Blind Signal Separation Method Research Articles

JCCM: Joint conformer and CNN model for overlapping radio signals recognition

AbstractOverlapping radio signals recognition is attracting more attention as the development and ubiquitous application of radio technologies. The traditional blind signal separation (BSS) method is mostly not effective when both radio propagation effects and low signal‐to‐noise ratio (SNR) scenarios are taken into consideration. In this letter, joint conformer and CNN model (JCCM) is proposed to separate and recognize the overlapping radio signals which are also unknown by the monitor node. JCCM utilizes the attention mechanism of conformer to encode signal spectrum into feature maps and decodes the feature maps into signal component proportions by perceiving the global and local features through convolutional neural networks (CNN). In addition, a signal preprocessing module is designed including MinPool and AvgPool layers for signal denoising. JCCM is evaluated in terms of signal separation, scaling preservation and principal signal classification experiments. The results show that JCCM has better performance especially in low SNR conditions.

Application of Blind Signal Separation Methods in the Problems on Improving the Interference Immunity of Space Communication Systems with Quadrature Amplitude Modulation

Application of Blind Signal Separation Methods in the Problems on Improving the Interference Immunity of Space Communication Systems with Quadrature Amplitude Modulation

Detecting the H i power spectrum in the post-reionization Universe with SKA-Low

ABSTRACT We present a survey strategy to detect the neutral hydrogen (H i) power spectrum at 5 < z < 6 using the SKA-Low radio telescope in presence of foregrounds and instrumental effects. We simulate observations of the inherently weak H i signal post-reionization with varying levels of noise and contamination with foreground amplitudes equivalent to residuals after sky model subtraction. We find that blind signal separation methods on imaged data are required in order to recover the H i signal at large cosmological scales. Comparing different methods of foreground cleaning, we find that Gaussian Process Regression (GPR) performs better than Principle Component Analysis (PCA), with the key difference being that GPR uses smooth kernels for the total data covariance. The integration time of one field needs to be larger than ∼250 h to provide large enough signal-to-noise ratio (SNR) to accurately model the data covariance for foreground cleaning. Images within the primary beam field-of-view give measurements of the H i power spectrum at scales $k\sim 0.02\, {\rm Mpc^{-1}}-0.3\, {\rm Mpc^{-1} }$ with SNR ∼2–5 in Δ[log(k/Mpc−1)] = 0.25 bins assuming an integration time of 600 h. Systematic effects, which introduce small-scale fluctuations across frequency channels, need to be ≲ 5 × 10−5 to enable unbiased measurements outside the foreground wedge. Our results provide an important validation towards using the SKA-Low array for measuring the H i power spectrum in the post-reionization Universe.

Анализ эффективности использования методов слепого разделения сигналов для борьбы с помеховым излучением электрических ракетных двигателей в системах космической связи

The article considers the use of blind signal separation algorithms to improve interference immunity of the satellite communication channel with octal phase shift keying as applied to the interference, that is caused by the operation of stationary plasma thrusters (SPT), and represents one of the varieties of electric propulsion [1]. During the study, a simulation model of communication channel with an additional channel for receiving interference from SPT was developed. While modeling, some pulsed noise-like signals, formed as a result of SPT operation, were considered as interference in the communication channel. The blind signal separation algorithm ncFastICA used in the study is based on the method of independent component analysis. The main task of the modeling was to verify the applicability of blind signal separation methods to counter the interference from SPT in the communication channel. The simulation results proved the possibility of efficient use of blind signal separation algorithms to counter SPT interference. Application of the considered algorithm allows achieving a gain in bit error rate by two and more orders of magnitude. Thus, one of the worst cases of signal reception was considered: the gain from the use of the ncFastICA algorithm is achieved when the bit signal-to- noise ratio is more than 10 dB and when the interference-to-signal ratio in the signal-receiving channel is 10 dB and in the interference receiving channel from the SPT is 30 dB.

A MLE-based blind signal separation method for time–frequency overlapped signal using neural network

The blind signal separation (BSS) algorithm obtains each original/source signal from the observed signal collected by the receiving antenna or sensor. Objective/loss/cost function and optimization method are two key parts of BSS algorithm. Modifying the objective function and optimization from the perspective of neural network (NN) is a novel concept in BSS domain. L_2 regularization is adopted as a term of maximum likelihood estimation (MLE)-based objective function like in Liu et al. (Sensors 21(3):973, 2021); however, we modified the probability density function (PDF) term of the objective function and used the kernel density estimation method for time–frequency overlapped digital communication signal. Multiple optimizers are studied in this paper, and we figure out the right optimizer for our application scenario. A varies of comparison experiments—whose separation results will be provided in forms of correlation coefficient and performance index—are carried out, which indicate our method can converge quickly and achieve satisfactory separation results with performance index (PI) lower than 0.02 when signal-to-noise ratio (SNR) no less than 10dB. Additionally, it demonstrates performance of our method is better than that of typical separation—FastICA, especially for the lower SNR environment, and it shows that our method is not sensitive to the frequency overlap level (FOL) of the source signal, even FOL as high as 100%; it still can get high-precision separation results with textrm{PI}<0.02.

Single-Channel Blind Signal Separation of the MHD Linear Vibration Sensor Based on Singular Spectrum Analysis and Fast Independent Component Analysis.

An MHD vibration sensor, as a new type of sensor used for vibration measurements, meets the technical requirements for the low-noisy measurement of acceleration, velocity, and micro-vibration in spacecraft during their development, launch, and orbit operations. A linear vibration sensor with a runway type based on MHD was independently developed by a laboratory. In a practical test, its output signal was mixed with a large amount of noise, in which the continuous narrowband interference was particularly prominent, resulting in the inability to efficiently carry out the real-time detection of micro-vibration. Considering the high interference of narrowband noise in linear vibration signals, a single-channel blind signal separation method based on SSA and FastICA is proposed in this study, which provides a new strategy for linear vibration signals. Firstly, the singular spectrum of the linear vibration signal with noise was analyzed to suppress the narrowband interference in the collected signal. Then, a FastICA algorithm was used to separate the independent signal source. The experimental results show that the proposed method can effectively separate the useful linear vibration signals from the collected signals with low SNR, which is suitable for the separation of the MHD linear vibration sensor and other vibration measurement sensors. Compared with EEMD, VMD, and wavelet threshold denoising, the SNR of the separated signal is increased by 10 times on average. Through the verification of the actual acquisition of the linear vibration signal, this method has a good denoising effect.

Blind Separation for Wireless Communication Convolutive Mixtures Based on Denoising IVA

In wireless communication systems, signal transmission through a channel can not avoid the influence of noise. When it reaches the receiver, it is accompanied by time delay and attenuation. Therefore, the observed mixture signals at the receiver are convolutional mixed signals with noise contamination. To solve the problem of traditional frequency-domain based convolutive blind signal separation methods have poor separation performance for convolutional mixed signals with noise, this paper propose a denoise-FastIVA method to separate convolutional mixed signals with noise. The basic principle is to use a wavelet transform to denoise the observation signal, reduce the effect of noise on the separation effect of the algorithm, and enhance the robustness of the fast fixed-point independent vector analysis (FastIVA) separation algorithm to noise. Simulation experiments show the effectiveness of a denoise-FastIVA, under the condition that the baseband signal of binary phase shift keying (BPSK) and binary frequency shift keying (2FSK) signal modulation signal is 10 bits respectively,the separation accuracy of linear frequency modulization (LFM) has increased from 87% to more than 94%; BPSK has risen from 83% to over 97%; 2FSK has improved from 81% to over 95%. When the SNR is greater than 10 dB, the separation similarity of denoise-FastIVA for the communication mixed signals is more than 90%, and the demixing signal can demodulate the baseband signal completely and correctly. Baseband signals of experimental BPSK and 2FSK signal modulation signals are 100 bits respectively. When the signal-to-noise ratio is greater than 5dB, the signal separated by denoise-FastIVA method has the highest similarity coefficient with the source signal, and the bit error rate (BER) of the separated BPSK signal and the separated 2FSK signal are the lowest, compared with the traditional frequency domain demixing method and FastIVA algorithm.

Unmixing methods based on nonnegativity and weakly mixed pixels for astronomical hyperspectral datasets

An increasing number of astronomical instruments (on Earth and space-based) provide hyperspectral images, that is three-dimensional data cubes with two spatial dimensions and one spectral dimension. The intrinsic limitation in spatial resolution of these instruments implies that the spectra associated with pixels of such images are most often mixtures of the spectra of the “pure” components that exist in the considered region. In order to estimate the spectra and spatial abundances of these pure components, we here propose an original blind signal separation (BSS), that is to say an unsupervised unmixing method. Our approach is based on extensions and combinations of linear BSS methods that belong to two major classes of methods, namely nonnegative matrix factorization (NMF) and sparse component analysis (SCA). The former performs the decomposition of hyperspectral images, as a set of pure spectra and abundance maps, by using nonnegativity constraints, but the estimated solution is not unique: It highly depends on the initialization of the algorithm. The considered SCA methods are based on the assumption of the existence of points or tiny spatial zones where only one source is active (i.e., one pure component is present). These points or zones are then used to estimate the mixture and perform the decomposition. In real conditions, the assumption of perfect single-source points or zones is not always realistic. In such conditions, SCA yields approximate versions of the unknown sources and mixing coefficients. We propose to use part of these preliminary estimates from the SCA to initialize several runs of the NMF in order to refine these estimates and further constrain the convergence of the NMF algorithm. The proposed methods also estimate the number of pure components involved in the data and they provide error bars associated with the obtained solution. Detailed tests with synthetic data show that the decomposition achieved with such hybrid methods is nearly unique and provides good performance, illustrating the potential of applications to real data.

ОБЗОР ИССЛЕДОВАНИЙ АДАПТИВНОГО ФОРМИРОВАНИЯ ДИАГРАММЫ НАПРАВЛЕННОСТИ И ЦИФРОВОЙ ОБРАБОТКИ СИГНАЛОВ

Introduction: Recently, there has been an active development of algorithms for adaptive beamforming. Such work is required in order to improve the efficiency of receiving a useful signal, especially in mobile communication systems. This article provides an overview of research on this topic. Papers devoted to the improvement of the classical least mean square algorithm, blind signal separation methods, antenna selection algorithms in MIMO systems and other topics of adaptive signal processing are presented. Practical relevance: Adaptive beamforming is a key development for modern wireless communication systems, in particular 5G. Increasing requirements that impose a limitation on the spectral bandwidth of the signal and its center frequency with limited signal power lead to the strategy of forming a narrow signal transmission beam, thereby increasing the energy efficiency of signal transmission and reducing the overhead associated with the reception of various interference. This article provides an overview of studies on this topic. The results of the analysis of works devoted to the improvement of the classical algorithm for minimizing the mean square error, blind signal separation, antenna selection algorithms in systems of several antennas, and other topics of adaptive signal processing are presented. Discussion: It is shown that the main directions of research are focused on the improvement of classical diagramming algorithms, as well as on the improvement of methods for receiving digital signals in MIMO systems. Algorithms for recognizing the type of signal modulation, an algorithm for blind separation of second-order signals, an improved algorithm for automatic gain control, an algorithm for suppressing noise-like interference, and an algorithm for estimating radiation pattern parameters were also considered.

Cyclostationary-based BSS method for time-frequency overlapped BPSK signals in electromagnetic surveillance

A cyclostationary-based blind signal separation (BSS) algorithm is proposed in this paper. The proposed separation algorithm is used for simultaneously received multiple spectrum overlapped BPSK signals in single channel electromagnetic surveillance system. The mixed signal is separated by means of blind adaptive frequency shift (BA-FRESH) filter based on the independent signals’ cyclostationary. This BA-FRESH filtering technique does not require training signals. It can separate the independent signals only by knowing some of their cycle frequencies, which is gained by the second order cycle cumulants in this work. Numerical simulation results proved the effectiveness of this separation method.

Learning mid-IR emission spectra of polycyclic aromatic hydrocarbon populations from observations.

The James Webb Space Telescope (JWST) will deliver an unprecedented quantity of high-quality spectral data over the 0.6-28 μm range. It will combine sensitivity, spectral resolution, and spatial resolution. Specific tools are required to provide efficient scientific analysis of such large data sets. Our aim is to illustrate the potential of unsupervised learning methods to get insights into chemical variations in the populations that carry the aromatic infrared bands (AIBs), more specifically polycyclic aromatic hydrocarbon (PAH) species and carbonaceous very small grains (VSGs). We present a method based on linear fitting and blind signal separation for extracting representative spectra for a spectral data set. The method is fast and robust, which ensures its applicability to JWST spectral cubes. We tested this method on a sample of ISO-SWS data, which resemble most closely the JWST spectra in terms of spectral resolution and coverage. Four representative spectra were extracted. Their main characteristics appear consistent with previous studies with populations dominated by cationic PAHs, neutral PAHs, evaporating VSGs, and large ionized PAHs, known as the PAH x population. In addition, the 3 μm range, which is considered here for the first time in a blind signal separation (BSS) method, reveals the presence of aliphatics connected to neutral PAHs. Each representative spectrum is found to carry second-order spectral signatures (e.g., small bands), which are connected with the underlying chemical diversity of populations. However, the precise attribution of theses signatures remains limited by the combined small size and heterogeneity of the sample of astronomical spectra available in this study. The upcoming JWST data will allow us to overcome this limitation. The large data sets of hyperspectral images provided by JWST analysed with the proposed method, which is fast and robust, will open promising perspectives for our understanding of the chemical evolution of the AIB carriers.

A dichotomy-based variational mode decomposition method for rotating machinery fault diagnosis

Variational mode decomposition (VMD) is a self-adaptive blind signal separation method that can decompose a signal into several band-limited intrinsic mode functions (BLIMFs). However, the choice of decomposition parameters is still a most concerning problem which has a great impact on the decomposition effectiveness. In this paper, the decomposition priority of multiple BLIMFs and the under- and over-decomposition phenomena of VMD are studied systematically. Our results indicate that the essential reason for these phenomena is that the number and value of the initial frequency points do not match the number of actual BLIMF signals and their center frequencies in the input signal. Based on this conclusion, a novel dichotomy-based VMD method is proposed which can adaptively search the center frequencies of the actual components in the input signal without any priority criteria, avoiding the disadvantage whereby the VMD algorithm artificially selects the decomposition parameter. A multi-component simulated signal is constructed to verify the advantage of the proposed method and compare the decomposition results with several methods based on VMD and empirical mode decomposition. The comparisons show that the dichotomy-based VMD has better decomposition performance, as it not only adaptively searches the real components existing in the mixed simulated signal and filters Gaussian white noise, but also obtains weak signals. The proposed method is then applied to diagnose faults in rotating machinery. The noise and vibration signals of a gasoline engine are decomposed by the proposed method, and the results show that the knocking abnormal noise component can be extracted effectively.

Analysis of the regional GNSS coordinate time series by ICA-weighted spatio-temporal filtering

Independent component analysis (ICA) is a blind source signal separation method which can effectively estimate high-order information and thus can effectively extract the common mode errors (CMEs) of a regional global navigation satellite system (GNSS) observation network. In this paper, ICA is used for the weighted filtering (WICA) and the extraction of CMEs of a regional GNSS observation network with the root mean square error (RMSE) of daily solution taken as the weighting factor. Through an analysis of the observed data from 19 valid stations of the Crustal Movement Observation Network of China (CMONOC) in North China, it is shown that the coordinate series precision of 13, 16 and 12 stations in the N, E and U directions, respectively, after filtering by WICA is higher than that by the traditional ICA method. The average correlation coefficient of the coordinate time series for each station after filtering is obviously decreased. Two simulation experiments are designed to extract known CMEs. It is shown that CMEs can be recovered better by WICA and that the standard deviations of most stations after filtering are smaller than those by ICA. The results from the real data and simulation experiments suggest that the RMSE of coordinate series be considered in spatio-temporal filtering.

Blind signal separation method and relationship between source separation and source localisation in the TF plane

A method for solving the instantaneous mixtures of the multiple non-stationary wideband signals in the time–frequency (TF) plane is proposed. The blind source separation is performed by calculation of spatial TF distribution matrices, estimation of a separating matrix, estimation of permutation matrices and scaling matrices and TF synthesis. The simulation result shows that the proposed method improves the signal-to-distortion ratio than the preceding methods. Moreover, also the mutual relationship between the source localisation and the source separation is clarified. The source localisation methods using the source separation result and the source separation method using the source localisation result are proposed.

Blind Signal Separation Methods for InSAR: The Potential to Automatically Detect and Monitor Signals of Volcanic Deformation

AbstractThere are some 1,500 volcanoes with the potential to erupt, but most are not instrumentally monitored. However, routine acquisition by the Sentinel‐1 satellites now fulfils the requirements needed for interferometric synthetic aperture radar (InSAR) to progress from a retrospective analysis tool to one used for near‐real‐time monitoring globally. However, global monitoring produces vast quantities of data, and consequently, an automatic detection algorithm is therefore required that is able to identify signs of new deformation, or changes in rate, in a time series of interferograms. On the basis that much of the signal contained in a time series of interferograms can be considered as a linear mixture of several latent sources, we explore the use of blind source separation methods to address this issue. We consider principal component analysis and independent component analysis (ICA) which have previously been applied to InSAR data and nonnegative matrix factorization which has not. Our systematic analysis of the three methods shows ICA to be best suited for most applications with InSAR data. However, care must be taken in the dimension reduction step of ICA not to remove important smaller magnitude signals. We apply ICA to the 2015 Wolf Volcano eruption (Galapagos Archipelago, Ecuador) and automatically isolate three signals, which are broadly similar to those manually identified in other studies. Finally, we develop a prototype detection algorithm based on ICA to identify the onset of the eruption.

Blind Signal Separation Algorithm for Acoustic Echo Cancellation

This paper is to the blind signal separation algorithm applied to acoustic echo cancellation. This algorithm doesn’t degrade the performance of echo cancellation even in the double-talk. In the closed echo environment, the mixing model of acoustic signals has multi-channel, so the convolutive blind signal separation method is applied. And the mixing coefficients are computed by using the feedback model without directly calculating the separation coefficients. The coefficient updating is performed by iterative computations based on the second-order statistical properties, thus estimating the near-end speech. Many simulations have been performed to verify the performance of the proposed blind signal separation. Simulation results show that the proposed acoustic echo canceller operates safely regardless of double-talk, and the PESQ is improved by 0.6 point compared with the general adaptive FIR filter structure.

Blind Signal Separation Algorithm for Acoustic Echo Cancellation

This paper is to the blind signal separation algorithm applied to acoustic echo cancellation. This algorithm doesn’t degrade the performance of echo cancellation even in the double-talk. In the closed echo environment, the mixing model of acoustic signals has multi-channel, so the convolutive blind signal separation method is applied. And the mixing coefficients are computed by using the feedback model without directly calculating the separation coefficients. The coefficient updating is performed by iterative computations based on the second-order statistical properties, thus estimating the near-end speech. Many simulations have been performed to verify the performance of the proposed blind signal separation. Simulation results show that the proposed acoustic echo canceller operates safely regardless of double-talk, and the PESQ is improved by 0.6 point compared with the general adaptive FIR filter structure.

Extraction of independent non-Gaussian components from non-Gaussian background

Independent component analysis (ICA) is a popular method for Blind Signal Separation applied to multichannel linear recordings. Convolutive mixtures of acoustic signals can be separated by applying ICA in the frequency domain to each sub-band separately or jointly as in Independent Vector Analysis (IVA). However, this way, the mixtures are separated into that many components as is the number of sensors. ICA and IVA are therefore not effective in applications where only one (or few) signals are of practical interest. We introduce a new parameterization of the ICA mixing model that is optimized for the extraction of one signal of interest (SOI). The approach is called Independent Component Extraction (ICE) and is closely related to methods for Blind Signal Extraction (BSE) such as One-unit FastICA. However, BSE methods assume that background signals (the other signals than SOI) are all Gaussian, which limits their accuracy. In this work, we show that ICE can be extended also for a non-Gaussian background so that the accuracy of algorithms is improved, although all components are not separated as in ICA. We also introduce an extension for the extraction of a vector component, so-called Independent Vector Extraction.

Blind Source Separation based on Whale Optimization Algorithm

Aiming at the problem of linear instantaneous aliasing in blind source separation, a new method of blind signal separation using whale optimization algorithm is proposed in this paper, which provides a new research idea and method for blind signal separation. The new method adopts the method of independent component analysis, optimizes the objective function by using the whale optimization algorithm, realizes the blind separation of instantaneous aliasing signals, and effectively avoids the problem of complex parameters and slow convergence rate of the particle swarm optimization algorithm. The simulation results show that the performance of whale optimization algorithm is better than that of particle swarm optimization for blind source separation, and it is effective for blind signal separation.

Acoustic Echo Cancellation Based on Convolutive Blind Signal Separation Method

Acoustic Echo Cancellation Based on Convolutive Blind Signal Separation Method