Holographic Subsurface Radar Target Resolution under Strong Clutter Interference
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Abstract:
Unresolved targets are great challenges in radar detection. In this paper, machine learning is introduced into radar detection. The autoencoder is used to carry out unsupervised learning on how the targets are distinguished. The subspace decomposition and sparse representation methods are also combined to optimize the model, and the alternating direction multiplier method is used to solve the problem for precision and efficiency. The experimental results on holographic subsurface radar show that this method can effectively mitigate the strong clutter in radar images with the target images clear and integral. The signal-to-clutter ratio improvement of the proposed method is over 15 dB higher than that of other comparison method. Through experimental research, a promising solution is provided for the indiscernibility of targets in radar detection.