2018
DOI: 10.1109/access.2018.2880237
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Underdetermined Blind Source Separation of Synchronous Orthogonal Frequency-Hopping Signals Based on Tensor Decomposition Method

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Cited by 12 publications
(13 citation statements)
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“…The time of the last non-complete jump in the observation period is ns t ∆ , the center frequency is ns t ∆ . The ( ) n t s can be expressed as [11][12][13][14]:…”
Section: Fh Signal Model Under Impulse Noisementioning
confidence: 99%
“…The time of the last non-complete jump in the observation period is ns t ∆ , the center frequency is ns t ∆ . The ( ) n t s can be expressed as [11][12][13][14]:…”
Section: Fh Signal Model Under Impulse Noisementioning
confidence: 99%
“…We choose b 1 1 from b 1 and its corresponding conjugate value b −1 1 from b −1 . Then, we respectively choose one value b 2 1…”
Section: B Pseudo Data Setmentioning
confidence: 99%
“…The corresponding negative lags in the set −D n 1 have the same results. According to properties (1), (2), 3, and (4), the number of single lags in the difference co-array is…”
Section: )mentioning
confidence: 99%
“…However, as mentioned earlier, references like [ 30 ] that rely on TFA-based approaches for initial estimations suffer from cross-term interference and high SNR requirements. The joint estimation of FH parameters and DOA for multiple sources is studied in [ 31 , 32 , 33 , 34 ] under the assumption that all sources are active throughout the entire observation interval. Additionally, in reference [ 34 ], the hop periods are assumed uniform.…”
Section: Introductionmentioning
confidence: 99%
“…Additionally, in reference [ 34 ], the hop periods are assumed uniform. None of the approaches in [ 31 , 32 , 33 , 34 ] are able to incorporate sources that are sparse spatially and have intermittent activity, and probabilistic source models, such as Markov models. A comprehensive summary of the approaches in the current literature and their limitations can be found in Table 1 .…”
Section: Introductionmentioning
confidence: 99%