Multiple Signal Classification Method Research Articles

Electromagnetic imaging of multiple-scattering small objects: non-iterative analytical approach

Multiple signal classification (MUSIC) imaging method and the least squares method are applied to solve the electromagnetic inverse scattering problem of determining the locations and polarization tensors of a collection of small objects embedded in a known background medium. Based on the analysis of induced electric and magnetic dipoles, the proposed MUSIC method is able to deal with some special scenarios, due to the shapes and materials of objects, to which the standard MUSIC doesn't apply. After the locations of objects are obtained, the nonlinear inverse problem of determining the polarization tensors of objects accounting for multiple scattering between objects is solved by a non-iterative analytical approach based on the least squares method.

High-resolution DOA estimation based on independent noise component for correlated signal sources

In this article, a modified complex-valued FastICA algorithm is utilized to extract the specific feature of the Gaussian noise component from mixtures so that the estimated component is as independent as possible to the other non-Gaussian signal components. Once the noise basis vector is obtained, we can estimate direction of arrival by searching the array manifold for direction vectors, which are as orthogonal as possible to the estimated noise basis vector especially for highly correlated signals with closely spaced direction. Superior resolution capabilities achieved with the proposed method in comparison with the conventional multiple signal classification (MUSIC) method, the spatial smoothing MUSIC method, and the signal subspace scaled MUSIC method are shown by simulation results.

Time-reversal RAP-MUSIC imaging

Time-reversal imaging with the MUltiple SIgnal Classification (MUSIC) method for the location of point targets was first proposed by Devaney et al. In this paper, a recursive time-reversal MUSIC algorithm is proposed. The considered approach is based on the Recursively Applied and Projected (RAP) MUSIC which was first introduced for magnetoencephalographic (MEG) data processing. The main goal of this contribution is to test and study a time-reversal RAP-MUSIC approach for target location and imaging.

A comparative study of model selection criteria for the number of signals

The performance of six existing model selection criteria is compared, which are commonly used in time series and regression analysis, when they are applied to the problem of the number of signals in the multiple signal classification (MUSIC) method. The five criteria are Akaike Information Criterion (AIC), Hannan and Quinn Criterion, Bayesian Information Criterion (BIC), Corrected AIC (AICc) and the recently introduced Vector Corrected Kullback Information Criterion (KICvc) and Weighted-Average Information Criterion (WIC). The general form of the above information criteria consists of a log likelihood function expressed in terms of the eigenvalues of the sample covariance matrix and a unique penalty term. In our estimation procedure, the number of signals is obtained by minimising each of the above criteria. Several simulated data sets, including a linear antenna array data set, are adopted for the comparison purpose. The authors show that, in simple MUSIC additive white noise model, for small sample size n, WIC performs nearly as well as AICc and outperforms other criteria, and for moderately large to large n, WIC performs nearly as well as BIC and outperforms other criteria. Therefore when the authors are not certain of the relative sample size, WIC may be a practical alternative to any criterion. The main purpose is to draw the attention and interests of signal processing researchers to adopt more recent statistical model selection criteria, such as WIC, in general signal processing problems.

MUSIC Imaging and Electromagnetic Inverse Scattering of Multiple-Scattering Small Anisotropic Spheres

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The Foldy-Lax equation is used to derive a multiple scattering model for the multiple-scattering small anisotropic spheres. By this model, if the number of the non-zero singular values of the multistatic response (MSR) matrix is smaller than the number of the antennas, the range space of the MSR matrix is found to be spanned by the background Green's function vectors corresponding to the <formula formulatype="inline"><tex>$x, y$</tex></formula> and <formula formulatype="inline"><tex>$z$</tex></formula> components of the electric and magnetic dipoles induced in each scatterer, which indicates that the multiple signal classification (MUSIC) method could be implemented to obtain the locations of the scatterers. After estimating the positions of the scatterers, a non-iterative analytical method is proposed for retrieving the polarization strength tensors as well as the orientations of the principle axes of each scatterer. Two numerical simulations show that, the MUSIC method and the non-iterative method are efficacious for the nonlinear inverse scattering problem of determining the locations and polarization strength tensors of multiple-scattering small anisotropic spheres. Such methods could also be applied to the inversion of small isotropic spheres or extended to the inversion of small bianisotropic spheres. </para>

A High-Resolution Frequency Estimation Method for Three-Phase Induction Machine Fault Detection

Fault detection in alternating-current electrical machines that is based on frequency analysis of stator current has been the interest of many researchers. Several frequency estimation techniques have been developed and are used to help the induction machine fault detection and diagnosis. This paper presents a technique to improve the fault detection technique by using the classical multiple signal classification (MUSIC) method. This method is a powerful tool that extracts meaningful frequencies from the signal, and it has been widely used in different areas, which include electrical machines. In the proposed application, the fault sensitive frequencies have to be found in the stator current signature. They are numerous in a given frequency range, and they are affected by the signal-to-noise ratio. Then, the MUSIC method takes a long computation time to find many frequencies by increasing the dimension of the autocorrelation matrix. To solve this problem, an algorithm that is based on zooming in a specific frequency range is proposed with MUSIC in order to improve the performances of frequency extraction. Moreover, the method is integrated as a part of MUSIC to estimate the frequency signal dimension order based on classification of autocorrelation matrix eigenvalues. The proposed algorithm has been applied to detect a rotor broken bar fault in a three-phase squirrel-cage induction machine under different loads and in steady-state condition.

Two-Dimensional DOA Estimation of Sound Sources Based on Weighted Wiener Gain Exploiting Two-Directional Microphones

We propose a new method for estimating directions of arrival (DOAs) of sound sources, both in azimuthal and elevation angle, using two directional microphones. This method adopts weighted Wiener gain (WWG) for DOA estimation. WWG is an estimate of the Wiener gain that we proposed for use in automatic gain control to enhance speech that is degraded by additive noise. Angular resolution of WWG arises from spectral subtraction (SS)-based noise reduction involved in the WWG calculation, which enhances the signal from the look direction while suppressing signals from other directions. Because WWG involves two-channel SS, which can deal with instantaneous noise, noise sources need not to be stationary, as they must be with ordinary single-channel SS. We further propose the exploitation of a pair of directional microphones whose front directions are arranged in rotational symmetry. The time difference and amplitude difference between the two-channel signal provided by the microphones are utilized to yield a two-dimensional resolution of DOA. We evaluated the proposed method through computer simulations and compared it to three DOA estimation methods that are based on a cross-correlation function and two popular high-resolution methods of multiple signal classification and minimum variance method. Evaluation results of the source detection rate and estimation accuracy demonstrate the remarkable superiority of our method compared to the other methods in conditions where multiple speech sources exist

Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings

AbstractA non‐parametric system identification‐based model is presented for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy wavelet neural network (WNN) model developed by the authors. The model does not require complete measurements of the dynamic responses of the whole structure. A large structure is divided into a series of sub‐structures around a few pre‐selected floors where sensors are placed and measurements are made. The new model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively, thus resulting in fast training convergence and high accuracy. A new damage evaluation method is proposed based on a power density spectrum method, called pseudospectrum. The multiple signal classification (MUSIC) method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using the data obtained for a 38‐storey concrete test model. The results demonstrate the effectiveness of the WNN model together with the pseudospectrum method for damage detection of highrise buildings based on a small amount of sensed data. Copyright © 2007 John Wiley &amp; Sons, Ltd.

Gaussian mixture-sound field landmark model for robot localization applications

This work proposes a novel Gaussian mixture-sound field landmark model for localization applications, based on the principle that sound fields produced by sources at different locations can be distinguished in terms of their statistical patterns. The experimental results indicate that two microphones are sufficient to differentiate among the patterns. The proposed method is robust against environmental noise and performs accurately in a complex environment. Moreover, it cannot only detect the non-line-of-sight locations when the direct path between the microphones and the location is blocked, but also can distinguish the locations aligned with respect to the line connecting the microphones. However, using only two microphones, these scenarios are difficult to handle by traditional direction-of-arrival or beamforming methods in microphone array research. The experiments were conducted on a quadruped robot platform with an eRobot agent using embedded Ethernet technology. Because of its high accuracy and low-cost, this method is suitable for robot localization in real environments. The experimental results also show that the proposed method with only two microphones outperforms the conventional multiple signal classification method (MUSIC) technique with six microphones at various signal-to-noise ratios.

Resolution of signal sources via spectral moment estimation

In different practical situations it is desired to estimate the number of signal sources and their positions in space or in frequency domain. The first problem is known as the detection or the order estimation and the second one as the resolution. For the resolution problem techniques such as nonlinear least squares (NLSM), high-order Yule-Walker method (HOYW), multiple signal classification (MUSIC), Pisarenko harmonic retrieval method, min-norm method, estimation of signal parameters by rotational invariance technique (ESPRIT), were proposed (Marple, 1987 and Stoica and Moses, 1997). All these high-resolution methods are based on the analysis of the signal covariance matrix. But the covariance matrix is not the only choice to represent the signal spectrum. In different applications (weather radars, synthetic aperture radar (SAR) signal processing, ultrasound imaging in medicine, atmospheric turbulence measurements) the signal spectrum can be modeled through its algebraic moments. Recently a number of efficient nonparametric methods have been proposed to estimate the algebraic spectral moments (Monakov, 1999). The presented paper is an attempt to solve the direction of arrival (DOA) problem via estimation of the algebraic spectral moments. A method proposed in the article is comparable in its accuracy with the MUSIC method. At the same time its computational burden is much lower. The method permits to estimate the signal power of sources easily to complete the full spectral line analysis. Additionally the method shows good robustness in situations when signal sources have noticeable spatial extend

A hybrid scheme of signal process techniques to improve the measurement accuracy of antenna radiation patterns inside an anechoic chamber

Antenna measurement inside an anechoic chamber can significantly suffer from measurement inaccuracy due to the scattering from the shielding walls and internal structures, if the space is not sufficiently large and the absorbers have not significantly reduced the multipath signals, which raises multipath interference. This paper develops a hybrid scheme that integrates the concepts of signal processing techniques including time gating, matrix pencil and multiple signal classification methods to reduce the multipath effects and improve the measurement accuracy. Characteristic comparison between individual method as well as the hybrid scheme will be performed based on experimental examinations.

Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment

The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification) method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART), which is compared to two standard classifiers, MLP (multilayer perceptron) and fuzzy KNN ( nearest neighbors). While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.

Robust speaker's location detection in a vehicle environment using GMM models

Abstract-Human-computer interaction (HCI) using speech communication is becoming increasingly important, especially in driving where safety is the primary concern. Knowing the speaker's location (i.e., speaker localization) not only improves the enhancement results of a corrupted signal, but also provides assistance to speaker identification. Since conventional speech localization algorithms suffer from the uncertainties of environmental complexity and noise, as well as from the microphone mismatch problem, they are frequently not robust in practice. Without a high reliability, the acceptance of speech-based HCI would never be realized. This work presents a novel speaker's location detection method and demonstrates high accuracy within a vehicle cabinet using a single linear microphone array. The proposed approach utilize Gaussian mixture models (GMM) to model the distributions of the phase differences among the microphones caused by the complex characteristic of room acoustic and microphone mismatch. The model can be applied both in near-field and far-field situations in a noisy environment. The individual Gaussian component of a GMM represents some general location-dependent but content and speaker-independent phase difference distributions. Moreover, the scheme performs well not only in nonline-of-sight cases, but also when the speakers are aligned toward the microphone array but at difference distances from it. This strong performance can be achieved by exploiting the fact that the phase difference distributions at different locations are distinguishable in the environment of a car. The experimental results also show that the proposed method outperforms the conventional multiple signal classification method (MUSIC) technique at various SNRs.

MUSIC方式方向探知システムの大型船舶への適用研究

The MUSIC (Multiple Signal Classification) method is different from the conventional direction finder by the Goniometer method and Doppler method that has been using on vessels, in having capability of the measurement the direction of radio wave in azimuth and elevation, and even during jamming. The MUSIC method direction finder in a short wave band is using practically for radio wave monitor in the land. This time, Authors investigated the performance and the usefulness on vessel. The authors measured direction of radio signal in the land. Arrangement of the antennas which were set on circumference with diameter of 80 meters in the land was like a ship, and its interval are similar to actual deck shape of the hull, and measurement precision depend on its strength of radio signals, and the situation that did not become same became clear. In addition, the authors measured direction of radio signal on the actual vessel, and made the electromagnetic model of the hull, and became clear that a measurement error by the hull reduced by adopting the electromagnetic steering vector.

Automatic seizure detection in EEG using logistic regression and artificial neural network

The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. In this study, multiple signal classification (MUSIC), autoregressive (AR) and periodogram methods were used to get power spectra in patients with absence seizure. The EEG power spectra were used as an input to a classifier. We introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression (LR) and the emerging computationally powerful techniques based on artificial neural networks (ANNs). LR as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN-based classifier was more accurate than the LR-based classifier.

HF Radar Ocean Current Algorithm Based on MUSIC and the Validation Experiments

Wuhan University's ocean state measuring and analyzing radar (OSMAR2000), working at around 7.5 MHz in the low region of the HF band with a 120-m-long linear receiving antenna array, can measure ocean surface current at ranges of up to 200 km. An ocean surface current algorithm based on direction finding (DF) using the multiple signal classification (MUSIC) method is developed for the OSMAR2000 radar. This paper describes the OSMAR2000 ocean surface current algorithm based on MUSIC and the validation experiments in the East China Sea. The results of the ocean surface current measurements demonstrate that the OSMAR2000 ocean surface current algorithm based on MUSIC is feasible for the long range of ocean surface current mapping with a sufficient bearing resolution.

Multiple Signal Classification by Aggregated Microphones

This paper introduces the multiple signal classification (MUSIC) method that utilizes the transfer characteristics of microphones located at the same place, namely aggregated microphones. The conventional microphone array realizes a sound localization system according to the differences in the arrival time, phase shift, and the level of the sound wave among each microphone. Therefore, it is difficult to miniaturize the microphone array. The objective of our research is to build a reliable miniaturized sound localization system using aggregated microphones. In this paper, we describe a sound system with N microphones. We then show that the microphone array system and the proposed aggregated microphone system can be described in the same framework. We apply the multiple signal classification to the method that utilizes the transfer characteristics of the microphones placed at a same location and compare the proposed method with the microphone array. In the proposed method, all microphones are placed at the same place. Hence, it is easy to miniaturize the system. This feature is considered to be useful for practical applications. The experimental results obtained in an ordinary room are shown to verify the validity of the measurement.

周囲雑音を用いた無音物体探知における高分解能アレイ信号処理手法の比較

Much attention has been paid to the new idea of detecting objects by using ocean ambient noise affirmatively. This concept is called acoustic daylight imaging. The authors have tried to detect a silent target object using ambient noise by employing a wide-band beam former consisting of an array of receivers. In experimental results obtained in air, we have applied the delay-sum array (DSA) method and the multiple signal classification (MUSIC) method to successfully detect a silent target object in an acoustic noise field generated by a large number of transducers by using a wide-band beam former. In this paper, we applied the minimum variance (MV) method to detect silent targets as one of the promising high resolution technique of the array signal processing method. The relative power levels near the target obtained with MV and MUSIC were compared with those obtained by DSA. Then, the effectiveness of the MV and MUSIC method was evaluated according to the rate of increase and the direction in the maximum relative power levels.

Identification of the number and locations of acoustic sources

The identification of the number and locations of acoustic sources is a fundamental problem of engineering acoustics. The method of multiple signal classification (MUSIC) has been widely used to solve this problem. The main drawback of the MUSIC method is that the unavoidable measurement noise should be identical for all the channels of measurement system. This requirement generally cannot be met in practical application. In this work, a method was proposed to identify the number and locations of multiple incoherent sources. The proposed method was based on the traditional MUSIC method, however, without the basic assumption about the identity of the measurement noise. Besides, a procedure was proposed to improve the calculation efficiency of identification. The numerical and experimental results demonstrated that the identification accuracy of the proposed method was far better than that of the traditional MUSIC method. The results also demonstrated that the proposed calculation procedure was very efficient, only the data at a few frequencies should be used for identification.

Sinusoidal Modeling of Ictal Activity Along a Thalamus-to-Cortex Seizure Pathway I: New Coherence Approaches

Understanding associations in neuronal circuitry is critical for tracing epilepsy pathways. Two new methods of measuring coherence between field potentials and EEG channels are proposed for modeling the level of linear association between channels during epileptic seizures. These methods rely upon modeling the repetitive clonic seizure activity as a sum of sinusoids with varying degrees of phase locking. Estimating the amplitude of sinusoids from correlation and cross-correlation time domain data, we can find the coherences from a ratio of these amplitudes. One method utilizes amplitude finding from the multiple signal classification (MUSIC) technique. The other method uses alterations in amplitude of individual sinusoids and their ratios in a matrix pencil equation formed from cross- and auto-correlation matrices. The corresponding generalized eigenvalues of these equations form the coherence ratios. This utilizes the estimation of signal parameters using rotational invariance techniques (ESPRIT) algorithm to arrive at coherence amplitude ratios. Simulations illustrate that the MUSIC method provides better noise immunity as it out-performs the conventional Fourier transform-based method for coherence estimation. Both coherence estimators reflect presence of sinusoidal components that are propagated or not propagated along a particular transmission pathway. We illustrate the value of both methods by examining the strength of correlation between seizure EEG from specific thalamic nuclei and cortex in a rodent model of generalized epilepsy. The pentylenetetrazol (PTZ) chemoconvulsant model in rats reflects selective activation of the anterior thalamic nucleus. Using both methods, this neuronal element has much larger coherence with cortex than another thalamic region, the posterior thalamus (p < 0.05). These methods isolate the unique contribution of anterior thalamus in the formation of an ictal network and corroborate earlier conventional or periodogram techniques.