面向混合远近场场景的特征空间基变换稳健自适应波束形成

陈鹏; 章倩; 刘耀明; 胡欣; 王威

doi:10.11999/JEIT260968

面向混合远近场场景的特征空间基变换稳健自适应波束形成

doi: 10.11999/JEIT260968 cstr: 32379.14.JEIT260968

陈鹏^1,,
章倩¹,
刘耀明¹,
胡欣^2, ,,
王威¹

1.
长安大学信息工程学院西安 710064
2.
长安大学未来交通学院西安 710064

基金项目: 陕西省自然科学基础研究计划资助项目(2025JC-YBQN-947)，长安大学中央高校基本科研业务费专项资金(300102244203，300102243401)

详细信息

作者简介:
陈鹏：男，副教授，研究方向为阵列信号处理，邮箱　pchen@chd.edu.cn

章倩：女，硕士生，研究方向为阵列信号处理

刘耀明：男，硕士生，研究方向为阵列信号处理

胡欣：女，讲师，研究方向为信号处理，邮箱　xinhu@chd.edu.cn

王威：男，教授，研究方向为阵列信号处理

通讯作者:
胡欣　xinhu@chd.edu.cn

中图分类号: TN911.7
计量
- 文章访问数: 21
- HTML全文浏览量: 3
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- 被引次数: 0
出版历程
- 收稿日期: 2026-07-14
- 修回日期: 2026-09-18
- 录用日期: 2026-09-28
- 网络出版日期: 2026-10-08

Robust Adaptive Beamforming with Eigenspace Bases Transition for Mixed-Near-and-Far Field Scenarios

CHEN Peng^1
,,
ZHANG Qian¹,
LIU Yaoming¹,
HU Xin^{2
, ,},
WANG Wei¹

1.
School of Information Engineering, Chang’an University, Xi’an 710064, China
2.
School of Future Transportation, Chang’an University, Xi’an 710064, China

Funds: Natural Science Basic Research Program of Shaanxi (2025JC-YBQN-947), Fundamental Research Funds for the Central Universities, Chang’an University (300102244203, 300102243401)

摘要

摘要: 自适应波束形成旨在设计一种能够根据环境变化自动提取期望信号并抑制干扰和噪声的空域滤波器。随着第六代(6G)移动通信、毫米波感知等技术的发展，远场源与近场源同时存在的混合远近场场景日益普遍。现有的单一远场或近场自适应波束形成算法在此类混合远近场场景中面临平面波与球面波模型失配导致的性能局限；同时，源位置参数未知且参数间存在耦合，进一步加剧了性能恶化。针对上述问题，本文基于子空间基变换框架，针对混合远近场中响应向量同时依赖于角度和距离的特点，提出一种面向混合远近场场景的稳健自适应波束形成算法。该算法的核心在于：首先，建立混合远近场信号模型，通过多起点L-BFGS-B优化策略求解空间距离最小化问题，实现混合远近场响应向量的高精度估计；其次，通过特征空间基变换方法重构干扰加噪声协方差矩阵—该方法在子空间基变换思想基础上，将基矩阵的构造从纯角度域拓展至角度-距离联合域，并通过基变换矩阵直接从样本协方差矩阵中去除期望信号成分，避免了传统方法中噪声功率估计引入的附加误差；最后，利用重构的干扰加噪声协方差矩阵修正导向向量，进而计算波束形成器的加权向量。仿真结果表明，所提算法在混合远近场场景下能够有效缓解源位置参数耦合导致的性能恶化，在高输入信噪比(Signal-to-Noise Ratio, SNR)条件下，输出信干噪比(Signal-to-Interference-and-Noise Ratio, SINR)与理论最优值的偏差保持在1 dB以内，显著优于所对比的七种算法。
- 自适应波束形成 /
- 混合远近场 /
- 特征空间基变换 /
- L-BFGS-B优化 /
- 干扰加噪声协方差矩阵重构
Abstract: Objective The rapid evolution of modern communication and sensing technologies, such as 6G communication-sensing integration, 5G ultra-dense networking, and indoor Internet of Things (IoT) applications, has rendered the operating environment of array antenna systems increasingly complex, and Mixed-Near-and-Far (MNF) field scenarios with the coexistence of far-field and near-field targets have become the norm. As the core technology of array signal processing, beamforming includes adaptive beamforming and static beamforming. Unlike static beamforming, adaptive beamforming can automatically adjust the weight vector according to changes in the actual environment, allowing the desired signal to pass without distortion while suppressing interference and noise. Existing adaptive beamforming algorithms tailored for either single far-field or single near-field scenarios are limited to signal processing in a single-distance scenario and suffer from performance limitations when addressing the filtering problem in MNF field scenarios. Therefore, it is imperative to carry out research on adaptive beamforming algorithms for the MNF field. Current studies on the MNF field mainly focus on source localization or communication precoding, while research on MNF-field beamforming for interference suppression remains insufficient. Moreover, the source position parameters are generally unknown and coupled, i.e., the response vector depends simultaneously on the angle and the distance, which causes traditional angle-only methods to suffer from significant model mismatch when processing near-field sources. Aiming at the degradation of beamforming performance caused by this coupling problem, this paper proposes a Robust Adaptive Beamforming Using Eigenspace Bases Transition in MNF field. Methods Consider a uniform linear array (ULA) composed of M isotropic sensor elements with a fixed spacing, which receives one desired signal, J interferences, and additive white Gaussian noise in the MNF field; the Fresnel first-order approximation is adopted for near-field sources. The algorithm reconstructs the interference-plus-noise covariance matrix (INCM) by estimating the eigenspace of the received data and directly removing the desired signal component from the Sample Covariance Matrix (SCM). Unlike the reconstruction algorithms based on subspace basis transition, the reconstruction algorithm based on eigenspace bases transition can avoid the direct estimation of the noise covariance matrix and improve the accuracy of INCM reconstruction. To tackle the issue of coupled source position parameters within the MNF field, a multi-start L-BFGS-B optimization strategy is employed to solve the space distance minimization problem, resulting in an estimated source response vector set (SRVS). Specifically, multiple initial points are randomly sampled within the search space bounded by nominal DOA prior information, and each point independently undergoes L-BFGS-B optimization to convergence; the solution with the minimum objective value among all runs is then selected as the parameter estimation result. This multi-start mechanism effectively overcomes the local minimum issue caused by permutation symmetry. Additionally, an eigenspace bases transition (EBT) method is proposed to compute the reconstructed INCM, thereby circumventing the need for power estimation of interference and noise. With the basis transition matrix, the reconstructed INCM is obtained by attenuating the desired-signal component and nulling the noise-subspace components through a diagonal attenuation matrix, and the noise-subspace term is then added back to compensate for the missing noise components. In typical scenarios with large arrays and controlled optimization parameters, the computational complexity of the proposed algorithm is on the same order as that of classical adaptive beamforming methods. Subsequently, the modified steering vector (SV) is derived through eigendecomposition of the desired signal covariance matrix, and the weighting vector for the MNF field beamformer is produced. Results and Discussions A series of simulation experiments are performed to evaluate the performance of the proposed EBT-based adaptive beamforming in the MNF field. In the simulations, an 11-element ULA with half-wavelength spacing is adopted, and the desired signal and two interferences are located at (15°, 30 m), (−25°, 1000 m), and (35°, 15 m), respectively, with an interference-to-noise ratio of 20 dB and 200 Monte Carlo trials. These experiments include the beamformer performance, the impact of L-BFGS-B parameter selection (number of starting points and maximum iterations) on the algorithm performance, and the impact of the attenuation coefficient on it. The results show that the proposed algorithm maintains stable mainlobe gain in the direction of the desired signal, while forming deep nulls in the direction of interference, which greatly suppresses interference (Fig.1). Under MNF field conditions, the existing near-field and far-field algorithms are mismatched with this scenario and suffer from significant performance degradation when processing the array-received data. In contrast, the proposed algorithm can effectively solve the problem of beamformer performance degradation caused by the aforementioned coupling situation. Meanwhile, under the condition of high input signal-to-noise ratio (SNR), the difference between the output signal-to-interference-plus-noise ratio (SINR) of the proposed algorithm and the theoretical optimal value is less than 1 dB (Fig.2, Fig.3). Moreover, the output SINR value of the proposed algorithm is much higher than that of other algorithms under different snapshot numbers and approaches the optimal upper bound. In the simulation analysis of L-BFGS-B parameter and attenuation coefficient selection, the proposed algorithm can ensure that the output SINR value remains relatively stable under reasonable parameter selection and close to the theoretical optimal value (Fig.4, Fig.5). Overall, the proposed algorithm can effectively solve the problem of beamformer performance degradation caused by the aforementioned coupling situation and outperforms the compared beamforming algorithms. Conclusions This paper addresses the performance degradation of beamformers in MNF field scenarios caused by unknown and coupled source position parameters. A robust adaptive beamforming algorithm using EBT is proposed. The method first estimates the source response vector set via the multi-start L-BFGS-B optimization strategy to handle parameter coupling. It then reconstructs the interference-plus-noise covariance matrix through the EBT method, eliminating the desired signal component and avoiding direct noise power estimation. Finally, the corrected steering vector and the optimal weight vector are derived. Simulation results demonstrate that the proposed algorithm effectively maintains mainlobe gain towards the desired signal while forming deep nulls at interference directions, significantly suppressing interference. The proposed algorithm achieves an output SINR close to the theoretical optimum, especially under high input SNR, and outperforms the compared far-field or near-field algorithms. The performance remains robust across different snapshot numbers and parameter selections. Therefore, the EBT-based algorithm provides an effective solution for robust adaptive beamforming in complex MNF field environments, enhancing system reliability in modern communication and sensing applications.
- Adaptive beamforming /
- Mixed-near-and-far field /
- Eigenspace bases transition (EBT) /
- L-BFGS-B optimization /
- Interference-plus-noise covariance matrix reconstruction

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图 1 不同距离处的归一化波束响应

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图 2 输出SINR随输入SNR变化

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图 3 输出SINR随快拍数变化

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图 4 输出SINR随L-BFGS-B参数变化

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图 5 输出SINR随$ \eta $的变化情况

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