal value (OV) in later stage. Compared with other IF PSO, it has better convergence speed and accuracy.

Space vehicles such as missiles and aircraft have relatively long tracking distances. Infrared (IR) detectors are used for small target detection. The target presents point target characteristics, which lack contour, shape, and texture information. The high-brightness cloud edge and high noise have an impact on the detection of small targets because of the complex background of the sky and ground environment. Traditional template-based filtering and local contrast-based methods do not distinguish between different complex background environments, and their strategy is to unify small-target template detection or to use absolute contrast differences; so, it is easy to have a high false alarm rate. It is necessary to study the detection and tracking methods in complex backgrounds and low signal-to-clutter ratios (SCRs). We use the complexity difference as a prior condition for detection in the background of thick clouds and ground highlight buildings. Then, we use the spatial domain filtering and improved local contrast joint algorithm to obtain a significant area. We also provide a new definition of gradient uniformity through the improvement of the local gradient method, which could further enhance the target contrast. It is important to distinguish between small targets, highlighted background edges, and noise. Furthermore, the method can be used for parallel computing. Compared with the traditional space filtering algorithm or local contrast algorithm, the flexible fusion strategy can achieve the rapid detection of small targets with a higher signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF).

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.