In order to study the optical properties of marine aerosols, the optical thickness, wavelength index, spectral distribution, refractive index, single scattering albedo and elevation of aerosol particles were measured by means of solar radiometer, micro pulse lidar and automatic weather station in Qingdao, South China Sea, East China Sea and South China Sea from July to November 2019. The results show that the aerosol optical thickness measured by the shipborne data is smaller than that measured by Qingdao and islands, and the aerosol elevation range is about 0.4-0.7, The diurnal variation is relatively stable, and the distribution of aerosol colloidal product spectrum in the offshore and the open sea has the same change trend. The radius of the coarse mode is about 2.4 μm-3.6 μ M. compared with other data, the real part of the refractive index is larger, the imaginary part is smaller, and the difference in the long wave band is more obvious. The single scattering albedo basically does not change with the wavelength.
As a hot research direction in the field of communication reconnaissance, signal modulation classification plays an increasingly important role in the field of national defense. Traditional signal modulation style classification methods are mostly based on the combination of feature engineering and pattern recognition. First, the expert features are extracted by manual design, and then the signal modulation is recognized by the pattern recognition algorithm. The limitation of this method is that an expert feature can only effectively identify a few specific modulation signals. Besides, the deficiency of the number of expert features will lead to a low classification accuracy. In order to improve the accuracy of signal modulation classification, a signal modulation classification algorithm based on convolutional neural network is proposed. Convolutional neural networks can achieve end-to-end classification without manually designing and extracting features. Convolutional neural networks can automatically extract various levels of abundant features through learning, which can improve classification accuracy. The convolutional neural network architecture designed in this paper includes: three convolutional layers, three pooling layers, and the last layer is the softmax classification layer, which outputs the classification results. Experimental results show that on a data set containing 32 I/O signals, when the signal-to-noise ratio is 6dB, the algorithm has a training accuracy rate of 92.7% and a test accuracy rate of 90.2%.
Selecting the solar radiometer and ground-based laser-radar data of several typical regions, we could invert the laser-radar ratio of each region by using the entire aerosol optical thickness measured by the solar radiometer as a constraint, and we conducted vertical aerosol distribution observation research based on this. The average laser-radar ratio in the four regions of Delingha Qingha(i spring), Hefei Anhu(i summer), Zhangye Gansu (summer), and Maoming Guangdong(winter)are 38, 62, 47, and 17 respectively. Comparing with selecting fixed LR,the extinction tends to change with the height approximately similarly,but the values of the extinction at different heights are different evenly.The vertical distribution of aerosols in different spatial and temporal distribution characteristics.
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