基于四阶累积量的改进传播算子DOA估计算法

    Improved Propagator Method for DOA Estimation Based on Fourth-order Cumulant

    • 摘要: 针对四阶累积量在多重信号分类(multiple signal classification, MUSIC)算法中计算复杂度高和在低信噪比情况下性能差的问题, 提出一种去冗余后平均的四阶累积量传播算子算法(modified fourth-order cumulant propagator method, MFOC-PM)。该算法通过引入传播算子算法线性地获得噪声子空间, 避免了特征值分解造成的计算量, 然后在保持四阶累积量阵列孔径扩展和抑制高斯噪声的优势下, 通过求平均后去除的方法将四阶累积量中冗余的部分去除, 降低了算法的复杂度。实验结果表明, 所提算法显著减少了算法的计算量, 可以实现角度的精确估计, 在低信噪比的情况下表现良好, 具有较好的实用价值。

       

      Abstract: Aiming at the problem that the fourth-order cumulant has high computational complexity in multiple signal classification (MUSIC) and poor performance under low signal-to-noise ratio conditions, a modified fourth-order cumulant propagator method (MFOC-PM) was proposed. By introducing the propagation operator algorithm to obtain the noise subspace linearly, the algorithm avoided the computation caused by eigenvalue decomposition. To maintain the advantage of the aperture expansion of the fourth-order cumulant array and suppress Gaussian noise, the redundant part of the fourth-order cumulant was removed by the method of averaging and then removing. The experimental results demonstrate that the effectiveness and feasibility of the proposed MFOC-PM algorithm, which significantly reduces the computational complexity of the algorithm. Specifically, it achieves accurate angle estimation and performs well under low signal-to-noise ratio conditions. This indicates that the algorithm has significant practical value and can meet the requirements of DOA estimation in many practical application scenarios.

       

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