Due to the periodic and non-periodic variations in the sea surface, smearing is caused by the multiple paths between the sea surface and man-made objects in synthetic aperture radar images of harbor areas. This smearing can cover real targets and lead to false alarms. To derive the relationship between the motion of the sea surface and blurring in synthetic aperture radar images, a sway signal model is established, and the Doppler spectrum of the sea surface is found to undulate for well-focused targets with different shapes. Based on this finding, a subaperture combined detection algorithm based on an inverse coherence factor filter is developed to separate the unwanted pixels from the resultant synthetic aperture radar image. An energy balance is used to suppress interference and maintain the resolution of the real scene. The algorithm can be automatically applied to synthetic aperture radar images. The experimental results with TerraSAR-X spotlight mode data show that this method can effectively detect and mitigate the effects of the time-varying multipath phenomena.
Aiming at the congestion control problem of SDN, this paper designs a network congestion control scheme based on Q-learning algorithm of reinforcement Learning. According to the usage of link bandwidth in the network, the algorithm adjusts the transmission rate of the source data stream continuously, and finally obtains the optimal data stream allocation scheme. Experimental simulation evaluates the performance of the algorithm, the proposed control algorithm based on reinforcement learning congestion, can effectively improve the link utilization, reduce network congestion. The experimental results show that the congestion control algorithm proposed in this paper can allocate the optimal rate for each data stream, reduce network congestion, and has better performance than the traditional SDN algorithm.
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