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<p>This paper addresses the time-difference-of-arrival (TDOA) estimation problem of almost-cyclostationary signals under accelerated scenarios. First, the signal characteristics of fast-moving targets with constant relative acceleration are analyzed, and the generalized almost-cyclostationary (GACS) property of the received signals is systematically derived. It is worth noting that the cyclostationary frequency of the received signals is time-variant, following the variation pattern of a linear frequency-modulated (LFM) signal. Second, leveraging the distinct features in the cyclic frequency domain, an improved cyclic autocorrelation method based on the fractional Fourier transform (FrFT) is developed. This algorithm enables efficient exploitation of the almost-cyclostationary property across multiple cyclostationary frequencies, leading to a significant reduction in estimation errors. Finally, numerical simulations are performed to validate the effectiveness of the proposed method. The simulation results further demonstrate that it achieves superior performance compared with existing algorithms.</p>

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TDOA estimation of cyclostationary signal from fast moving target in accelerated scene

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月28日

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  • Sun ZhouHai,
  • Wang Zhangsheng

摘要

This paper addresses the time-difference-of-arrival (TDOA) estimation problem of almost-cyclostationary signals under accelerated scenarios. First, the signal characteristics of fast-moving targets with constant relative acceleration are analyzed, and the generalized almost-cyclostationary (GACS) property of the received signals is systematically derived. It is worth noting that the cyclostationary frequency of the received signals is time-variant, following the variation pattern of a linear frequency-modulated (LFM) signal. Second, leveraging the distinct features in the cyclic frequency domain, an improved cyclic autocorrelation method based on the fractional Fourier transform (FrFT) is developed. This algorithm enables efficient exploitation of the almost-cyclostationary property across multiple cyclostationary frequencies, leading to a significant reduction in estimation errors. Finally, numerical simulations are performed to validate the effectiveness of the proposed method. The simulation results further demonstrate that it achieves superior performance compared with existing algorithms.