Detection and parameter estimation of quadratic frequency modulated signal based on non-uniform quadrilinear autocorrelation function

YANG Yuchao; FANG Gang

doi:10.11999/JEIT250723

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YANG Yuchao, FANG Gang. Detection and parameter estimation of quadratic frequency modulated signal based on non-uniform quadrilinear autocorrelation function[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250723

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YANG Yuchao, FANG Gang. Detection and parameter estimation of quadratic frequency modulated signal based on non-uniform quadrilinear autocorrelation function[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250723

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Detection and parameter estimation of quadratic frequency modulated signal based on non-uniform quadrilinear autocorrelation function

doi: 10.11999/JEIT250723 cstr: 32379.14.JEIT250723

YANG Yuchao^,,
FANG Gang

Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China

Funds: 无

Accepted Date: 2026-01-12
Rev Recd Date: 2026-01-12

Available Online: 2026-01-27

Abstract

Abstract

Objective Polynomial phase signal (PPS) analysis has been a research field of interest in recent decades due to the fact that many signals in radar, sonar, and seismic are usually modeled as a PPS of different orders based on research needs. First-order PPS can be quickly focused to a frequency bin through Fourier transform, thereby obtaining the estimation of center frequency. However, for high-order PPS, e.g., quadratic frequency modulated signal, due to its non-coherent characteristic, conventional Fourier transform is insufficient for energy integration. Existing time-frequency distribution method, e.g., short-time Fourier transform and Wigner-Ville distribution fail to effectively address the conflicts between auto-terms and cross terms, as well as time-domain resolution and frequency-domain resolution. Furthermore, existing algorithms struggle to strike a balance between computational complexity and signal detection performance. These issues result in poor parameter estimation performance of existing methods. This research proposes a QFM signal detection methods based on non-uniform sampling autocorrelation function, aiming to provide a balanced solution in the application of parameter estimation of QFM signal, considering both computational cost and detection performance. Methods This research presents a time-frequency distribution method for the QFM signal detection and parameter estimation. This method utilizes non-uniform sampling technology and maps one-dimensional signal into a two-dimensional time domain through a novel forth-order autocorrelation function. Subsequently, non-uniform fast Fourier transform is employed to resolve the time variable, and integrates the signal energy into a vertical line on a two-dimensional plane. Then, through FFT along this line, the signal is focused into a peak, and the estimation of chirp rate and quadratic chirp rate can be obtained based on peak position. Finally, de-chirp processing compensates the high-order phase terms of the original signal, and the center frequency parameter estimation is obtained through FFT. Results and Discussions Theoretical analysis and simulation data demonstrate that the proposed method achieves a balance between computational complexity and signal detection performance. In low signal-to-noise ratio scenario, the proposed method can effectively distinguish targets and achieve accurate parameter estimation (Fig. 1). For multi-component signal with significant amplitude differences, the proposed method can accomplish detection and estimation by steps (Fig. 2). Comparative experiments with mainstream state-of-the-art algorithms show that the proposed method is quasi-optimal in terms of estimation accuracy and integration gain (Fig. 3, Fig. 4, Fig. 5 and Fig. 6). However, compared to the ML best estimator, the proposed method significantly improves computational efficiency. Conclusions This paper proposes a method based on non-uniform sampling autocorrelation functions for QFM signal detection and parameter estimation. The method maps QFM signal into a two-dimensional time domain through a novel autocorrelation kernel function and achieves coherent integration through scaling and FFT. Mathematical analysis and simulation experiments demonstrate that, compared to ML method with optimal detection capabilities, the proposed method sacrifices a portion of detection performance to significantly reduce computational complexity. Furthermore, when computational efficiency is similar, the proposed method outperforms other classical methods in terms of signal detection and parameter estimation performance. Overall, this paper provides a balanced solution for QFM signal detection and parameter estimation.
- time-frequency analysis,
- quadratic frequency modulated signal,
- non-uniform sampling technology,
- autocorrelation function

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References(28)

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