面向毫米波模数混合阵列的压缩协方差无网格DOA估计

    Grid-lessDOA Estimation of Compressed Covariance for Millimeter-Wave Hybrid Arrays

    • 摘要: 针对毫米波子阵连接混合阵列中射频链受限与低比特相移器带来的压缩观测和有色噪声问题,研究连续角域的波达角(Direction of Arrival, DOA)估计问题。首先,在多配置子阵连接系统中构造压缩协方差观测模型,显式保留由混合合并矩阵引起的配置相关噪声协方差;其次,利用均匀线阵信号协方差的托普利兹(Toeplitz)和半正定结构,引入Toeplitz参数化的无网格原子范数最小化方法,在协方差域重构结构化信号协方差,并联合估计噪声强度;最后,采用对角加载Root-MUSIC算法从重构协方差矩阵中提取DOA参数。蒙特卡洛仿真表明,与栅格化稀疏重构和压缩域MVDR扫描基线相比,所提方法在中高信噪比、有限快拍、低比特量化和有色噪声条件下具有更低的角度均方根误差和更强的近角分辨能力。

       

      Abstract: To address compressed observations and colored noise caused by the limited number of radio-frequency (RF) chains and low-bit phase shifters in millimeter-wave (mmWave) subarray-connected hybrid arrays, this paper studies direction-of-arrival (DOA) estimation in the continuous angular domain. A multi-configuration compressed covariance observation model is first established, where the configuration-dependent noise covariance induced by hybrid combining is explicitly retained. Then, by exploiting the Toeplitz and positive semidefinite (PSD) priors of the uniform linear array (ULA) signal covariance, a Toeplitz-parameterized gridless atomic norm minimization method is developed to reconstruct the structured signal covariance and jointly estimate the noise power. Finally, diagonally loaded Root-MUSIC is used to extract DOAs from the reconstructed covariance matrix. Monte Carlo simulations show that the proposed method achieves lower angular root-mean-square error and stronger resolution of closely spaced sources than grid-based sparse reconstruction and compressed-domain MVDR scanning baselines under moderate-to-high SNR, finite snapshots, low-bit quantization, and colored-noise conditions.

       

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