Time-Frequency Feature Enhancement of Moving Target Based on Adaptive Short-Time Sparse Representation

Accurate time-frequency (TF) feature extraction of moving target is a challenging task due to the poor resolution and serious cross-terms of the conventional TF analysis (TFA) methods. In this letter, an effective TFA algorithm based on the adaptive short-time sparse representation (ASTSR) is proposed to enhance the TF feature of moving target. First, the limitation of the Fourier-transform-based short-time TFA is revealed from the motion approximation perspective. Then, to achieve accurate motion approximation, the width of the analysis window is determined adaptively by minimizing the bandwidth of each short-time signal individually. Finally, TF representation (TFR) with high energy concentration is obtained using the sparsity of these signal segments in the chirp dictionary. Comparisons indicate that the ASTSR provides high-resolution TFRs without producing interference terms at an acceptable computational cost while performing well in weak component expressing and signal denoising. Furthermore, an ISAR imaging example confirms the potential of the proposed method.

Automated summary

This letter proposes an effective time-frequency analysis algorithm based on adaptive short-time sparse representation (ASTSR) to enhance the TF feature of moving targets. The algorithm achieves accurate motion approximation by adaptively determining the width of the analysis window, resulting in high-resolution TF representations with high energy concentration. The algorithm performs well in weak component expressing and signal denoising without producing interference terms.