Frequency‐hopping signals sorting based on underdetermined blind source separation

Sha, Zhi-Chao; Huang, Zhitao; Zhou, Yiyu; Wang, Fenghua

doi:10.1049/iet-com.2013.0276

Cited by 34 publications

(32 citation statements)

References 21 publications

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“…where (M ) 푙,푙 denotes the element on the th row and th column of the matrix Σ S after the th iteration, which can be deduced from (12) and (13).…”

Section: Spatial Frequency Estimationmentioning

confidence: 99%

“…In many cases, FH signals are actually wideband, while many existing methods of DOA estimation consider the FH signals to be narrowband signals [13,14]. To solve this issue, an expectation maximization (EM) approach is proposed for blind hop timing DOA and frequency estimation in the situation of possible bandwidth mismatch in [15]; the success of this approach mainly depends on the selection of the initial value.…”

Section: Introductionmentioning

confidence: 99%

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A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

Zhang¹,

Guo²,

Qi³

2017

Mathematical Problems in Engineering

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Parameter estimation and network sorting for noncooperative wideband frequency-hopping (FH) signals have been essential and challenging tasks, especially in the case with little or even no prior information at all. In this paper, we propose a nearly blind estimation approach to estimate signal parameters based on sparse Bayesian reconstruction. Taking the sparsity in the spatial frequency domain of multiple FH signals into account, we propose a sparse Bayesian algorithm to estimate the spatial frequency parameters. As a result, the frequency and direction of arrival (DOA) parameters can be obtained. In order to improve the accuracy of the estimation parameters, we employ morphological filter methods to further clean the data poisoned by the noise. Moreover, our method is applicable to the wideband signal models with little prior information. We also conduct extensive numerical simulations to verify the performance of our method. Notably, the proposed method works well even in low signal-to-noise ratio (SNR) environment.

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“…where (M ) 푙,푙 denotes the element on the th row and th column of the matrix Σ S after the th iteration, which can be deduced from (12) and (13).…”

Section: Spatial Frequency Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

Zhang¹,

Guo²,

Qi³

2017

Mathematical Problems in Engineering

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“…The improved subspace-based algorithm is used to sort the network, Sha [6] proposed an improved subspace-based algorithm for undetermined network sorting to relax sparsity constraints. The improved subspace-based algorithm introduces the comparative powers of sources to conventional subspace-based algorithm.…”

Section: Network Sorting Under Undetermined Conditionmentioning

confidence: 99%

“…For asynchronous non-orthogonal FH signals, the separation accuracy is high and fast, but for synchronous orthogonal FH signals, the accuracy is greatly reduced. Then Sha in [6] proposed an improved subspace projection method to realize the network separation under undetermined conditions, but the SNR adaptive capacity is weak.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the mixing matrix is estimated accurately by the TF ratio matrix of TF single source point, and then an improved subspace projection method is used for network sorting, and the adaptive SNR TF pivot threshold method is used to find the TF single source point. Finally, the algorithm in [6] is used as the reference algorithm and the simulation results using the two algorithms are compared as well.…”

Section: Introductionmentioning

confidence: 99%

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Network Sorting Algorithm for Multi-frequency Hopping Signals with Adaptive SNR

Yu¹,

Guo²,

Yang³

et al. 2017

dtcse

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Abstract. Aiming at the multi-FH signal sorting under undetermined condition, this paper put forward a SNR-adaptive sorting algorithm. Firstly, sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source; lastly, signal sorting is realized through improved subspace projection combined with relative power deviation of source. Simulation result shows the effectiveness of this method under low SNR condition.

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A Sorting Algorithm for Multiple Frequency-Hopping Signals in Complex Electromagnetic Environments

Zhan

2019

Circuits Syst Signal Process

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Frequency‐hopping signals sorting based on underdetermined blind source separation

Cited by 34 publications

References 21 publications

A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

Network Sorting Algorithm for Multi-frequency Hopping Signals with Adaptive SNR

A Sorting Algorithm for Multiple Frequency-Hopping Signals in Complex Electromagnetic Environments

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