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Components Separation Algorithm for Localization and Classification of Mixed Near-Field and Far-Field Sources in Multipath Propagation
IEEE Transactions on Signal Processing ( IF 5.4 ) Pub Date : 2020-01-01 , DOI: 10.1109/tsp.2019.2961226
Amir Masoud Molaei , Bijan Zakeri , Seyed Mehdi Hosseini Andargoli

In recent years, the sources localization has noticed an increase in research conducted on the problem of mixed far-field sources (FFSs) and near-field sources (NFSs). The main assumption of the existing researches is that the signals should be uncorrelated. Therefore, they cannot be used for multipath environments. The present paper provides a method called components separation algorithm (CSA) for the localization of multiple mixed FFSs and NFSs, including uncorrelated, lowly correlated and coherent signals. Firstly, by constructing one special cumulant matrix, and using a MUSIC-based technique, the noncoherent DOA vector (NDOAV) is extracted. By constructing another special cumulant matrix, and with respect to NDOAV, an estimate of the range, as well as a signal classification is obtained for noncoherent sources. Then, by estimating their kurtosis, the noncoherent component and consequently the coherent one of the second cumulant matrix is obtained. Finally, by introducing a novel approach based on squaring, projection, spatial smoothing, array interpolation transform and coherent component restoring, the parameters of coherent signals in each coherent group are estimated separately. The CSA prevents severe loss of the aperture. Furthermore, it does not require any pairing. The simulation results validate its satisfactory performance in terms of estimation accuracy, resolution, computational complexity, reasonable classification, and also its robustness against lowly correlated sources.

中文翻译:

多径传播中近场和远场混合源定位和分类的分量分离算法

近年来,源定位已经注意到对混合远场源(FFS)和近场源(NFS)问题的研究有所增加。现有研究的主要假设是信号应该是不相关的。因此,它们不能用于多路径环境。本文提供了一种称为分量分离算法 (CSA) 的方法,用于定位多个混合 FFS 和 NFS,包括不相关、低相关和相干信号。首先,通过构造一个特殊的累积量矩阵,并使用基于MUSIC的技术,提取非相干DOA向量(NDOAV)。通过构建另一个特殊的累积量矩阵,并且相对于 NDOAV,可以获得非相干源的范围估计以及信号分类。然后,通过估计它们的峰度,可以获得第二累积矩阵的非相干分量和相干分量。最后,通过引入基于平方、投影、空间平滑、阵列插值变换和相干分量恢复的新方法,分别估计每个相干组中相干信号的参数。CSA 可防止孔径的严重损失。此外,它不需要任何配对。仿真结果验证了其在估计精度、分辨率、计算复杂度、合理分类以及对低相关源的鲁棒性方面的令人满意的性能。空间平滑、阵列插值变换和相干分量恢复,分别估计每个相干组中相干信号的参数。CSA 可防止孔径的严重损失。此外,它不需要任何配对。仿真结果验证了其在估计精度、分辨率、计算复杂度、合理分类以及对低相关源的鲁棒性方面的令人满意的性能。空间平滑、阵列插值变换和相干分量恢复,分别估计每个相干组中相干信号的参数。CSA 可防止孔径的严重损失。此外,它不需要任何配对。仿真结果验证了其在估计精度、分辨率、计算复杂度、合理分类以及对低相关源的鲁棒性方面的令人满意的性能。
更新日期:2020-01-01
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