| 基于导向矢量双层估计和协方差矩阵重构的稳健波束形成算法 |
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| 引用本文： | 吕岩, 曹菲, 杨剑, 冯晓伟. 基于导向矢量双层估计和协方差矩阵重构的稳健波束形成算法[J]. 电子与信息学报, 2022, 44(12): 4159-4167. doi: 10.11999/JEIT211120 |
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| 作者姓名： | 吕岩 曹菲 杨剑 冯晓伟 |
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| 作者单位： | 1.火箭军工程大学核工程学院 西安 710025;;2.中国人民解放军 96746部队 库尔勒 841000;;3.火箭军工程大学导弹工程学院 西安 710025 |
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| 基金项目： | 国家自然科学基金(62071481)，国家青年科学基金(61903375, 61501471) |
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| 摘 要： | 针对干扰加噪声协方差矩阵(INCM)重构过程中Capon功率谱(CPS)估计分辨率低的问题，该文提出两种稳健自适应波束形成（RAB）算法。该算法首先通过搜索CPS的峰值确定积分区间，然后对各区间积分所得的协方差矩阵进行特征值分解。通过合理设置判定门限确定区间内所含的入射信源数量，并将较大特征值所对应的特征向量作为信源导向矢量(SV)的初步估计。而后通过最大化估计功率的方法，在初步估计SV的正交空间内搜索其与真实SV之间的误差。该算法1利用最小特征值所对应的特征向量，向初步估计的SV中添加正交比例梯度，得到双层估计的SV。与算法1不同，算法2通过求解2次优化(QP)问题得到修正的SV。最后通过重构INCM获得阵列最优权值矢量。通过计算机仿真实验，验证了所提算法有效解决了CPS估计分辨率低的问题，较其他算法综合性能更优，具备更高的稳健性。
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| 关 键 词： | 稳健自适应波束形成 协方差矩阵重构 导向矢量估计 Capon功率谱估计 |
| 收稿时间： | 2021-10-13 |
| 修稿时间： | 2021-12-18 |
Robust Beamforming Algorithm Based on Double-layer Estimation of Steering Vector and Covariance Matrix Reconstruction |
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| LÜ Yan, CAO Fei, YANG Jian, FENG Xiaowei. Robust Beamforming Algorithm Based on Double-layer Estimation of Steering Vector and Covariance Matrix Reconstruction[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4159-4167. doi: 10.11999/JEIT211120 |
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| Authors: | Lü Yan CAO Fei YANG Jian FENG Xiaowei |
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| Affiliation: | 1. Nuclear Engineering College, Rocket Force University of Engineering, Xi’an 710025, China;;2. Unit 96746 of PLA, Korla 841000, China;;3. Missile Engineering College, Rocket Force University of Engineering, Xi’an 710025, China |
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| Abstract: | Considering the problem of the low resolution of the Capon Power Spectrum (CPS) in the reconstruction of Interference plus Noise Covariance Matrix (INCM), two Robust Adaptive Beamforming (RAB) algorithms are proposed. The proposed algorithm first searches the peaks of CPS to determine the integration intervals and then eigen-decomposes the covariance matrixes obtained from the integration of each interval. The number of incident sources in the interval is determined by reasonably setting the decision threshold, and the eigenvectors corresponding to the larger eigenvalues are used as the preliminary estimation of the Steering Vectors (SV). Then, by maximizing the estimated power, the gap between the nominal SV and the real SV is searched in the orthogonal space of the nominal SV. The first proposed algorithm uses the eigenvector corresponding to the minimum eigenvalue to add the orthogonal proportional gradient to the initial estimated SV to obtain the double-layer estimated SV. The second proposed algorithm obtains the modified SV by solving a Quadratic Programming (QP) problem. Finally, the optimal weight vector of the array is obtained by reconstructing the INCM. Simulation results demonstrate that the proposed algorithm solves effectively the problem of the low resolution of the CPS estimation and is superior to other algorithms. |
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| Keywords: | Robust Adaptive Beamforming(RAB) Covariance matrix reconstruction Steering Vector(SV) estimation Capon Power Spectrum(CPS) estimation |
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