摘要
近场源的阵列流型包含角度和距离参数,两者相互耦合,难以分离。现有方法一般采用近似解耦模型,分步估计出角度和距离参数。然而,在近似解耦过程中,不可避免地引入了系统模型误差,导致定位性能严重下降。为了应对上述挑战,提出了一种基于非均匀网格的稀疏表示近场源定位方法,将复杂的近场源定位问题直接建模成一个较低维度的稀疏信号恢复问题,并利用稀疏贝叶斯学习(Sparse Bayesian learning, SBL)方法实现对稀疏信号的自适应恢复,从而避免引入近似误差,显著提高了参数估计的准确性。所提方法中的非均匀网格仅含有较少的网格点,极大降低了计算复杂度;各网格点之间的角度和距离均不相同,有效克服了字典矩阵中相邻基之间相关性高的缺陷;额外引入网格优化技术,进一步解决了粗糙网格可能导致的失配问题。仿真结果证实了所提方法的优越性。
The near-field steering vector contains the angle and range parameters.They are coupled with each other and difficult to separate.Most existing methods adopt the approximate decoupling model to estimate the angle and range parameters step by step.However,such an approximate decoupling model will inevitably introduce a systematic model error,which could lead to severe localization performance degradation.To address the above challenges,this paper proposes a near-field sources localization method for sparse representation via a non-uniform grid.It directly models the complex near-field sources localization as a lower-dimensional sparse signal recovery problem and adopts sparse Bayesian learning(SBL)to adaptively recover the sparse signal,avoiding the approximate error and improving the parameters estimation accuracy.In the proposed method,the non-uniform grid only contains a few points,reducing the computational complexity greatly.The nearby points neither share the same direction of arrival(DOA)nor the range value,effectively overcoming the high correlation basis.And the grid refinement trick is additionally introduced to further solve the mismatch problem caused by the coarse grid.The numerical simulation results confirm the superiority of the proposed method.
作者
李一
傅海军
戴继生
LI Yi;FU Haijun;DAI Jisheng(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China;College of Information Science and Technology,Donghua University,Shanghai 201620,China)
出处
《数据采集与处理》
北大核心
2025年第1期187-196,共10页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(62071206)。
关键词
近场源定位
稀疏表示
稀疏信号恢复
稀疏贝叶斯学习
网格细化
near-field sources localization
sparse representation
sparse signal recovery
sparse Bayesian learning(SBL)
grid refinement
