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基于VMD-FastICA算法的三元阵来波方位估计

Direction of Arrival Estimation Using Triad Array Based on VMD-FastICA

  • 摘要: 水下目标测向一直是拖曳线列阵的重点研究方向。相较于波束形成方法,基于到达时间差(time difference of arrival, TDOA)方法的三元线列阵测向原理更简单且三元线列阵成本更低。然而,在低信噪比条件下,三元线列阵对目标的测向精度不佳。针对这一问题,本文提出了一种基于变分模态分解(variational modal decomposition, VMD)与快速独立成分分析(fast independent component analysis, FastICA)的联合优化策略,并结合功率谱熵阈值动态筛选IMF分量对接收信号降噪,克服了传统方法因固定阈值导致信号失真的问题,从而提高对目标测向的精度。与传统类EMD方法相比,VMD-FastICA在仿真中表现出更强的鲁棒性。在50次仿真实验中,当目标信号为窄带或变频信号且信噪比为0 dB时,经其降噪后再使用三元阵测向均值误差在0.2°以内,单次试验误差最大为1.58°。

     

    Abstract: Underwater target direction finding remains a critical research focus for towed linear arrays. While Time Difference of Arrival (TDOA) methods using three-element arrays offer simpler implementation and lower costs compared to conventional beamforming, their direction-finding accuracy significantly deteriorates under low Signal-to-Noise Ratio (SNR) conditions. To address this limitation, this paper proposes a joint optimization strategy combining Variational Mode Decomposition (VMD) and Fast Independent Component Analysis (FastICA), incorporating dynamic Intrinsic Mode Function (IMF) selection through power spectral entropy thresholding. This approach eliminates signal distortion caused by fixed thresholds in traditional methods while effectively enhancing denoising performance for received signals. Compared with traditional EMD-based methods, VMD-FastICA demonstrates stronger robustness in simulations. In 50 simulation experiments, when the target signal is a narrowband signal or a frequency-modulated signal and the signal-to-noise ratio is 0 dB, the mean direction-finding error using a three-element array after VMD-FastICA denoising is within 0.2°, and the maximum error in a single experiment is 1.58°.

     

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