Search Results (8)

In complex and diverse practical application scenarios, the challenge of blind source separation under underdetermined and nonlinear conditions is often encountered. To address this challenge, this paper proposes an innovative underdetermined nonlinear bounded component analysis method. This method first employs Multivariate Nonlinear Chirp Mode Decomposition (MNCMD) to process and reconstruct the observed signals, transforming the original underdetermined problem into a positive definite problem. Subsequently, Gaussianization techniques are introduced as a means of nonlinear compensation, successfully converting the nonlinear model into an analyzable linear model, laying a solid foundation for subsequent signal separation. Finally, the signal is separated by the bounded component analysis method, which does not require the source signals to be independent of each other. To validate the effectiveness and superiority of the proposed algorithm, detailed simulation experiments were designed and implemented. The experimental results demonstrate that compared to traditional underdetermined blind source separation algorithms, the algorithm presented in this paper exhibits significant advantages in terms of universality, convergence speed, separation accuracy, and robustness. Furthermore, this paper successfully applies the algorithm to the blind extraction of fetal electrocardiogram (FECG) signals from real datasets. The experimental results show that the algorithm can rapidly and effectively extract clearer and more accurate FECG signals, demonstrating its great potential and value in practical applications. Full article

Independent component analysis (ICA), as a statistical and computational approach, has been successfully applied to digital signal processing. Performance analysis for the ICA approach is perceived as a challenging task to work on. This contribution concerns the complex-valued FastICA algorithm in the range of ICA over the complex number domain. The focus is on the robust and equivariant behavior analysis of the complex-valued FastICA estimator. Although the complex-valued FastICA algorithm as well as its derivatives have been widely used methods for approaching the complex blind signal separation problem, rigorous mathematical treatments of the robust measurement and equivariance for the complex-valued FastICA estimator are still missing. This paper strictly analyzes the robustness against outliers and separation performance depending on the global system. We begin with defining the influence function (IF) of complex-valued FastICA functional and followed by deriving its closed-form expression. Then, we prove that the complex-valued FastICA algorithm based on the optimizing cost function is linear-equivariant, depending only on the source signals. Full article

The separation of time–frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow convergence speed, and poor robustness in low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR) scenarios. To address the above issues, this paper firstly establishes a time delay mixing mathematical model. A robust whitening algorithm is proposed by using the time delay correlation matrix of the observed signal, which is insensitive to noise. Secondly, the joint diagonalized F-parametrization is used as the objective function, and the separation matrix is constructed based on the multiple complex-valued Givens matrices. The complex-valued Givens matrix not only ensures orthogonality in the separation matrix but also effectively reduces the number of parameters to be calculated. This approach guarantees accuracy and simplifies the complexity of the separation process. Finally, the nonlinear chaotic grey wolf optimizer is utilized to search for the optimal rotation angle. The simulation results demonstrate that this algorithm offers higher separation accuracy and requires fewer iterations compared to the traditional algorithm. Additionally, it enhances the accuracy of direction of arrival (DOA) estimation, reduces the communication bit error rate, and enables the joint estimation of the target distance and velocity even in the presence of powerful jamming and a low SNR. Full article

Bridge structures are susceptible to environmental and operational variations (EOVs). Improperly handling these influences may result in incorrect assessments of the bridge’s health condition. Blind source separation (BSS) techniques show promising potential in suppressing the effects of EOVs. However, major challenges such as high data variability, difficulty in parameter selection, lack of reliable decision thresholds, and practical engineering validation have seriously hindered the application of such techniques in bridge health monitoring. Consequently, this paper proposes a new method for bridge damage detection that combines complexity pursuit (CP) and extreme value theory (EVT). This method first uses the exponentially weighted moving average (EWMA) technique to preprocess the measured modal frequencies. The CP algorithm and information entropy are then used to extract structural damage sources from the preprocessed data automatically. Based on the extracted structural damage sources, the damage index (DI) is defined using k-means clustering and Euclidean distance. Following that, the generalized extreme value (GEV) distribution is used to fit the DI data under the normal condition of the bridge, and the damage detection threshold is given according to the fitted distribution. Benchmark data of the KW51 railway bridge are considered to verify the effectiveness of the proposed method along with several comparative studies. The results show that even under strong EOV influences, the proposed method still maintains good damage detection accuracy and robustness, and its effectiveness is superior to some well-known damage detection methods. Full article

Source number estimation plays an important role in successful blind signal separation. At present, the application of machine learning allows the processing of signals without the time-consuming and complex work of manual feature extraction. However, the convolutional neural network (CNN) for processing complex signals has some problems, such as incomplete feature extraction and high resource consumption. In this paper, a lightweight source number estimation network (LSNEN), which can achieve a robust estimation of the number of mixed complex signals at low SNR (signal-to-noise ratio), is studied. Compared with other estimation methods, which require manual feature extraction, our network can realize the extraction of the depth feature of the original signal data. The convolutional neural network realizes complex mapping of modulated signals through the cascade of multiple three-dimensional convolutional modules. By using a three-dimensional convolution module, the mapping of complex signal convolution is realized. In order to deploy the network in the mobile terminal with limited resources, we further propose a compression method for the network. Firstly, the sparse structure network is obtained by the weight pruning method to accelerate the speed of network reasoning. Then, the weights and activation values of the network are quantified at a fixed point with the method of parameter quantization. Finally, a lightweight network for source number estimation was obtained, which was compressed from 12.92 MB to 3.78 MB with a compression rate of 70.74%, while achieving an accuracy of 94.4%. Compared with other estimation methods, the lightweight source number estimation network method proposed in this paper has higher accuracy, less model space occupation, and can realize the deployment of the mobile terminal. Full article

With the development of industrial robots and other mechanical equipment to a higher degree of automation, mechanical systems have become increasingly complex. This represents a huge challenge for condition monitoring. The separation of vibration source signals plays an important role in condition monitoring and fault diagnosis. The key to the separation method of the vibration source signal is prior knowledge, such as of the statistical features of the vibration source signal, the number of vibration sources, and so forth. However, effective prior knowledge is difficult to obtain in engineering applications. This study found that low rank is a common feature of rotating machinery vibration source signals. To address the problem of the difficulty obtaining the signal feature of a vibration source, the multi-low-rank constrained vibration source signal separation method was proposed. Its advantages and effectiveness have been verified through simulations and experimental tests. Compared with the blind source separation method of independent component analysis (BSS-ICA) and the ensemble empirical mode decomposition (EEMD) methods, it obtained better clustering results and higher signal-to-signal ratio (SSR) values. Full article

This paper considers the complex-valued mixing matrix estimation and direction-of-arrival (DOA) estimation of synchronous orthogonal frequency hopping (FH) signals in the underdetermined blind source separation (UBSS). A novel mixing matrix estimation algorithm is proposed by detecting single source points (SSPs) where only one source contributes its power. Firstly, the proposed algorithm distinguishes the SSPs by the comparison of the normalized coefficients of time frequency (TF) points, which is more effective than existing detection algorithms. Then, mixing matrix of FH signals can be estimated by the hierarchical clustering method. To sort synchronous orthogonal FH signals, a modified subspace projection method is presented to obtain the DOAs of FH. One superiority of this paper is that the estimation accuracy of the mixing matrix can be significantly improved by the proposed SSPs detection criteria. Another superiority of this paper is that synchronous orthogonal FH signals can be sorted in underdetermined condition. The experimental results demonstrate the efficiency of the two proposed algorithms. Full article

Artificial neural networks are widely applied for prediction, function simulation, and data classification. Among these applications, the wavelet neural network is widely used in image classification problems due to its advantages of high approximation capabilities, fault-tolerant capabilities, learning capacity, its ability to effectively overcome local minimization issues, and so on. The error function of a network is critical to determine the convergence, stability, and classification accuracy of a neural network. The selection of the error function directly determines the network’s performance. Different error functions will correspond with different minimum error values in training samples. With the decrease of network errors, the accuracy of the image classification is increased. However, if the image classification accuracy is difficult to improve upon, or is even decreased with the decreasing of the errors, then this indicates that the network has an “over-learning” phenomenon, which is closely related to the selection of the function errors. With regards to remote sensing data, it has not yet been reported whether there have been studies conducted regarding the “over-learning” phenomenon, as well as the relationship between the “over-learning” phenomenon and error functions. This study takes SAR, hyper-spectral, high-resolution, and multi-spectral images as data sources, in order to comprehensively and systematically analyze the possibility of an “over-learning” phenomenon in the remote sensing images from the aspects of image characteristics and neural network. Then, this study discusses the impact of three typical entropy error functions (NB, CE, and SH) on the “over-learning” phenomenon of a network. The experimental results show that the “over-learning” phenomenon may be caused only when there is a strong separability between the ground features, a low image complexity, a small image size, and a large number of hidden nodes. The SH entropy error function in that case will show a good “over-learning” resistance ability. However, for remote sensing image classification, the “over-learning” phenomenon will not be easily caused in most cases, due to the complexity of the image itself, and the diversity of the ground features. In that case, the NB and CE entropy error network mainly show a good stability. Therefore, a blind selection of a SH entropy error function with a high “over-learning” resistance ability from the wavelet neural network classification of the remote sensing image will only decrease the classification accuracy of the remote sensing image. It is therefore recommended to use an NB or CE entropy error function with a stable learning effect. Full article