  Bilinear time-frequency representations (TFRs) provide high-resolution
time-varying frequency characteristics of nonstationary signals. However, they
suffer from crossterms due to the bilinear nature. Existing crossterm-reduced
TFRs focus on optimized kernel design which amounts to low-pass weighting or
masking in the ambiguity function domain. Optimization of fixed and adaptive
kernels are difficult, particularly for complicated signals whose autoterms and
crossterms overlap in the ambiguity function. In this letter, we develop a new
method to offer high-resolution TFRs of nonstationary signals with crossterms
effectively suppressed. The proposed method exploits a deep convolutional
neural network which is trained to construct crossterm-free TFRs. The
effectiveness of the proposed method is verified by simulation results which
clearly show desirable autoterm preservation and crossterm mitigation
capabilities. The proposed technique significantly outperforms state-of-the-art
time-frequency analysis algorithms based on adaptive kernels and compressive
sensing techniques.
