FreqAF: A New Frequency Attention Fusion Spectral Estimation Method for Radar Super-Resolution Imaging

Abstract

Frequency estimation was a fundamental problem in radar imaging. The classical Fourier spectral analysis suffered from the Rayleigh limit. The imaging performance deteriorated rapidly in low SNR conditions. In addition, the prior knowledge on the number of signal sources was required. To solve the problems, a new data-driven spectral estimation method via frequency attention fusion (FreqAF) was proposed in this letter. Different from the preceding works, the signal spectral were estimated by a deep architecture neural network automatically. The echo signal was first dechirped according to the radar parameters. It is then fed into a deep architecture for spectral estimation. The proposed architecture was composed of three phases, the decomposition, the FreqAF, and the projection. In the decomposition phase, the individual single-frequency components were estimated from the input dechirped signal. The components were dynamically fused in a delicate FreqAF module. The frequencies were obtained finally in the projection phase. Numerical experiments are performed to verify the proposed method.