An extended alternating projection neural networks based weak-signal separation algorithm

An efficient algorithm for blind separation of binary symmetrical signals is proposed, which don’t depend on the statistical characteristics of source signals. The mixture matrix is estimated accurately by using the relations of sensor signals in case of no noise and it is also proved in this paper, and the estimated mixture matrix is a primary column transformation of the original mixture matrix by the algorithm, through which the source signals are recovered by permutations and sign changes of their rows. In practice, they can be corrected by introducing headers in the bit-streams and differently encoding them. The algorithm is shown simple and efficient in last simulations.

In correlation‐based signal separation algorithms, the received mixed signals are fed to a de‐coupling system designed to minimize the output cross‐correlation functions. If minimizaion is perfect, each of the system's outputs carries only one signal independent of the others. In these algorithms, the computation burden of the output cross‐correlation functions normally slows down the separation algorithm. This paper, describes a computationally efficient method for off‐line pre‐computation of the needed cross‐correlation functions. Explicit formulas have been derived for the output cross‐correlation functions in terms of the received input signals and the de‐coupling system parameters. Then, it is shown that signal separation amounts to the least‐squares solution of a system of linear equations describing these output cross‐correlation functions, evaluated over a batch of lags. Next, a fast RLS‐type adaptive algorithm is devised for on‐line signal separation. In this respect, an algorithm is derived for updating the de‐coupling parameters as data comes in. This update is achieved recursively, along the negative of the steepest descent directions of an objective cost function describing the output cross‐correlation functions over a batch of lags, subject to equal output power constraints. Illustrative examples are given to demonstrate the effectiveness of the proposed algorithms. Copyright © 2002 John Wiley & Sons, Ltd.

Blind single-channel source separation is a long-standing machine learning and signal processing problem. Traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints. Generative adversarial network (GAN) are free from statistical constraints and samples, but the role of adversarial training in the task of BSS has not been fully demonstrated. Therefore, a new separation network model that enables to learn the known separated signal distribution from the stepwise fine estimation of the unknown mixture distribution was presented in this paper, and a self-attention mechanism was introduced to solve the problem of the blurring details of the generated image by the generator which preserves image details in the process of image separation. Compared with the existing single-channel blind source separation algorithm based on generative adversarial network-neural egg separation (NES), the detailed information of this new separation algorithm is more prominent, and the source signal in the mixed image has been separated more effectively, and has better separation performance than the classic blind source separation algorithms.

A frequently encountered problem in signal processing is harmonic retrieval in additive colored Gaussian or non-Gaussian noise, especially when the frequencies of the harmonic signals are very close in space. The purpose of this paper is to develop an efficient Blind Source Separation (BSS) algorithm from linear mixtures of source signals, which enables to separate harmonic source signals using only one observed channel signal even if the frequencies of the harmonic signals are closely spaced. First, we establish the BSS based harmonic retrieval model in additive noise by using the only one observed channel, and analyze the fundamental principle by utilizing BSS method to retrieve harmonics. Then, we propose a BSS-based approach to the harmonic retrieval by resorting the concept of W-disjoint orthogonality in the over-complete BSS situation, and as a result, we get the separation algorithm using only one channel mixed signals. Simulation results show that the proposed separation algorithm-BSS-HR is able to separate the harmonic source signals.

The problem of blind separation of signals in a post-nonlinear mixture is addressed in this paper. A post-nonlinear mixture is formed by a component-wise nonlinear distortion of a linear mixture. Hence, a nonlinear adjusting part, placed in front of the linear separation structure, is needed to compensate for the distortion in separating such signals. The learning rules for the post-nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for the blind separation of post-nonlinearly mixed suband super-Gaussian signals is proposed, based on some previous works. Multilayer perceptrons are used in this algorithm to model the nonlinear part of the separation structure. The algorithm switches between sub- and super-Gaussian probability models during learning according to a stability condition, and it operates in a block-adaptive manner. The effectiveness of the algorithm is verified by experiments on artificial and natural signals.

An efficient method for parameter estimation and separation of multi-component LFM signals

In this work, the authors describe an algorithm for the real-time separation of mixed non-stationary signals. The algorithm is based on entropy maximization of the system output, and the final recursive updating equation is simple and easy to implement.

Most of the existing algorithms for blind sources separation have a limitation that sources are statistically independent. However, in many practical applications, the source signals are nonnegative and mutual statistically dependent signals. When the observations are nonnegative linear combinations of nonnegative sources, the correlation coefficients of the observations are larger than these of source signals. In this letter, a novel Nonnegative Matrix Factorization (NMF) algorithm with least correlated component constraints to blind separation of convolutive mixed sources is proposed. The algorithm relaxes the source independence assumption and has low-complexity algebraic computations. Simulation results on blind source separation including real face image data indicate that the sources can be successfully recovered with the algorithm.

Recently, the use of unmanned aerial vehicles for both reconnaissance and fire strikes has been increasing. The control of such vehicles and the transmission of telemetry information are carried out using a combined command and telemetry radio link and frequency hopping spread spectrum technology. In such conditions, the task of developing automatic algorithms for detecting and separating radio links for portable radio monitoring devices becomes especially relevant. The aim of this work is to extend the capabilities of portable radio monitoring devices to detect unmanned aerial vehicles’ radio signals in sophisticated electronic environment by automating signal processing. Formally, the research task is to detect and separate several frequency hopping spread spectrum signals in same frequency band. To solve this task, it is first necessary to detect the frequency channels and estimate frequency and, if necessary, temporal and modulation parameters of the signals in these channels. Estimates of signal parameters values are recorded in associative arrays. The paper proposes a scheme of the algorithm for automatic detection and separation of signals with frequency hopping spread spectrum, as well as the structure of the array with the results of frequency band analysis. In the study of developed algorithm, we considered the practically important option of separating radio links with frequency hopping spread spectrum by the spectral width of the frequency element and the value of the signal-to-noise ratio in the frequency channel, since these features can be quite easily distinguished in the frequency domain. The radio links separation was performed by cluster analysis of scatter plots of parameter pairs estimates using a Gaussian mixture model. The developed algorithm can be implemented in existing and prospective portable radio monitoring devices to automatically detect and determine the number of unmanned aerial vehicles by their radio links signals, in particular, under conditions of a priori uncertainty about the values of signal parameters.

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.

There are a large variety of applications that require considering sources that usually behave light or strong dependence and this is not the case that common blind signal separation (BSS) algorithms can do. The purpose of this paper is to develop non-parametric BSS algorithm for linear dependent source signals, which is proposed under the framework of contrast method. The contrast function is derived from the Schweizer-Wolff measure of pairwise dependence between the variables. Simulation results show that the proposed algorithm is able to separate the dependent signals and yield ideal performance.

Choosing a proper Region of Interest (ROI) for Remote Photoplethysmography (rPPG) is essential and a challenging first step, and it has a direct effect on the accuracy and reliability of the overall heart rate (HR) algorithm. Non-skin areas have no contribution to the HR information; however, few works have tackled the issue of non-skin pixels included in the ROI. First, this paper considers improving the quality of the rPPG signal by filtering out non-skin pixels included within the ROI. The feasibility of employing skin segmentation for ROI definition is demonstrated. Then, this technique is compared with our previous real-time rPPG-based method. Moreover, we explore the effect of extracting the HR from three ROIs using signal fusion. Second, we give a comprehensive account of the examined methods in our algorithm for face detection, face tracking, skin detection, and blind signal separation. Finally, we compare our rPPG measurements with ground truth values obtained from a commercial pulse oximeter. Based on the simulation results, the proposed algorrithm significantly improves the quality of the rPPG technique.

We present an efficient algorithm for the blind signal separation (BSS) problem with convolutive signal mixtures, as it usually appears in acoustics, e.g., in the cocktail party problem. Since acoustical signals are typically nonstationary and nonwhite, we make use of these two statistical properties in the formulation of the blind cost function. In order to achieve true signal separation, the algorithm aims at finding a single polynomial matrix, the convolutive separation matrix that jointly diagonalizes a set of measured spatiotemporal correlation matrices. Minimizing the cost function turns out to be mathematically equivalent to a convolutive joint approximate diagonalization problem (CJAD). In order to increase the initial convergence rate, an Armijo line search is incorporated into the update. The final algorithm operates primarily in the frequency domain (fast convolution techniques) even though the cost function and gradient are formulated in the time domain. This approach reduces the computational complexity and avoids the so-called permutation problem.

The algorithm for separation of signals from two different wireless standards (Bluetooth and IEEE 802.11b standard), operating within the same frequency band is proposed in this paper. The components separation is performed using the time-frequency representation and the concept of Compressive Sensing. Knowing the signals nature, it is possible to select just a small set of time-frequency points that entirely belongs to the IEEE 802.11b signal. These points are extracted from the original time-frequency representation and are used to recover the full signal by using Compressive Sensing method. Once when the components of IEEE 802.11b signal are known, it is possible to reconstruct the remaining components in the band, belonging to the Bluetooth signal. Unlike the conventional separation methods, such as windowing or filtering, this approach works well even in the case of overlapping signals as well. The theory is proved with experimental results.

In digital signal processing, spectral source separation involves recovering the original signal components from the set of mixed signals in the spectral domain. There are many applications where the signal separation continues to be the underlying problem to be solved. In this paper, we present a computationally efficient algorithm for performing narrowband signal separation from signal mixtures for a special set of mixing coefficients in the frequency domain. The problem dealt with in this paper, apparently looks under-determined while we prove that there is a unique solution to the problem. The proposed algorithm is proved for correctness through MATLAB simulations. Results show that the proposed algorithm is efficient in terms of computational complexity which is in line with the theoretical measurements. In addition to the computational gain, the proposed algorithm is suited for high performance computing architectures like OpenCL, CUDA because the solution makes use of standard parallel computing libraries.