Abstract
Feature extraction and recognition of micromotion targets based on the joint time-frequency (JTF) analysis has received extensive attention in the field of radar imaging and automatic target recognition in recent years. Compressed sensing theory uses the sparsity of the radar signal JTF representation to solve the problems of low resolution and cross terms caused by traditional JTF analysis methods. However, the available methods mostly use high-dimensional non-parametric dictionaries, and when the number of samples of the signal is large, the calculation complexity is relatively high. To solve this problem, this paper constructs a single-window JTF model, which effectively reduces the dimension of non-parametric dictionaries. Then, a weighting term is proposed, which can extract the time-frequency ridges and enhance the weaker components in the JTF distribution. Finally, the Alternating Direction Multiplier Algorithm (ADMM) is used to solve the optimal sparse JTF distribution. The validity of the method has been proved by simulated data.
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