| 王安妮,单泽彪,刘小松,于艳鑫,苏成志.基于互质阵的分数阶累积量与虚拟插值DOA估计[J].电子测量与仪器学报,2026,40(5):60-68 |
| 基于互质阵的分数阶累积量与虚拟插值DOA估计 |
| DOA estimation based on fractional-order cumulants andvirtual interpolation using coprime arrays |
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| DOI： |
| 中文关键词: 波达方向估计 互质阵列 高斯色噪声 Alpha稳定分布噪声 分数阶累积量 |
| 英文关键词:DOA estimation coprime array Gaussian-colored noise Alpha-stable distributed noise fractional order cumulant |
| 基金项目:吉林省自然科学基金（20250102050JC）项目资助 |
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| Author | Institution |
| Wang Anni | School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China |
| Shan Zebiao | 1.School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China;
2.Jilin Provincial Collaborative Innovation Center for Intelligent Robots, Changchun University of Science and Technology,
Changchun 130022, China; 3.Jilin Provincial UniversityEnterprise Joint Technological Innovation Laboratory for Intelligent
Hybrid Robots, Changchun University of Science and Technology, Changchun 130022, China |
| Liu Xiaosong | School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China |
| Yu Yanxin | School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China |
| Su Chengzhi | 2.Jilin Provincial Collaborative Innovation Center for Intelligent Robots, Changchun University of Science and Technology,
Changchun 130022, China; 3.Jilin Provincial UniversityEnterprise Joint Technological Innovation Laboratory for Intelligent
Hybrid Robots, Changchun University of Science and Technology, Changchun 130022, China |
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| 中文摘要: |
| 为解决Alpha稳定分布噪声和高斯色噪声混合环境下波达方向（DOA）估计问题，提出了一种互质阵列下基于分数阶累积量的前向预测与回溯正交匹配追踪（FOC-LABOMP）算法。首先，通过互质阵列构建信号接收模型，利用阵元间距的差分共阵获得虚拟阵列，引入孔洞插值技术将物理阵列的有效孔径扩展至虚拟阵列的连续区间，从而提升自由度和提高角度分辨率；其次，利用分数阶累积量的半不变特性，有效抑制高斯色噪声和Alpha稳定分布噪声的干扰。进一步，结合前向预测与回溯正交匹配追踪算法，通过原子内积计算相关度，预测原子在未来迭代中的性能选择最佳原子，加入回溯策略，提高稀疏恢复精度，最终得到DOA估计值。通过计算机仿真实验验证了算法的有效性，在Alpha稳定分布和高斯色混合噪声条件下，当混合信噪比为0 dB时，所提算法DOA估计的均方根误差为0.536 8°，与相控分数低阶矩多重信号分类算法（PFLOM-MUSIC）相比，精度提升了41.45%。仿真结果表明所提算法可在混合噪声下实现较高精度的DOA估计。 |
| 英文摘要: |
| To address the problem of direction-of-arrival (DOA) estimation in environments with mixed Alpha-stable distributed noise and Gaussian colored noise, a forward prediction and backtracking orthogonal matching pursuit algorithm based on fractional-order cumulants (FOC-LABOMP) is proposed under a coprime array framework. First, a signal reception model is constructed using the coprime array, where the difference co-array formed by sensor spacings is utilized to generate a virtual array. By introducing a hole-filling interpolation technique, the effective aperture of the physical array is extended to the continuous region of the virtual array, thereby enhancing the degrees of freedom and improving angular resolution. Second, the semi-invariant property of fractional-order cumulants is leveraged to effectively suppress the interference from both Gaussian colored noise and Alpha-stable noise. Furthermore, the proposed method incorporates a forward prediction and backtracking orthogonal matching pursuit algorithm, which evaluates the correlation of atoms via inner products and predicts their performance in future iterations to select the optimal atom. A backtracking strategy is employed to improve the accuracy of sparse recovery, ultimately yielding the estimated DOA values. The effectiveness of the proposed algorithm was validated through computer simulation experiments. Under mixed noise conditions consisting of Alpha-stable distribution and colored Gaussian noise, when the mixed signal-to-noise ratio (SNR) is 0 dB, the root mean square error of the proposed algorithm for DOA estimation is 0.536 8 °, which improves the accuracy by 41.45% compared to the PFLOM-MUSIC algorithm. The simulation results fully demonstrate that the proposed algorithm can achieve high-accuracy DOA estimation under mixed noise conditions. |
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