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Underdetermined DOA Estimation of Wideband LFM Signals Based on Gridless Sparse Reconstruction in the FRF Domain

机译:FRF域中基于无网格稀疏重建的宽带LFM信号欠定DOA估计

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摘要

An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant steering vector is firstly derived based on a coprime array. Afterwards, by interpolating virtual sensors, a virtual extended uniform linear array (ULA) is constructed with more degrees of freedom, and its covariance matrix in the FRF domain is recovered by employing sparse matrix reconstruction. Meanwhile, in order to avoid the grid mismatch problem, the modified atomic norm minimization is used to retrieve the covariance matrix with the consecutive basis. Different from the existing methods that approximately assume the frequency and the steering vector of the wideband signals are time-invariant in every narrowband frequency bin, the proposed method not only can directly solve more DOAs of LFM signals than the number of physical sensors with time-variant frequency and steering vector, but also obtain higher resolution and more accurate DOA estimation performance by the gridless sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed method.
机译:提出了一种不带网格失配的宽带线性调频(LFM)信号的不确定到达方向(DOA)估计方法。根据分数阶傅立叶(FRF)域中LFM信号的集中特性,首先基于共质数阵列,推导了带有时变转向矢量的宽带信号的稀疏模型。然后,通过内插虚拟传感器,构建具有更大自由度的虚拟扩展均匀线性阵列(ULA),并通过稀疏矩阵重构来恢复其在FRF域中的协方差矩阵。同时,为了避免网格不匹配问题,使用改进的原子范数最小化来检索具有连续基数的协方差矩阵。与现有的近似方法(假设每个窄带频点中宽带信号的频率和导引矢量是时不变的)不同,该方法不仅可以直接解决LFM信号的DOA问题,而且可以解决多个LFM信号的问题。通过使用无网格稀疏重建,可以得到更高的分辨率和更准确的DOA估计性能。仿真结果证明了该方法的有效性。

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