Simulation of rocket plume infrared images by computer programs is an important method to study their infrared radiation characteristics. In order to improve the computation efficiency, a simulation method based on CUDA was proposed. Firstly, the characteristics of the LOS (Line of Sight) method in radiative transfer calculation were analyzed. All the path points of LOS method were calculated in advance, and the parallel level was promoted to the number of path points of LOS method and bands of spectrum. In this case, more threads could be mobilized at the same time, and the utilization of GPU was improved. Secondly, A hardware bilinear interpolation method using CUDA texture memory was proposed, which greatly improved the calculation efficiency of physical parameters of the components. Finally, CUDA was also used for acceleration in the projection imaging module of spaceborne infrared sensor. The simulation results show that using CUDA for parallel computing to realize physical parameters search and projection imaging can greatly improve the overall simulation efficiency of rocket plume infrared images.
Rocket engine exhaust plume produces a strong infrared radiation signal and is widely used for target diagnosis and detection. The traditional method for calculating the infrared radiation characteristics of the exhaust plume is difficult and time-consuming. In this paper, the engineering analytical method in the band is used to calculate the equivalent spectral radiation intensity of the exhaust plume, and the spectral radiation intensity varies with the viewing angle. The relationship curve constructs the spectral radiance intensity as a function of the viewing angle. This method does not just take advantage of the efficiency of the engineering analysis method, but also preserves the accuracy of numerical simulation. The spatial distribution of the infrared radiation intensity field in the typical band of the exhaust plume is simulated by an example.
Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).
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