Underdetermined Blind Source Separation of Synchronous Orthogonal Frequency Hopping Signals Based on Single Source Points Detection

Zhang, Chaozhu; Wang, Yu; Jing, Fulong

doi:10.3390/s17092074

Cited by 20 publications

(10 citation statements)

References 45 publications

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“…A number of blind signal tracking and sorting structures have been proposed for FH signals. One widely used method is spatial time-frequency analysis, which is usually utilized to describe sparse signals in the time-frequency domain [4]. In [5], the sparsity of the FH signal in the time-frequency domain was described and the parameters of multiple frequency hopping signals were estimated via sparse linear regression.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], the proposed method uses the smoothed pseudo Wigner-Ville distribution (SPWVD) of each separated FHSS signal to estimate its transmission parameters and separates the signals by the joint approximate diagonalization of eigen-matrices (JADE) algorithm. In [4], a novel method to detect single source points in the time-frequency domain for separation was proposed, which also improves the subspace projection method to recover signal [4,[6][7][8][9][10]. These methods are intuitive and effective.…”

Section: Introductionmentioning

confidence: 99%

“…It can be concluded that estimating parameters is a way to obtain the conditions for tracking and sorting. However, the methods in [4,[6][7][8][9][10] cannot process signals in real time, because they are all based on clustering information of a large amount of data. In many practical applications, such as jamming and dynamic reconnaissance, these methods do not satisfy the command.…”

Section: Introductionmentioning

confidence: 99%

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Frequency Hopping Signals Tracking and Sorting Based on Dynamic Programming Modulated Wideband Converters

et al. 2019

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Most of the earlier tracking and network sorting approaches with a high sampling rate for frequency hopping (FH) signals did not adapt to the wideband system during their implementation, whereas the sub-Nyquist based algorithms cannot satisfy the real-time requirement for dealing with the rapid change of sparsity. It is important to improve the compressed sensing (CS) methods for tracking and sorting wideband FH signals. In this paper, a dynamic programming modulated wideband converters (MWC) scheme is proposed. First, considering the wide gap of FH signals, an improved power estimation method is proposed to track the support set in the time domain. Second, to sort multiple signals more effectively, a feedback control algorithm based on dynamic programming is proposed. In the proposed method, the total sampling rate is decreased significantly, and multiple FH signals are separated rapidly without recovery based on the results of tracking and comparative power. Simulations show that the proposed method can track and sort FH signals efficiently and more practically than previous methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Frequency Hopping Signals Tracking and Sorting Based on Dynamic Programming Modulated Wideband Converters

et al. 2019

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“…Other than statistical independence and non-Gaussianity, signal separation approach based on second-order statistics of the speech signals using canonical correlation approach [ 13 ] has also been proposed. The work [ 14 ] considers complex-valued mixing matrix estimation and direction-of-arrival estimation of synchronous orthogonal frequency hopping signals in the underdetermined blind source separation (UBSS). A mixing matrix estimation algorithm is proposed by detecting single source points where only one source contributes its power.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution †

Woo

Gao

Bouridane

et al. 2018

Sensors

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This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time–frequency deconvolution with optimized fractional β-divergence. The β-divergence is a group of cost functions parametrized by a single parameter β. The Itakura–Saito divergence, Kullback–Leibler divergence and Least Square distance are special cases that correspond to β=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional values. It describes a maximization–minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time–frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional β value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy.

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“…Parameter estimation of multiple emitting sources plays an important role in array signal processing due to its applications in various areas, ranging from radar, sonar, microphone arrays, radio astronomy, seismology, medical diagnosis and treatment, to communications [ 1 ]. Signals with time-varying frequencies, such as linear frequency-modulation and frequency-hopping (FH) signals, have been extensively used [ 1 , 2 , 3 , 4 , 5 ]. Multiple signals’ sorting and DOA estimations are two important tasks for array signal processing.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Multiple Frequency-Hopping Signals with Two Sensors

Zuo

Pan

2018

Sensors

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This paper essentially focuses on parameter estimation of multiple wideband emitting sources with time-varying frequencies, such as two-dimensional (2-D) direction of arrival (DOA) and signal sorting, with a low-cost circular synthetic array (CSA) consisting of only two rotating sensors. Our basic idea is to decompose the received data, which is a superimposition of phase measurements from multiple sources into separated groups and separately estimate the DOA associated with each source. Motivated by joint parameter estimation, we propose to adopt the expectation maximization (EM) algorithm in this paper; our method involves two steps, namely, the expectation-step (E-step) and the maximization (M-step). In the E-step, the correspondence of each signal with its emitting source is found. Then, in the M-step, the maximum-likelihood (ML) estimates of the DOA parameters are obtained. These two steps are iteratively and alternatively executed to jointly determine the DOAs and sort multiple signals. Closed-form DOA estimation formulae are developed by ML estimation based on phase data, which also realize an optimal estimation. Directional ambiguity is also addressed by another ML estimation method based on received complex responses. The Cramer-Rao lower bound is derived for understanding the estimation accuracy and performance comparison. The verification of the proposed method is demonstrated with simulations.

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Underdetermined Blind Source Separation of Synchronous Orthogonal Frequency Hopping Signals Based on Single Source Points Detection

Cited by 20 publications

References 45 publications

Frequency Hopping Signals Tracking and Sorting Based on Dynamic Programming Modulated Wideband Converters

Frequency Hopping Signals Tracking and Sorting Based on Dynamic Programming Modulated Wideband Converters

Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution †

Parameter Estimation of Multiple Frequency-Hopping Signals with Two Sensors

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