An efficient method for parameter estimation and separation of multi-component LFM signals

• Published in: Signal processing 2023-06, Vol.207, p.108964, Article 108964
• Main Authors: Lu, Zhenkun, Liu, Shaohang, Qiu, Ji, Huang, Qinghua, Yang, Cui
• Format: Article
• Language: English
• Subjects: Fractional fourier transform; Multi-component LFM signals; Parameter estimation; Signal separation
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2023.108964

•The proposed method effectively reduces the number of redundant calculations.•A reasonable search strategy is formulated.•An efficient method based on FRFT domain filter is proposed to separate the multi-component LFM signal.•The CRLB is derived for the unbiased estimation error of LFM signal parameters.•The estimation of parameters is achieved with high accuracy even in low SNR. In this paper, an efficient and robust algorithm for fast parameter estimation and separation of multi-component LFM signals is proposed, which is suitable for processing overlapping and intersect LFM signals in the time-frequency plane. First, the key innovative idea of the algorithm is to use the fourth-order origin moment of fractional Fourier transform spectrum to formulate a reasonable search strategy for the optimal rotation order to achieve parameter estimation, and propose a step-by-step filtering technique for signals in the fractional Fourier domain achieve the signal separation we are interested in. Then, the proposed algorithm overcomes the problems of low estimation accuracy and large computational of traditional two-dimensional search and time-frequency analysis methods by virtue of good impulse characteristics and anti-noise performance. Different from existing solutions, the method avoid interference problems between multiple signals with close time-frequency distance, overlapping and cross components. Moreover, the error analysis of the linear frequency modulation signal is carried out, and the Cramer-Rao lower bound for the unbiased estimation of the parameters is derived. Finally, the simulation results demonstrate the accuracy and effectiveness of the proposed method in noisy environments by comparing with traditional methods and existing methods.