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.

The infrared detector is adopted to track and recognize targets, including missiles and airplanes. These targets are usually far from the detector. The pixel size is less than $9\times 9$ when the image has $256\times 256$ pixels. They are regarded as the IR small targets. Because of the complex space environment, the high-brightness clouds and noise have a bad effect on the real small target detection. We need to focus on the tracking and detection of a small target in IR images in the intricate background. This article utilizes the Laplacian of Gaussian (LOG) filter and an improved local contrast method to reduce the impact of the high-light corner and strip. Compared with traditional space filtering, frequency domain technology, or the single local contrast method, we can greatly improve the signal clutter ratio gain (SCRG) and background suppression factor (BSF) in the real complex background in IR image processing. Under the same false alarm rate, our method achieves a better detection rate.

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).

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.