In this paper, a novel source localization algorithm using a uniform linear array is proposed for scenarios where both the near-field (NF) and far-field multi-band sources may exist simultaneously. The proposed method is performed in two stages. In the first stage, we firstly exploit some spatial correlations of each frequency output component to construct a virtual array output and represent the virtual array output on a corresponding overcomplete basis or dictionary which is only related to the direction-of-arrivals (DOAs) of sources. And then we can establish a multiple-dictionary sparse representation model. Finally, we estimate DOAs of the incident sources by solving the weighted $$\ell _1$$ ℓ 1 -norm minimization problem. In the second stage, every frequency output component is firstly represented on a corresponding mixed overcomplete basis with the estimated DOAs, and then a multiple-dictionary sparse representation model is created. At last, the ranges of the NF sources are estimated using the weighted $$\ell _1$$ ℓ 1 -norm minimization, and the types of sources are also distinguished. The proposed algorithm avoids parameter pair-matching and two-dimensional search, and does not require a prior knowledge of source number. Simulation results indicate that the proposed algorithm can provide an improved localization accuracy and locate more sources than the existing techniques for the case of multi-band sources.

中文翻译：

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基于稀疏表示的近场和远场混合多波段源定位

在本文中，针对可能同时存在近场 (NF) 和远场多波段源的场景，提出了一种使用均匀线性阵列的新型源定位算法。所提出的方法分两个阶段执行。在第一阶段，我们首先利用每个频率输出分量的一些空间相关性来构建虚拟阵列输出，并在相应的过完备基或仅与到达方向（DOA）相关的字典上表示虚拟阵列输出。来源。然后我们可以建立一个多字典的稀疏表示模型。最后，我们通过解决加权 $$\ell _1$$ ℓ 1 -norm 最小化问题来估计事件源的 DOA。在第二阶段，每个频率输出分量首先用估计的DOA在相应的混合过完备基础上表示，然后创建多字典稀疏表示模型。最后，使用加权$$\ell _1$$ ℓ 1 -范数最小化估计NF源的范围，并区分源的类型。所提出的算法避免了参数对匹配和二维搜索，并且不需要源编号的先验知识。仿真结果表明，对于多波段源的情况，所提出的算法可以提供比现有技术更高的定位精度和定位更多的源。使用加权$$\ell _1$$ ℓ 1 -norm 最小化估计 NF 源的范围，并且还区分了源的类型。所提出的算法避免了参数对匹配和二维搜索，并且不需要源编号的先验知识。仿真结果表明，对于多波段源的情况，所提出的算法可以提供比现有技术更高的定位精度和定位更多的源。使用加权$$\ell _1$$ ℓ 1 -norm 最小化估计 NF 源的范围，并且还区分了源的类型。所提出的算法避免了参数对匹配和二维搜索，并且不需要源编号的先验知识。仿真结果表明，对于多波段源的情况，所提出的算法可以提供比现有技术更高的定位精度和定位更多的源。