KEYWORDS: Interference (communication), Mathematical modeling, Background noise, Signal processing, Atmospheric modeling, Telecommunications, Signal to noise ratio, Modeling, Monte Carlo methods, MATLAB
Noise and interference are the main factors affecting high-frequency channels, and it is important to generate noise and interference that match the actual high-frequency channel characteristics when modeling high-frequency channels. To address the noise characteristics in wide-band high-frequency channels, we construct an Alpha-Stable distribution-based noise and interference model for wide-band high-frequency channels based on the analysis of Alpha-Stable distribution theory. Simulation results show that our model not only generates noise that is more consistent with the actual wide-band high-frequency channel but also effectively reduces the complexity of the current state-of-the-art wide-band high-frequency channel noise model. In order to better simulate the noise characteristics of the wide-band high-frequency channel, swept-frequency interference is added to our model, and the simulation results show that the wide-band high-frequency channel model with swept-frequency interference can better simulate the noise characteristics of the real wide-band high-frequency channel.
This paper proposes a warship image segmentation algorithm based on Mask RCNN network. Based on the Tensorflow+ Keral deep learning framework, the Mask-RCNN network structure was constructed. The segmentation of the image of warship at sea level was achieved by using the supervised learning method and tagging of the data set. Mask R-CNN is the most advanced convolutional neural network algorithm, which is mainly used for object detection and object instance segmentation of natural images. Due to the difficulty in obtaining warship samples and the insufficient number of data sets, the method of data enhancement is adopted to expand the data set. Through parameter adjustment and experimental verification, the mAP of warship reaches 0.603, which can meet the requirements of high-precision segmentation. The experimental results show that the Mask RCNN model has a very good effect on the image segmentation of naval ships at sea.
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