LFM signal parameter estimation in the fractional Fourier domains: Analytical models and a high-performance algorithm

• Published in: Signal processing 2024-01, Vol.214, p.109224, Article 109224
• Main Authors: Aldimashki, Omair, Serbes, Ahmet
• Format: Article
• Language: English
• Subjects: Chirp rate; Chirp signals; Fractional Fourier transform; Golden section search; LFM parameter estimation
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2023.109224

The index of the peak magnitude of the fractional Fourier transform (FrFT) of linear frequency modulated (LFM) signals has been widely used as a powerful chirp-rate estimator. In this paper, we propose analytical approximations for the peak FrFT magnitude (PFM) of mono and multicomponent LFM signals, both for noiseless and noisy cases. In addition, we present a novel coarse-to-fine FrFT-based algorithm designed specifically for chirp-rate estimation of multi-component LFM signals. Our approach entails an initial coarse estimation of the chirp-rates for each component by utilizing our proposed mathematical models. By leveraging these models, we achieve improved performance and a reduced signal-to-noise breakdown threshold. Furthermore, we incorporate a unique and efficient estimate-and-subtract strategy to refine the estimated parameters using our proposed models. Rather than removing the components from the LFM signal, we utilize the derived model to identify and remove peaks in the PFM. This strategy enhances the algorithm’s capability to handle challenging scenarios. Extensive simulation results demonstrate that our proposed algorithm performs very close to the Cramér–Rao lower bound. It effectively eliminates the leakage effect between signal components, avoids error propagation, and maintains an acceptable computational cost compared to other state-of-the-art methods. •Analytical approximations for the peak FrFT magnitude of LFM signals are presented.•We propose a fine-to-coarse estimation approach, which uses our models in parameter estimation of multi-component LFM signals.•Simulations show that the algorithm performs very close to the Cramer–Rao bound.•The algorithm has a very low computational cost, compared to the other methods.