Source Separation Of Signals Research Articles

Identifying sources of interference in civil aviation radio communication

Civil aviation is an important part of public transportation. However, the wireless communication systems used in the approach and tower control phases of traffic control are susceptible to external interference, posing a threat to flight safety. Traditional communication interference solutions are time-consuming and require specialized technicians to troubleshoot. To solve this problem, we propose a real-time method for monitoring abnormal signals and detecting interference sources during aviation radio communications. The method consists of three steps: real-time blind source signal separation using cubic polynomial fitting, abnormal signal monitoring based on discriminative signal residence time, and using Pearson correlation coefficients to identify abnormal interference sources. This comprehensive approach effectively ensures the frequency safety of aviation radio communications. Experiments conducted at different locations in real airport environments demonstrate that this method can efficiently identify the signal bands and their interference sources.

Blind Source Separation of Electromagnetic Signals Based on Swish-Tasnet

Blind Source Separation of Electromagnetic Signals Based on Swish-Tasnet

Tunable nonreciprocal metasurfaces based on a nonlinear quasi-bound state in the continuum.

Nonreciprocal devices are essential and crucial in optics for source protection and signal separation. A hybrid grating system consisting of a silicon grating, a graphene layer, and a silicon waveguide layer is employed to create a high-Q quasi-BIC (bound state in the continuum). Then, the high-Q properties of the quasi-BIC are harnessed to enhance the third-order nonlinear effect of silicon, thereby improving the nonreciprocal characteristics of the device. The nonreciprocal transmittance ratio of the device can be tunable by adjusting the graphene Fermi energy level, achieving tunability ranging from 0.0865 to 30.57 dB. It also enables the best performance of the device over a wider range of frequency bands. This study provides a new, to the best of our knowledge, method for designing tunable nonreciprocal devices with a wide range of potential applications.

Deep Metric Learning With Locality Sensitive Mining for Self-Correcting Source Separation of Neural Spiking Signals.

Automated source separation algorithms have become a central tool in neuroengineering and neuroscience, where they are used to decompose neurophysiological signal into its constituent spiking sources. However, in noisy or highly multivariate recordings these decomposition techniques often make a large number of errors. Such mistakes degrade online human-machine interfacing methods and require costly post-hoc manual cleaning in the offline setting. In this article we propose an automated error correction methodology using a deep metric learning (DML) framework, generating embedding spaces in which spiking events can be both identified and assigned to their respective sources. Furthermore, we investigate the relative ability of different DML techniques to preserve the intraclass semantic structure needed to identify incorrect class labels in neurophysiological time series. Motivated by this analysis, we propose locality sensitive mining, an easily implemented sampling-based augmentation to typical DML losses which substantially improves the local semantic structure of the embedding space. We demonstrate the utility of this method to generate embedding spaces which can be used to automatically identify incorrectly labeled spiking events with high accuracy.

Blind Source Separation and Denoising of Underwater Acoustic Signals

Blind Source Separation and Denoising of Underwater Acoustic Signals

Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors.

Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields.

A nested generalized sidelobe canceller for source counting, localization, and signal separation in reverberant fields

This paper proposes a nested generalized sidelobe canceller (NGSC) for typical array signal processing tasks, including source counting, localization, and signal separation. Multiple blocking matrices are arranged in a nested structure to successively eliminate dominant sources until the remaining signal is predominantly incoherent noise. The microphone signals are dereverberated using a multichannel weighted prediction error algorithm. In source counting, the number of sound sources is determined by tracking the average power of the blocked signal. In source localization, the direction-of-arrival (DOA) is estimated via cosine similarity in conjunction with the golden section search. In signal separation, the estimated DOA enables speech separation in a linearly constrained minimum variance beamformer with postfiltering (LCMV-PF). Monte Carlo simulations are performed to compare the proposed NGSC approach with four baselines, minimum description length, second order statistic of the eigenvalue, multistage Wiener filtering, and multiple signal classification. The results show that NGSC achieves at least 28.80% higher source counting accuracy with a 1.04° lower root mean square degree error than the baselines. The signal-to-distortion ratio achieved by LCMV-PF is 1.52 dB higher than that achieved by the linearly constrained minimum power beamformer and the multichannel Wiener filter.

Blind source separation of electromagnetic signals based on deep focusing U-Net

The unintentional electromagnetic radiation of digital electronic devices during operation can cause information leakage and threaten the information security of the system. In order to explore the leakage level of important information, it is necessary to separate the electromagnetic leakage signal from the complex environmental electromagnetic wave, so the blind source separation technology is studied.Traditional blind source separation methods are mainly unsupervised learning methods, and their separation results are not as expected. In recent years, deep learning technology based on supervised learning has achieved good results in speech separation and other fields, indicating that it is a feasible method.In order to solve the problem of separating source signals from mixed electromagnetic radiation signals and reducing noise interference in electromagnetic safety detection. this paper proposes a Deep Focusing U-Net neural network, which makes the model pay more attention to the features at deeper layer. The network is applied to the blind separation of LCD electromagnetic leakage signals, and the good separation performance proves the effectiveness of this method.

A Blind Source Separation Method Based on Bounded Component Analysis Optimized by the Improved Beetle Antennae Search.

Currently, the widely used blind source separation algorithm is typically associated with issues such as a sluggish rate of convergence and unstable accuracy, and it is mostly suitable for the separation of independent source signals. Nevertheless, source signals are not always independent of one another in practical applications. This paper suggests a blind source separation algorithm based on the bounded component analysis of the enhanced Beetle Antennae Search algorithm (BAS). Firstly, the restrictive assumptions of the bounded component analysis method are more relaxed and do not require the signal sources to be independent of each other, broadening the applicability of this blind source separation algorithm. Second, the objective function of bounded component analysis is optimized using the improved Beetle Antennae Search optimization algorithm. A step decay factor is introduced to ensure that the beetle does not miss the optimal point when approaching the target, improving the optimization accuracy. At the same time, since only one beetle is required, the optimization speed is also improved. Finally, simulation experiments show that the algorithm can effectively separate independent and dependent source signals and can be applied to blind source separation of images. Compared to traditional blind source separation algorithms, it has stronger universality and has faster convergence speed and higher accuracy compared to the original independent component analysis algorithm.

Compound radar jamming recognition based on signal source separation

To deal with various jamming signals based on digital radio frequency memories, a compound jamming signal recognition method based on source signal separation is proposed. In order to overcome label limitation of the supervised learning method in the recognition process, this paper puts forward "Separation + Recognition" strategy. Firstly, the received signals of multiple channels are preprocessed, and the number of signal sources is analyzed through the single-source detection algorithm. Then the received compound jamming signals are processed by source separation, and the independent single jamming signals can be obtained. On this basis, a fast signal compensation algorithm is added to compensate different source signals at overlapping time-frequency points, which effectively increases the integrity of the separated signals. The separated jamming signals are then put into the convolutional neural network for recognition, and the specific jamming types in the compound jamming signals can be obtained. It has been proved that the recognition accuracy of five kinds of compound jamming exceeds 90% when the jamming-to-noise ratio is 0 dB.

The Single-channel blind source separation based on VMD and Tukey M estimation for rolling bearing composite fault diagnosis

Rolling bearing is one of the core components in rotating machinery, and its running status directly affects the operation of the whole equipment. Faults of rolling bearings in the actual working process are often multiple faults. To effectively separate fault sources, the blind source separation method is used for the compound fault diagnosis of rolling bearings. Because of the impact of the number of artificially limited decompositions and quadratic penalty factor on VMD in the decomposition process, and the slow convergence and low accuracy in the objective function of traditional FastICA operation, the VMD algorithm based on the energy loss coefficient and the information entropy is proposed, which adaptively determines the number of modal components and the quadratic penalty factor; The Tukey M estimation is selected as the objective convergence function of the FastICA algorithm to improve its robustness. First, VMD is used to decompose the signal; Secondly, the original signal and the decomposed IMF component are reconstructed, the covariance matrix and the singular value decomposition are constructed, the number of fault sources is estimated by the proximity dominance method, and the decomposed IMF components are filtered through correlation analysis and kurtosis index to build a multi-channel feature set; Finally, the constructed multi-channel feature set is input to the FastICA algorithm based on the Tukey M estimation for the separation of fault source signals to achieve composite fault diagnosis. The compound fault experiment shows that the proposed method in this paper can effectively realize the blind source separation of rolling bearing fault features to realize the compound fault diagnosis in different positions.

Multi-target reconstruction strategy based on blind source separation of surface measurement signals in FMT.

Fluorescence molecular tomography (FMT) is a promising molecular imaging technique for tumor detection in the early stage. High-precision multi-target reconstructions are necessary for quantitative analysis in practical FMT applications. The existing reconstruction methods perform well in retrieving a single fluorescent target but may fail in reconstructing a multi-target, which remains an obstacle to the wider application of FMT. In this paper, a novel multi-target reconstruction strategy based on blind source separation (BSS) of surface measurement signals was proposed, which transformed the multi-target reconstruction problem into multiple single-target reconstruction problems. Firstly, by multiple points excitation, multiple groups of superimposed measurement signals conforming to the conditions of BSS were constructed. Secondly, an efficient nonnegative least-correlated component analysis with iterative volume maximization (nLCA-IVM) algorithm was applied to construct the separation matrix, and the superimposed measurement signals were separated into the measurements of each target. Thirdly, the least squares fitting method was combined with BSS to determine the number of fluorophores indirectly. Lastly, each target was reconstructed based on the extracted surface measurement signals. Numerical simulations and in vivo experiments proved that it has the ability of multi-target resolution for FMT. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.

A probability model with Variational Bayesian Inference for the complex interference suppression in the acoustic array measurement

The microphone array is widely used in acoustics as a non-contact measurement tool, which can obtain multi-dimensional information about the sound source, such as spatial, time, and frequency. The microphone array is not always used in an ideal anechoic chamber environment, making the sound source signal contaminated with the background interference. The separation of the sound source signal from the complex background interference is very challenging, especially when arrays are used in wind tunnel measurements. A probability model on the time–frequency matrix is constructed in this paper to address this issue. The background interference is constructed by the Gaussian mixture model to fit its complex probability distributions adaptively. The sound source signal is constructed as a low-rank model according to its correlation characteristics on the microphones. The distributions of parameters involved in the low-rank and Gaussian mixture model are estimated through variational Bayesian inference, which can realize the separation of the sound source signal from the complex background interference. The performance of the proposed method is evaluated by the numerical simulation and the DLR closed wind tunnel experimental. The robustness and the effectiveness of extracting the sound source signal from the complex background interference are also verified.

Single-channel blind source separation of underwater acoustic signals using improved NMF and FastICA

When automatic monitoring buoys receive mixed acoustic signals from multiple underwater acoustic targets, the statistical blind source separation (BSS) task is used to separate the signals and identify vessel features, which is overly complex and needs improvement, especially noting that noise cancellation and stealth technologies are advancing rapidly. To fill this gap in capability, an improved non-negative matrix factorization (NMF) based BSS algorithm is built on a FastICA machine learning backbone. With this tool, the spatial and spectral correlation of underwater acoustic signals is introduced into the NMF algorithm improved by to resolve the non-convex and feature correlation problems commonly encountered by contemporary NMF algorithms. Moreover, the improved modulation feature adaptability of the NMF increases the local expressivity and independence of the decomposed base matrix, which is proven to meet the requirements of FastICA and used to improve the BSS effect of the FastICA. Simulated and empirical results show that compared with state-of-the-art FastICA and NMF based BSS algorithms, our novel approach obtains better signal-to-noise reduction and separation accuracy while maintaining superior target signal recognition features.

Determined Reverberant Blind Source Separation of Audio Mixing Signals

Audio signal separation is an open and challenging issue in the classical “Cocktail Party Problem”. Especially in a reverberation environment, the separation of mixed signals is more difficult separated due to the influence of reverberation and echo. To solve the problem, we propose a determined reverberant blind source separation algorithm. The main innovation of the algorithm focuses on the estimation of the mixing matrix. A new cost function is built to obtain the accurate demixing matrix, which shows the gap between the prediction and the actual data. Then, the update rule of the demixing matrix is derived using Newton gradient descent method. The identity matrix is employed as the initial demixing matrix for avoiding local optima problem. Through the real-time iterative update of the demixing matrix, frequency-domain sources are obtained. Then, time-domain sources can be obtained using an inverse short-time Fourier transform. Experimental results based on a series of source separation of speech and music mixing signals demonstrate that the proposed algorithm achieves better separation performance than the state-of-the-art methods. In particular, it has much better superiority in the highly reverberant environment.

Simultaneous source separation by shot collocation and strength variation

Simultaneous shooting offers opportunities for significant cost savings in seismic data acquisitions. The most common strategy uses random delay shots where source separation is achieved during the processing stage, thereby doubling source densities. We have determined that the creation of a collocated source survey, where shots are repeated simultaneously at multiple positions, is a viable alternative strategy, with the additional benefit that it may increase source density even further while keeping the acquisition duration unchanged. Source separation is achieved using overcomplete independent component analysis by first by applying a directional wavelet transform to separate source signals with different slowness, then estimating the mixing matrix, followed by solving an optimization problem with an energy constraint combined with a sparseness inducing prior and obtaining the required waveforms. Synthetic tests find average reconstruction quality on the order of 22.1, 15.4, and 8.4 dB if, respectively, three, four, or five shots are acquired in two mixtures. Examination of the true versus obtained zero-offset sections also demonstrates the robustness of our signal recovery strategy. The advantage of shot collocation over conventional acquisitions is that it may triple or even quadruple the source density for unchanged acquisition durations with superior reconstruction results compared with dithered acquisitions using similar blending factors.

Bearing fault diagnosis model using improved Bayesian information criterion-based variational modal decomposition and IGA-SVM

In rotating machinery, the bearing vibration signal is easily covered by the vibration signal of other components, which makes the fault characteristic signal not obvious in the collected vibration signal. In order to better separate the vibration source from the collected vibration signal, a variational modal decomposition optimized by bayesian information criterion (VMDBIC) is proposed. At the same time, the data set is constructed based on the separated vibration source signals, and the support vector machine (SVM) optimized by improved genetic algorithm (IGA) is used to diagnose the bearing fault. Firstly, the Bayesian information criterion is used to calculate the number of potential vibration sources in the vibration signal. The calculated number of vibration sources is used as the number of modal components of variational modal decomposition (VMD) to decompose vibration signal. Secondly, the modal components obtained by VMD are used to construct the eigenmatrix of vibration signal. And the singular value energy spectrum of 3 bearing types (a total of 24 fault types) is calculated based on the eigenmatrix of vibration signal. Finally, the singular value energy spectrum is used as the input of IGA-SVM model to diagnose the bearing fault. The effectiveness of VMDBIC method is verified from three aspects of the time-frequency domain signal, the index of orthogonality (IO), and the index of energy conservation (IEC). The feasibility of the method proposed in this paper is verified from three aspects: fault identification effect of various types of bearings, load changes, and mutual diagnosis of different bearings.

Nanomaterials-Based Electrochemiluminescence Biosensors for Food Analysis: Recent Developments and Future Directions.

Developing robust and sensitive food safety detection methods is important for human health. Electrochemiluminescence (ECL) is a powerful analytical technique for complete separation of input source (electricity) and output signal (light), thereby significantly reducing background ECL signal. ECL biosensors have attracted considerable attention owing to their high sensitivity and wide dynamic range in food safety detection. In this review, we introduce the principles of ECL biosensors and common ECL luminophores, as well as the latest applications of ECL biosensors in food analysis. Further, novel nanomaterial assembly strategies have been progressively incorporated into the design of ECL biosensors, and by demonstrating some representative works, we summarize the development status of ECL biosensors in detection of mycotoxins, heavy metal ions, antibiotics, pesticide residues, foodborne pathogens, and other illegal additives. Finally, the current challenges faced by ECL biosensors are outlined and the future directions for advancing ECL research are presented.

Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process

Blind source separation (BSS) aims to separate latent source signals from their mixtures. For spatially dependent signals in high-dimensional and large-scale data, such as neuroimaging, most existing BSS methods do not take into account the spatial dependence and the sparsity of the latent source signals. To address these major limitations, we propose a Bayesian spatial blind source separation (BSP-BSS) approach for neuroimaging data analysis. We assume the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, for which we construct a new class of Bayesian nonparametric prior models by thresholding Gaussian processes. We assign the vMF priors to mixing coefficients in the model. Under some regularity conditions, we show that the proposed method has several desirable theoretical properties including the large support for the priors, the consistency of joint posterior distribution of the latent source intensity functions and the mixing coefficients, and the selection consistency on the number of latent sources. We use extensive simulation studies and an analysis of the resting-state fMRI data in the Autism Brain Imaging Data Exchange (ABIDE) study to demonstrate that BSP-BSS outperforms the existing method for separating latent brain networks and detecting activated brain activation in the latent sources. Supplementary materials for this article are available online.

Single channel blind source separation of complex signals based on spatial‐temporal fusion deep learning

Blind Source Separation (BSS) of complex signals composed of radar, communication and jamming signals is the first step in an integrated electronic system, which requires higher accuracy of separation. However, the traditional Single-Channel Blind Source Separation (SCBSS) method has low separation accuracy and poor robustness. Aiming at this problem, this paper proposes a SCBSS method based on spatial-temporal fusion deep learning model. This is a deep neural network model, which realizes spatial-temporal of mixed signals by integrating Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). Convolutional Neural Network is used to extract spatial features from input sequences, and BiLSTM is used to mine timing rules of signals. A batch normalisation layer and a dropout layer are added to improve stability and prevent overfitting. The experiments show that the average similarity coefficient of the separated signals is above 0.99 and the Signal-Distortion Ratio (SDR) is up to 27 dB without noise. When the Signal-Noise Ratio is 0–20 dB and Jamming-Signal Ratio is 15 dB, the SDR is 5–30 dB higher than the traditional methods and the single network structure deep learning methods.