The blue-green laser, which is attenuated less in water, are of potential applications in the deep seawater communication. The Orbital Angular Momentum (OAM) as a new degree of freedom with characteristic of unlimited topological charge, is expected to extensive improve the capacity of blue-green laser communication. In seawater channel the turbulence is the main factor to influence laser transmission, thus, it is important to research the effect of seawater turbulence on OAM blue-green laser. In this paper, based on power spectrum method the phase screen effected by turbulence is studied, and the transmission model for OAM LG laser in the presence of seawater turbulence is established. For typical wavelength of 532 nm, the received laser power and phase is analyzed with different transmitted distance and beam waist. Mathematical simulation shows that, the smaller the beam waist is, the better the OAM spiral phase matins and the stronger the received laser power is, since the turbulence influence is less, which means the smaller beam waist obtains obvious advantages. The results can benefit the transmission characteristic analysis and modulation for underwater blue-green laser channel research and provide foundation for system optimum design.
In the rocket recovery environment, the imaging conditions are complicated, and the vision equipment is prone to problems such as ground target information loss, image contrast and saturation reduction, color deviation and so on, which directly affect the solution of the rocket pose. Therefore, it is necessary to study the de-fog algorithm for the special environment of rocket recovery, so as to contribute to the solution of rocket pose and altitude measurement. Through the investigation of previous research on de-fogging algorithms, it can be found that the dark channel de-fogging algorithm is not only simple in principle but also has better de-fogging effect in changeable environment. In addition, the brightness of fog in rocket recovery is often affected by the flame emitted from the nozzle, and a single dark channel de-fogging algorithm cannot adjust the overall brightness according to the environmental characteristics, resulting in blurred feature points in the image after de-fogging. Therefore, considering that RETINEX theory can be used to adjust the brightness of images, in combination with the dark channel prior model, this paper mainly uses RETINEX theory to improve the dark channel de-fogging theory to process the transmission t(x), so that the final image retains the feature information to the maximum extent, and improves the adaptability of the dark channel de-fogging algorithm to different fog environments. Finally, the removal effect of different de-fogging algorithms on different concentrations of fog in rocket recovery environment is quantitatively analyzed. The experimental results show that the de-fogging algorithm in this paper can solve the impact of the fog generated by the rocket nozzle on the vision equipment.
KEYWORDS: Clouds, Computer simulations, Nickel, Detection and tracking algorithms, Data modeling, 3D modeling, Space operations, Sensors, Information visualization, Global Positioning System
Many space tasks, such as on-orbit servicing, space rendezvous and docking strongly rely on accurate relative position and posture collectively (referred to as pose) of spacecraft. The single measurement methods are limited to their respective advantages and disadvantages, which cannot meet the demand of non-cooperative target pose measurement in complex space situations. But due to different information sources, multi-source heterogeneous point cloud registration algorithms face problems such as noise impact, outliers, partial overlap of point clouds, difference in density of point clouds, inconsistent scales and so on. To solve this problem, based on the Scaling Iterative Closest Point (SICP) algorithm, this paper proposes a high-precision registration algorithm of variable scale heterogeneous point clouds based on intrinsic shape signatures (ISS) features. Firstly, the algorithm down samples the voxels of the point cloud to be aligned, sparseness the number of point clouds, and screens out some noise; Secondly, coarse alignment of the feature point clouds extracted by the ISS algorithm is performed by establishing a cost function containing the surface variance of the feature points and the module value of the Euclidean distance of the feature points from their centers of mass; Finally, the two-point clouds are finely aligned by the SICP algorithm. The experimental results show that the algorithm shows high robustness and well real-time performance, and can realize the accurate registration of multi-source heterogeneous point clouds with multi scales.
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