Number Of Source Signals Research Articles

DOA Estimation using Random Linear Arrays via Compressive Sensing

In this article we analyze the performance of nonuniform linear arrays for Direction of Arrival (DOA) estimation. We use different classes of sparse recovery algorithms to estimate the direction of arrival of the signal sources and its information. We focus on three array configurations, a structured or virtual array with prefixed potential locations for the elements, a random array and a random array with a restriction on the minimum distance between elements. We provide simulations of the performance of each configuration under these algorithms for different values of aperture, and number of signal sources.

Investigation on Blind Source Separation for Under-determined Mixtures Based on Time-frequency Analysis and Weak Feature Extraction

Blind source separation(BSS) is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration sources.The traditional BSS algorithm is applicable to the number of observed signals is no less to the source signals.BSS performance is limit for the under-determined condition that the number of observed signals is less than source signals.An under-determined BSS method is provided based on the advantage of time-frequency analysis and empirical mode decomposition(EMD).It is suitable for weak feature extraction and pattern recognition.The vibration signal is decomposed by using EMD.The number of source signals are estimated and the optimal observed signals are determined according to the EMD result.Then,the vibration signal and the optimal observed signals are used to construct the multi-channel observed signals.In the end,blind source separation based on time-frequency analysis are used to the constructed signals.Simulation signal and gearbox signals are used to verify the effectiveness of this method.Compared with independent component analysis,BSS based on time-frequency analysis has good performance on signal separation.It is more suitable for weak feature extraction.

Single Guide League Ventricular Late Potentials Extraction Based on Signal Correlation

The detection and analysis of Ventricular Late Potentials (VLP) is a kind of effective means to predict patients with disease outbreaks clinically and understand ventricular mechanism, single guide league system will become a trend in future medicine. This paper proposes a Blind Source Separation (BSS) method to single guide league observed signal with correlation theory, this method will unify wavelet packet transformation and Independent Component Analysis (ICA), which can fully recover VLP, but the extracted ECG signal has distortion, in order to eliminate distortion, it is necessary to reduce the noises of extracted ECG signal, then the source signals can be estimated accurately. It is proved that the proposed method is effective, and able to solve underdetermined BSS, it can get better separation effect than the traditional FastICA algorithm that the number of source signals is equal to the number of observed signals.

Computationally efficient ESPRIT algorithm for direction-of-arrival estimation based on Nyström method

A low-complexity ESPRIT algorithm for direction-of-arrival (DOA) estimation is devised in this work. Unlike the conventional subspace based methods, the proposed scheme only needs to calculate two sub-matrices of the sample covariance matrix, that is, R11∈CK×K and R21∈C(M−K)×K, avoiding its complete computation. Here, M is the number of sensors of the array, K satisfies P≤K≤min(M,N) with P being the number of source signals and N being the number of snapshots. Meanwhile, a Nyström-based approach is utilized to correctly compute the signal subspace which only requires O(MK2) flops. Thus, the proposed method has the advantage of computational attractiveness, particularly when K⪡M. Furthermore, we derive the asymptotic variances of the estimated DOAs. Numerical results are included to demonstrate the effectiveness of the developed DOA estimator.

Application of Power Spectral Density on Estimation of the Number of Source Signals

Blind source separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the complex systems. A new estimating method based on power spectral density (PSD) is presented. When the relation between the number of sensors and that of sources is unknown, the PSD matrix is first obtained by the ratio of PSD of the observation signals, and then the number of source signals can be estimated by clustering the column vectors of PSD matrix. The effectiveness of the proposed method is verified by theoretical analysis and experiments.

Blind Source Separation based on Estimation for the Number of Source Signals using Hough Transform

Blind Source Separation based on Estimation for the Number of Source Signals using Hough Transform

DOA Estimation Algorithm in Switch Antenna Array with a Single Cycle of Sampled Data

For DOA estimation of coherent signals in switch antenna array (SAA), a new fast algorithm is proposed. Instead of conventional sub-space algorithm’s covariance matrix, a Toeplitz matrix is constructed with a single cycle of sampled data. It is proved theoretically that the ranks of the Toeplitz matrix is equal to the number of signal sources and has no relations with the coherency of the signal source. Through eigenvalue decomposition, signal and noise subspace are obtained respectively, then DOA estimation can be done by one-dimensional spectral peak searching according to the MUSIC algorithm. The theoretical analysis and simulation results demonstrate validity and superiority of the novel algorithm.

Fast algorithm for coherent source number estimation

In order to estimate the number of coherent sources, a Hankel matrix with the size of half the number of the received arrays is constructed using snapshot data of observed vectors. And the rank of the Hankel matrix is only related with the number of signal sources, no matter the signals are uncorrelated or coherent. We can get the signal and noise eigenvalues by conducting the singular value decomposition (SVD) to the Hankel matrix, the source number can be obtained by calculating the maximum ratio of each eigenvalue pair. The complexity of the algorithm is reduced greatly as only part of the observed data (single snapshot) is used. The Monte-Carlo simulation results demonstrate the feasibility of the algorithm.

Use of Ground-Based Signals of Opportunity for Smart Projectile Navigation

Global positioning system (GPS) is a widely accepted means of navigation, whether it is for civilian or military means. With the implementation of GPS on smart projectiles, these weapons have been able to achieve remarkable accuracy. Even though the improvements in accuracy are impressive, GPS signals are susceptible to jamming and spoofing by a sufficiently motivated enemy. The work reported here examines the viability of constructing a navigation solution using ground-based signals of opportunity that provide range and range rate information. Using a generalized sensor model encompassing the key error terms, a variety of physical devices are included in the analysis. For a typical indirect fire trajectory, navigation solutions are computed as a function of the number and density of signal sources, terrain type, and sensor errors. Results indicate that navigation solutions can be computed with the same accuracy as current GPS systems with a moderate number of signal sources. Generally, more accurate solutions are obtained when the projectile is directly over the signal sources and there is a variation of signal source location in all three axes.

Fast Detection Algorithm for the Number of Signal Sources Based on Modified Hung-Turner Projection

Fast Detection Algorithm for the Number of Signal Sources Based on Modified Hung-Turner Projection

Blind source separation with unknown number of sources based on hierarchical genetic algorithm

In order to solve blind source separation without any information of the number of source signals,a new algorithm based on hierarchical genetic was proposed.Control layer and parameter layer represent the available number of rows and the concrete value of the available rows of separation matrix respectively.Non-Gaussian was used to measure the independence of the stochastic signals.The kurtosis was maximized/minimized by searching a linear combination of observation signals,and then the independent variables were separated.Computer simulation verifies the validity of the proposed algorithm.

Swallow segmentation with artificial neural networks and multi-sensor fusion

Swallow segmentation is a critical precursory step to the analysis of swallowing signal characteristics. In an effort to automatically segment swallows, we investigated artificial neural networks (ANN) with information from cervical dual-axis accelerometry, submental MMG, and nasal airflow. Our objectives were (1) to investigate the relationship between segmentation performance and the number of signal sources and (2) to identify the signals or signal combinations most useful for swallow segmentation. Signals were acquired from 17 healthy adults in both discrete and continuous swallowing tasks using five stimuli. Training and test feature vectors were constructed with variances from single or multiple signals, estimated within 200 ms moving windows with 50% overlap. Corresponding binary target labels (swallow or non-swallow) were derived by manual segmentation. A separate 3-layer ANN was trained for each participant–signal combination, and all possible signal combinations were investigated. As more signal sources were included, segmentation performance improved in terms of sensitivity, specificity, accuracy, and adjusted accuracy. The combination of all four signal sources achieved the highest mean accuracy and adjusted accuracy of 88.5% and 89.6%, respectively. A–P accelerometry proved to be the most discriminatory source, while the inclusion of MMG or nasal airflow resulted in the least performance improvement. These findings suggest that an ANN, multi-sensor fusion approach to segmentation is worthy of further investigation in swallowing studies.

BLIND SOURCE SEPARATION AND SOUND SOURCE LOCALIZATION ON TIME-FREQUENCY DOMAIN CONSIDERING TIME LAG INFORMATION

The blind source separation and specification of location on time-frequency domain considering time lag information are conducted. The formulation based on the independency of time-frequency domain is presented. First of all, the number of source signals is specified. Next, the locations of source signals are specified. Then, the source signals are obtained. The numerical test is conducted to confirm our method, and then the locations of source signals and source signals are obtained by high accuracy.

Blind source separation and sound source localization on time‐frequency domain considering time lag information

The blind source separation and sound source localization based on independent component analysis on time‐frequency domain considering time lag information between source signals and observation signals are conducted. The formulation based on the independency of time‐frequency domain and the linearity of source signal is presented. The method which can be conducted not only the separation of source signals but also the specification of location of source signals is proposed through the consideration of time lags. Using this method, it can be analyzed even if observation signals include an intermittent noise, under the assumption of some independency of source signals. First of all, the number of source signals is specified through the quotient of complex valued time‐frequency information of two observation signals. Next, the locations of source signals are specified using the relationship of relative time lags between source signal and observation signal. Then, the source signals are obtained by use of the Fourier information. The numerical test is conducted to confirm our method, and then the locations of source signals and source signals are obtained by high accuracy.

Two-Microphone Separation of Speech Mixtures

Separation of speech mixtures, often referred to as the cocktail party problem, has been studied for decades. In many source separation tasks, the separation method is limited by the assumption of at least as many sensors as sources. Further, many methods require that the number of signals within the recorded mixtures be known in advance. In many real-world applications, these limitations are too restrictive. We propose a novel method for underdetermined blind source separation using an instantaneous mixing model which assumes closely spaced microphones. Two source separation techniques have been combined, independent component analysis (ICA) and binary time - frequency (T-F) masking. By estimating binary masks from the outputs of an ICA algorithm, it is possible in an iterative way to extract basis speech signals from a convolutive mixture. The basis signals are afterwards improved by grouping similar signals. Using two microphones, we can separate, in principle, an arbitrary number of mixed speech signals. We show separation results for mixtures with as many as seven speech signals under instantaneous conditions. We also show that the proposed method is applicable to segregate speech signals under reverberant conditions, and we compare our proposed method to another state-of-the-art algorithm. The number of source signals is not assumed to be known in advance and it is possible to maintain the extracted signals as stereo signals.

Independent Component Analysis for Magnetic Resonance Image Analysis

Independent component analysis (ICA) has recently received considerable interest in applications of magnetic resonance (MR) image analysis. However, unlike its applications to functional magnetic resonance imaging (fMRI) where the number of data samples is greater than the number of signal sources to be separated, a dilemma encountered in MR image analysis is that the number of MR images is usually less than the number of signal sources to be blindly separated. As a result, at least two or more brain tissue substances are forced into a single independent component (IC) in which none of these brain tissue substances can be discriminated from another. In addition, since the ICA is generally initialized by random initial conditions, the final generated ICs are different. In order to resolve this issue, this paper presents an approach which implements the over-complete ICA in conjunction with spatial domain-based classification so as to achieve better classification in each of ICA-demixed ICs. In order to demonstrate the proposed over-complete ICA, (OC-ICA) experiments are conducted for performance analysis and evaluation. Results show that the OC-ICA implemented with classification can be very effective, provided the training samples are judiciously selected.

Separation of fault features from a single-channel mechanical signal mixture using wavelet decomposition

This study reports a joint wavelet decomposition and Fourier transform approach to the separation of periodic mechanical source signals from single-channel signal mixture. With this method, the signal mixture is first decomposed to certain wavelet scales. The resulting wavelet coefficients are then Fourier transformed to extract the information pertinent to each signal source from these scales. Next, the number of signal sources is determined and the wavelet coefficients for each signal are constructed in all scale levels. Finally the source signals can be reconstructed using these wavelet coefficients. Since this method does not require the number of sources to be known a priori, it is particularly suitable for mechanical fault signal separation as the number of source signals varies with time and is unpredictable. It is also important to point out that the number of sources is determined without the commonly adopted sequential extraction/learning process and hence the proposed method can be used for on-line fault detection due to the reduced computing burden. The application of this method has been demonstrated using mixed bearing data containing both inner and outer race fault signals.

Study for blind source separation on time‐frequency domain considering phase information

The methods that can be done for the estimation of the number of source signals and separation of source signals using time‐frequency information are proposed as a method of blind source separation. In these methods, some independent conditions are assumed in a time‐frequency domain, and observed signals are expanded to time‐frequency domain using wavelet analysis. However, these methods are not considered phase differences (difference of distance) between source signals and observed signals. A new method is proposed. The method can be done not only for the source separation but also the specification of the locations of source signals considering phase differences. In this method, it is necessary to assume a rather stronger independent condition than former methods. But, on account of this assumption, a function that becomes a real value only when phase differences are coincidence in time‐frequency domain is determined. Using this function, first of all, the number of source signals is calculated, then the locations of source signals and source signals are calculated. The effectiveness of the method is shown using actual synthetic signals.

Blind source separation with unknown and dynamically changing number of source signals

The contrast function remains to be an open problem in blind source separation (BSS) when the number of source signals is unknown and/or dynamically changed. The paper studies this problem and proves that the mutual information is still the contrast function for BSS if the mixing matrix is of full column rank. The mutual information reaches its minimum at the separation points, where the random outputs of the BSS system are the scaled and permuted source signals, while the others are zero outputs. Using the property that the transpose of the mixing matrix and a matrix composed by m observed signals have the indentical null space with probability one, a practical method, which can detect the unknown number of source signals n, ulteriorly traces the dynamical change of the sources number with a few of data, is proposed. The effectiveness of the proposed theorey and the developed novel algorithm is verified by adaptive BSS simulations with unknown and dynamically changing number of source signals.

Automatic target recognition for hyperspectral imagery using high-order statistics

Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective