The characteristics of space targets are the basis of detecting and identifying space targets. The size, shape and material of the space target are closely related to its scattering characteristics. Starting with the three typical influencing factors of dimensional accuracy, shape and material, this paper designed a typical simple body and assembly model with 15% dimensional accuracy, different shapes and different materials, and analyzed the influence of dimensional accuracy, shape and material on scattering characteristics according to the calculation results of the model's omnidirectional luminosity. The research results show that, when the size deviation is less than 15%, it has little effect on the scattering characteristics of the target. When the size is equivalent, the influence of different shapes on the scattering characteristics of the target is not significant, and the surface material has the greatest influence on the scattering characteristics of the target.
Aiming at the serious non-uniformity of infrared hyperspectral data, a non-uniformity correction method based on spectral channel is proposed.This method not only corrects the non-uniformity of each spectral channel image, but also corrects the non-uniformity between spectral channels. By comparing the traditional two-point correction method with the proposed method in this paper, it shows that the method proposed in this paper can not only effectively remove the non-uniformity of each spectral image, but also retain the original spectral curve characteristics of the target, which verifies the effectiveness of the proposed method in this paper.
The main problem with the current radar target radial length extraction algorithm is its susceptibility to interference signals, which makes it difficult to optimize the boundaries of the target support area, especially the impact of noise at positions that tend to be farther away from the target support. Therefore, based on deep learning networks, this article trains and analyzes different pixel points, completes the segmentation of the target support area and background area through image semantic segmentation algorithms, obtains the target support area, and estimates the radial length of the target based on the boundary of the target support area. Finally, validate the effectiveness of the algorithm using simulation data.
In the process of target re-entry, since the target enters the atmosphere at a high speed, the violent friction between the target and the atmosphere generates aerodynamic heat, which makes the target surface heat up rapidly and generates a strong radiation signal. It is an important factor in the target detection process. In order to calculate the surface temperature caused by the aerothermal effect quickly, a rapid prediction model of temperature characteristics is proposed in this paper. Based on the temperature of stagnation point and the surface temperature distribution obtained by the summary analysis with accurate simulation calculation, The surface temperature distribution characteristics during the target re-entry process can be estimated quickly.
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