论文标题
使用太空频率虚拟差异共同估计联合DOA范围
Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray
论文作者
论文摘要
在本文中,我们使用频率不同的副阵列(FDCA)解决了联合方向(DOA)和范围估计的问题。通过结合副阵列结构和频率偏移,认为对应于均匀阵列和均匀频率偏移量对应的二维空间虚拟差差异被认为增加了自由度(DOFS)的数量。然而,双向eplitz协方差矩阵的重建在计算上是过时的。为了解决这个问题,我们提出了一种基于解耦的原子规范最小化(DANM)的插值算法,该算法将coarray信号转换为简单矩阵形式。在此基础上,制定了基于松弛的优化问题,以实现与增强DOF的关节DOA范围估计。重建的COARRAY信号可实现现有基于子空间的光谱估计方法。提出的DANM问题进一步重新重新重新重新重新进行,这是一个同等的等级最小化问题,该问题通过循环等级最小化解决。这种方法避免了基于核规范的方法中引入的近似误差,从而达到了更接近Cramer-Rao结合的优质根平方误差。提出方法的有效性通过理论分析和数值模拟证实。
In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array structure and coprime frequency offsets, a two-dimensional space-frequency virtual difference coarray corresponding to uniform array and uniform frequency offset is considered to increase the number of degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz covariance matrix is computationally prohibitive. To solve this problem, we propose an interpolation algorithm based on decoupled atomic norm minimization (DANM), which converts the coarray signal to a simple matrix form. On this basis, a relaxation-based optimization problem is formulated to achieve joint DoA-range estimation with enhanced DoFs. The reconstructed coarray signal enables application of existing subspace-based spectral estimation methods. The proposed DANM problem is further reformulated as an equivalent rank-minimization problem which is solved by cyclic rank minimization. This approach avoids the approximation errors introduced in nuclear norm-based approach, thereby achieving superior root-mean-square error which is closer to the Cramer-Rao bound. The effectiveness of proposed method is confirmed by theoretical analyses and numerical simulations.