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Mixed Far-Field and Near-Field Source Localization Algorithm via Sparse Subarrays

机译:混合远场和近场源定位算法通过稀疏子阵列

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摘要

Based on a dual-size shift invariance sparse linear array, this paper presents a novel algorithm for the localization of mixed far-field and near-field sources. First, by constructing a cumulant matrix with only direction-of-arrival (DOA) information, the proposed algorithm decouples the DOA estimation from the range estimation. The cumulant-domain quarter-wavelength invariance yields unambiguous estimates of DOAs, which are then used as coarse references to disambiguate the phase ambiguities in fine estimates induced from the larger spatial invariance. Then, based on the estimated DOAs, another cumulant matrix is derived and decoupled to generate unambiguous and cyclically ambiguous estimates of range parameter. According to the coarse range estimation, the types of sources can be identified and the unambiguous fine range estimates of NF sources are obtained after disambiguation. Compared with some existing algorithms, the proposed algorithm enjoys extended array aperture and higher estimation accuracy. Simulation results are given to validate the performance of the proposed algorithm.
机译:基于双尺寸换档不变性稀疏线性阵列,本文提出了一种新颖的混合远场和近场源的定位算法。首先,通过构建仅具有到达方向(DOA)信息的累积矩阵,所提出的算法从范围估计中解耦DOA估计。累积域区四分之一波长不变性产生了DOA的明确估计,然后用作粗略参考,以消除从较大的空间不变性引起的细估计中的相位模糊。然后,基于估计的DOA,派生和解耦以产生范围参数的明确和循环模糊估计的另一累积矩阵。根据粗略范围估计,可以识别源的类型,并且在消化不衰后获得NF源的明确微距估计。与一些现有算法相比,所提出的算法始置扩展阵列孔径和更高的估计精度。给出了仿真结果验证了所提出的算法的性能。

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