The simulation of high accuracy three-dimension (3D) scene optical field radiation distribution can provide the input for camera design, optimization of key parameters and testing of various imaging models. It can benefit for reducing the strong coupling between the imaging models and scene simulation. However, the simulation computation is extremely large and the non-optimization computing method can’t performed efficiently. Therefore, a study was carried out from the algorithm optimization and using high-performance platform to accelerate the operation speed. On the one hand, the visibility of scene was pre-computed which include the visibility from the light source to each facet in scene and the visibility between facets. The bounding box accelerate algorithm was adopted which can avoid a lot of time-consuming computation of occlusion in the light field radiation simulation process. On the other hand, since the 3D scene light field radiation simulation was obtained by a large number of light approximation, the algorithms can be divided blocks and processed parallelly. The GPU parallel framework was adopted to realize the simulation model of light field radiation in visible band. Finally, experiments were performed. The result shown the proposed method was more efficient and effective compared with the non-optimization method.
Adjacency effect could be regarded as the convolution of the atmospheric point spread function (PSF) and the surface leaving radiance. Monte Carlo is a common method to simulate the atmospheric PSF. But it can’t obtain analytic expression and the meaningful results can be only acquired by statistical analysis of millions of data. A backward Monte Carlo algorithm was employed to simulate photon emitting and propagating in the atmosphere under different conditions. The PSF was determined by recording the photon-receiving numbers in fixed bin at different position. A multilayer feed-forward neural network with a single hidden layer was designed to learn the relationship between the PSF’s and the input condition parameters. The neural network used the back-propagation learning rule for training. Its input parameters involved atmosphere condition, spectrum range, observing geometry. The outputs of the network were photon-receiving numbers in the corresponding bin. Because the output units were too many to be allowed by neural network, the large network was divided into a collection of smaller ones. These small networks could be ran simultaneously on many workstations and/or PCs to speed up the training. It is important to note that the simulated PSF’s by Monte Carlo technique in non-nadir viewing angles are more complicated than that in nadir conditions which brings difficulties in the design of the neural network. The results obtained show that the neural network approach could be very useful to compute the atmospheric PSF based on the simulated data generated by Monte Carlo method.
The vibration has an important influence on space-borne TDICCD imaging quality. It is generally aroused by an interaction between satellite jitter and attitude control. Previous modeling for this coupling relation is mainly concentrating on accurate modal analysis, transfer path and damping design, etc. Nevertheless, when controlling attitude, the coupling terms among three body axes are usually ignored. This is what we try to study in this manuscript. Firstly, a simplified formulation dedicated to this problem is established. Secondly, we use Dymola 2016 to execute the simulation model profiting Modelica synchronous feature, which has been proposed in recent years. The results demonstrate that the studied effect can introduce additional oscillatory modes and lead the attitude stabilization process slower. In addition, when fully stabilized, there seems time-statistically no difference but it still intensifies the motion-blur by a tiny amount. We state that this effect might be worth considering in image restoration.