Method for estimating frequency modulation rate of linear frequency modulation signal

Abstract

The invention discloses a method for estimating the frequency modulation rate of a linear frequency modulation signal, which comprises the following steps of: 1, performing discrete polynomial transformation on a received multi-component linear frequency modulation signal to obtain a mixed signal m(t) of a complex sinusoidal signal containing frequency modulation rate information and the linear frequency modulation signal; 2, constructing a Hankel matrix for the mixed signal, and then separating a complex sinusoidal signal through singular value decomposition; and 3, carrying out cycle estimation on the complex sinusoidal signal, then carrying out whole-cycle truncation, carrying out discrete Fourier transform to obtain the frequency, and finally obtaining the frequency modulation rate of each component. According to the method, each frequency modulation rate of the multi-component linear frequency modulation signal can be accurately estimated, and compared with an existing estimation method for the frequency modulation rate of the multi-component linear frequency modulation signal, the method has the advantage that the signal-to-noise ratio threshold can be reduced while the small calculation complexity is kept. The method is suitable for a multi-component scene containing strong noise.

Description

technical field [0001] The invention belongs to the field of linear frequency modulation signal parameter estimation, and relates to a method for estimating frequency modulation of linear frequency modulation signals, in particular to a method for estimating frequency modulation of multi-component linear frequency modulation signals by using discrete polynomials and singular value decomposition. Background technique [0002] Linear frequency modulated signal (LFM) is widely used in radar, satellite communication, assisted driving and other fields. Its estimation problem has always been an important content of LFM processing, especially the parameter estimation of multi-component linear frequency modulated signal has higher use value. As a key parameter of LFM, modulation frequency has always been a research hotspot of scholars in related fields. [0003] The classic time-frequency domain estimation method is an important idea for estimating the parameters of linear frequency...

AI Technical Summary

Problems solved by technology

STFT-based methods always have a contradiction between frequency and time resolution; WVD is difficult to deal with multi-component cross-terms and high computational complexity limits its practical application; WH-based methods require prior information of unknown parameters, and the anti-noise performance Poor; FrFT has unique advantages for linear frequency modulation signal processing, but its optimal order search greatly increases the computational complexity, especially in multi-component scenarios, the amount of calculation increases exponentially

The analysis method based on the center frequency-modulation frequency (Centroid Frequency-Chirp Rate, CFCR) domain reduces the amount of calculation, but the anti-noise performance decreases in multi-component scenarios

[0004] In summary, the existing transform domain-based parameter estimation methods have poor estimation accuracy in multi-component chirp signal scenarios, or cannot maintain excellent performance under low signal-to-noise ratios, and strong noise environments are very common. Therefore, in ensuring Under the premise of low computational complexity, it is of great significance to further reduce the anti-noise threshold of the parameter estimation method

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