Parameter estimation of multi-component LFM signals based on super-resolution generative adversarial network

In this paper, we present a new method for parameter estimation of multi-component linear frequency modulated (LFM) signals, using fractional Fourier transform (FrFT) and an improved super-resolution generative adversarial network (SRGAN). Different from existing estimation methods, the proposed method innovatively transforms parameter estimation into image processing, and effectively overcomes the impact of spectral overlap in FrFT domain by performing high-resolution reconstruction of signal peaks. This method initially maps multi-component LFM signals into a two-dimensional FrFT domain with coarse-step FrFT, in which each component exhibits a distinct energy peak. SRGAN-based image super-resolution (SR) is then introduced to perform denoising and high-resolution reconstruction on the low-resolution FrFT spectrum. Finally, the signal parameters are estimated using the peak locations in the high-resolution FrFT spectrum. Considering that conventional SRGAN mainly focuses on improving image perceptual quality, directly applying SR to the FrFT spectrum makes it difficult to recover peak features. To address this issue, we incorporate attention mechanisms into the network architecture to enhance signal energy concentration at peak locations. Meanwhile, an estimation loss is defined to avoid peak shifts and spurious peaks in the SR results. Extensive simulations demonstrate that the proposed method provides superior estimation accuracy and exhibits strong noise robustness.