Multiple Signal Classification Technique Research Articles

A robust direction-of-arrival estimation method for impulsive noise environments

The performance of conventional direction-of-arrival (DOA) estimation methods degrades greatly when there are few snapshots and the noise model is mismatched, especially in impulsive noise environments. To solve this problem, this paper proposes a robust DOA estimation method, which is robust to the probability density function of impulsive noise. First, by exploiting the low-rank decomposition of the residual fitting error matrix and the characteristics of impulsive noise, three conditions of a good cost function in impulsive noise environments are proposed, and a unified cost function framework is established. Based on this, a robust cost function is designed to suppress the contribution of samples with impulsive noise to the cost function. Then, a bi-iterative complex fixed-point algorithm (BI-CFPA) is developed for the nonconvex optimization problem of signal subspace estimation based on the proposed robust cost function. Theoretical analyses indicate that the BI-CFPA has excellent numerical stability and low computational complexity. Finally, with the estimated signal subspace, the multiple signal classification technique is employed to obtain DOA estimates. Simulation results show that the proposed DOA estimator performs better than existing methods in three typical impulsive noise environments, especially under fairly strong impulsive noise.

A Comparison between Multiple-Input Multiple-Output and Multiple-Input Single-Output Radar Configurations for Through-the-Wall Imaging Applications

The performances of a multiple-input multiple-output (MIMO) radar, employing 16 equivalent antennas, and multiple-input single-output (MISO) radar, employing 10 antennas, for through-the-wall imaging applications are analyzed. In particular, imaging algorithms based on the Fourier transform (FT) and the multiple signal classification (MUSIC) available in the literature are compared with the FT-MUSIC hybrid algorithm recently developed by the authors. Three different investigations have been performed. The first, performed analytically, refers to a scenario in which a point scatterer is placed in free space, and the second, addressed numerically using the CST full-wave software, refers to a scenario in which two targets are present, while the last was executed in a real scenario where a metal panel is placed behind a tuff wall. All the algorithms and radar configurations were found to be suitable for accurately reconstructing the position of the investigated target. In particular, applying the FT technique, the MISO configuration has a lower cross-range half-power beamwidths (HPBW) than the MIMO one, while the range HPBW is the same for the two radar configurations. Despite the different number of elements present in the two radar configurations, similar range and cross-range HPBW are obtained for both configurations when MUSIC and FT-MUSIC techniques are employed. The field of view for FT and FT-MUSIC is about 45°, while it is less than 15° for the MUSIC algorithm. The HPBWs obtained with the experimental setup are very close to those obtained in the analytical study. Finally, the proposed experimental MISO radar acquires the data in half the time required by the MIMO one. The numerical results, confirmed by the experimental measurements, seem to indicate in the FT-MUSIC technique the one that provides the best performance for the considered radar configurations.

Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array

Direction-of-arrival (DoA) estimation of electromagnetic (EM) waves impinging on a spherical antenna array in short time windows is examined in this paper. Reflected EM signals due to non-line-of-sight propagation measured with a spherical antenna array can be coherent and/or highly correlated in a snapshot. This makes spectral-based methods inefficient. Spectral methods, such as maximum likelihood (ML) methods, multiple signal classification (MUSIC), and beamforming methods, are theoretically and systematically investigated in this study. MUSIC is an approach used for frequency estimation and radio direction finding, ML is a technique used for estimating the parameters of an assumed probability distribution for given observed data, and PWD applies a Fourier transform to the capture response and produces them in the frequency domain. Although they have been previously adapted and used to estimate DoA of EM signals impinging on linear and planar antenna array configurations, this paper investigates their suitability and effectiveness for a spherical antenna array. Various computer simulations were conducted, and plots of root-mean-square error (RMSE) against the square root of the Cramér–Rao lower bound (CRLB) were generated and used to evaluate the performance of each method. Numerical experiments and results from measured data show the degree of appropriateness and efficiency of each method. For instance, the techniques exhibit identical performance to that in the wideband scenario when the frequency f = 8 GHz, f = 16 GHz, and f = 32 GHz, but f = 16 GHz performs best. This indicates that the difference between the covariance matrix of the signal is coherent and that the steering vectors of signals impinging from that angle are small. MUSIC and PWD share the same problems in the single-frequency scenario as in the wideband scenario when the delay sample d = 0. Consequently, the DoA estimation obtained with ML techniques is more suitable, less biased, and more robust against noise than beamforming and MUSIC techniques. In addition, deterministic ML (DML) and weighted subspace fitting (WSF) techniques show better DoA estimation performance than the stochastic ML (SML) technique. For a large number of snapshots, WSF is a better choice because it is more computationally efficient than DML. Finally, the results obtained indicate that WSF and ML methods perform better than MUSIC and PWD for the coherent or partially correlated signals studied.

Detectability of rotor failure for induction motors through stator current based on advanced signal processing approaches

This paper investigated the ability of the diagnosis techniques and detectability of induction motor faults through a stator current. The proposed techniques are based on advanced signal processing tools. These methods can be classified into: high resolution approaches and time–frequency representations. Sadly, the Fast Fourier transform technique cannot give good results such as the spectral leakage, it needs a big number of measurement data samples. To address these problems, the Multiple Signal Classification technique allows for reducing the spectrum noises and to reduce the computation of signal data samples, requires less memory. However, for the diagnosis in time varying conditions, non-stationary approaches are required to diagnose and detection IM failures in variable speed operation or transient. This article is intended for a comparative study between the spectrogram, the scalogram and the Hilbert-Huang transform. In this context, the results exhibit the effectiveness of the methodology to detect induction machine fault in time varying, it is capable to detect a rotor failure. The performances of these approaches are demonstrated in simulation results using the MATLAB environment and in the experimental validation.

ISAR cross-range scaling based on the MUSIC technique

Cross-range scaling plays an important role in the inverse synthetic aperture radar (ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation (FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity (RV) estimation of the slow rotation target. In this paper, we propose an accurate cross-range scaling algorithm based on the multiple signal classification (MUSIC) method. We first select some range bins with the mono-component linear frequency modulated (LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.

Fast and robust method for flow analysis using GPU assisted diffractive optical element based background oriented schlieren (BOS)

The paper introduces a method for studying flow dynamics using diffractive optical element based background-oriented schlieren (BOS). Our method relies on fringe demodulation using root multiple signal classification technique which provides high robustness against noise. Further, a graphics processing unit (GPU) based implementation is proposed which offers significant improvement in computational efficiency, and thus enables high speed analysis of flows. The performance of the method is demonstrated via numerical simulations and the practical applicability is also shown by analyzing a diffusion phenomenon in liquids by BOS.

Joint Sparsity With Partially Known Support and Application to Ultrasound Imaging

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-sparse recovery algorithms, e.g., simultaneous normalized iterative hard thresholding, subspace greedy methods and subspace-augmented multiple signal classification techniques. We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. We validate the proposed algorithms on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without partially known support. The supporting code is available on https://github.com/AdriBesson/spl2018_joint_sparse.

Off-Grid Direction-of-Arrival Estimation Based on Steering Vector Approximation

In this paper, a novel off-grid direction-of-arrival (DOA) estimation algorithm is proposed based on steering vector approximation. The conventional multiple signal classification (MUSIC) technique estimates the DOA with a uniformly discretized grid by assuming that the true DOAs lie on the grids. In practice, this assumption is rarely satisfied. In order to improve the estimation accuracy, a dense sampling grid is required for the MUSIC technique. Besides the higher computational complexity, it is still not guaranteed that the dense sampling grids will cover the true DOAs. In order to address the off-grid problem with a lower computational complexity, we use the Taylor series expansion to approximate the steering vectors. We first construct a two-dimensional spectrum searching method to simultaneously estimate the nearest on-grid DOA and the corresponding off-grid error. To further improve the estimation accuracy, we then solve an optimization problem to obtain the accurate DOA estimation, which is formulated by the fact that the signal and noise subspaces are orthogonal to each other. Simulation results demonstrate the effectiveness of the proposed off-grid DOA estimation algorithm.

Hard-Wall Radar Imaging: Localization of Objects Shadowed by Metallic Walls With MIMO Radar

The technology of “through-the-wall radar imaging” enables seeing through walls by reconstructing the image based on backscattered waves passing through an optically opaque dielectric wall. However, when the electromagnetic waves cannot penetrate the shielding walls (such as metallic walls), a problem called “hard-wall radar imaging” (HWRI) must be investigated. As the objects are located in the shadow region, where only the diffracted field can be reflected back via edges, conventional imaging techniques are not applicable. The HWRI problem is an imaging scenario with an intrinsic multipath of signal propagation. Therefore, it has been studied using a model-based time-reversal (TR) decomposition of TR operator imaging method. In this paper, an imaging technique with a distributed transmitting and receiving array is proposed as a robust way resolving the problem with a low signal-to-clutter ratio in TR imaging. In this method, the interelement response matrix is collected for the construction of the covariance matrix, from which the geometry of the hard wall is determined from the direction of arrival by the multiple signal classification techniques. Next, the adaptive interference cancellation technique is applied to suppress the interference terms in the image function due to the multipath effects. The experimental results were obtained from a distributed multiple-input multiple-output radar system and show that this method is useful not only for the detection of a weak object echo but also for the localization of multiple targets hidden behind the wall.

Estimating Angle of Arrival (AOA) for Wideband Signal by Sensor Delay Line (SDL) and Tapped Delay Line (TDL) Processors

Angle of arrival (AOA) estimation for wideband signal becomes more necessary for modern communication systems like Global System for Mobile (GSM), satellite, military applications and spread spectrum (frequency hopping and direct sequence). Most of the researchers are focusing on how to cancel the effects of signal bandwidth on AOA estimation performance by using a transversal filter (tap delay line) (TDL). Most of the researchers were using two elements array antenna to study these effects. In this research, a general case of proposed (M) array elements is used. A transversal filter (TDL) in phase adaptive array antenna system is used to calculate the optimum number of taps required to compensate these effect. The proposed system uses a phase adaptive array antenna in conjunction with LMS algorithm to work an angle of arrival (AOA) estimator for wideband signals rather than interference canceller. An alternative solution to compensate for the effect of signal bandwidth is proposed by using sensor delay line (SDL) instead of fixed delay unit since it has variable time sampling in the time domain and not fixed time delay, depending on the angle of arrival of received signals. The proposed system has the ability to estimate two parameters for received signals simultaneously (the output Signal to Noise Ratio (SNR) and AOA), unlike others systems which estimate AOA only. The comparison of the simulation results with Multiple Signal Classification (MUSIC) technique showed that the proposed system gives good results for estimating AOA and the output SNR for wideband signals. (SDL) processor shows better performance result than (TDL) processor. MUSIC technique with both (SDL) and (TDL) processors shows unacceptable results for estimating (AOA) for the wideband signal. 

Estimating Angle of Arrival and Output Signals to Noise Ratio for Wideband Signals

Multiple Signal Classification (MUSIC) algorithm is the most popular algorithm to estimate angle of arrival (AOA) for narrowband signals. This algorithm is based on the use of subspace noise Eigenvectors (Q_n) matrix related to the smallest set of Eigen values of received signal covariance matrix (ϕ_xx). These set of Eigen values are corresponding to thechannels thermal noise. It is found that when the received signal has none zero bandwidth the distribution of Eigen Values magnitudes is changed according to the value of received signals bandwidth. The Eigenvalues magnitudes which are relating to the received sources are spreading on expense of noise sub space Eigen values .This new distribution causes a change in the relating eigenvectors, and now the Eigenvalues corresponding to thermal noise is no longer exist .Since Music algorithm is based on the use of these eigenvectors relating to the thermal noise Eigenvalues, and all these thermal noise subspace Eigenvalues are occupied by wideband signals, Music algorithm is no longer work. In this paper we suggest a new approach to estimate angle of arrival (AOA) as well as output signal to noise ratio (SNR) for wide band signals by using linear adaptive array antenna with transversal filter as a wide band beam-former processor in conjunction with Linearly Constrained Minimum Variance beam-former algorithm (LCMV). A general mathematical model for M array sensors with J taps behind each sensor and with a delay time between taps equivalent to a phase shift (90o) at center frequency is presented. The proposed approach is simulated and tested in different cases for different input levels and different received signals relative bandwidth (10%, 15%, 20% up to 40%) the result shows that with a proper number of taps behind each sensor the system is excellently Compensate the effect of signals bandwidth and estimate the angles of arrivals well the output SNR's of all received signals simultaneously with high accuracy. Under the same assumptions MUSIC technique is tested with transversal filter as a broadband processor, the results were very poor.

Blind CFO estimation based on decision directed MUSIC technique for interleaved uplink OFDMA

This paper deals with blind carrier frequency offset (CFO) estimation based on the multiple signal classification (MUSIC) criterion with Taylor series expansion for interleaved orthogonal frequency division multiple access uplink systems. An efficient estimator is suggested which finds a new CFO vector based on the Taylor series expansion of the one initially given in the presence of CFO. The problem of finding the new CFO vector is formulated as the closed form of a generalized eigenvalue problem, which allows one to readily solve it. Due to more accurate residual CFO estimate, which can provide more accurate residual CFO compensation, this paper also presents a decision-directed MUSIC scheme to improve the CFO estimation performance. The proposed estimator not only can estimate CFO, but also entail less computational load. Several computer simulation results are provided for illustrating the effectiveness of the proposed blind CFO estimator.

High Resolution GNSS Delay Estimation for Vehicular Navigation Utilizing a Doppler Combining Technique

Multipath propagation can cause significant impairments to the performance of Global Navigation Satellite System (GNSS) receivers and is often the dominant source of accuracy degradation for high precision GNSS applications. Commonly used time-of-arrival estimation techniques cannot provide the required estimation accuracy in severely dense multipath environments such as urban canyons. Multipath components are highly correlated and this results in a rank deficiency of the signal autocorrelation matrix. In this paper the Doppler spectrum broadening of the fast fading channel resulting from the motion of the receiver or surrounding objects is employed to decorrelate signal reflections for the purpose of high-resolution estimation of multipath delays through the subspace-based Multiple Signal Classification (MUSIC) technique. Specifically, delay-domain correlator outputs at different Doppler frequencies are combined to enhance the rank of the signal autocorrelation matrix. Simulation and results of real data collected in an urban environment (downtown Calgary) are presented to compare the performance of the proposed method with the spatial-temporal-diversity-based MUSIC technique and a widely available algorithm in commercial GNSS receivers, namely the double-delta correlator technique. The performance metrics are based upon pseudorange and positioning errors, which are derived using an accurate reference trajectory established using a high precision GNSS-INS integrated system.

Coherent MUSIC technique for range/angle information retrieval: application to a frequency‐modulated continuous wave MIMO radar

A coherent two-dimensional (2D) multiple signal classification (MUSIC) processing for the simultaneous estimation of angular and range target positions has been presented. A 2D spatial smoothing technique is also introduced to cope with the coherent behaviour of the received echoes, which may result in a rank deficiency of the signals covariance matrix. The algorithm is analysed with respect to its application to coherent multiple-input multiple-output (MIMO) arrays. The extended baseline which is synthesised can indeed be used to further improve the performance of the system. The results of the algorithm are shown for both simulated and experimental data scenarios. In the latter case, the data are collected by a frequency-modulated continuous wave radar with MIMO functionality that has been designed and realised in cooperation with TNO, the Netherlands. © The Institution of Engineering and Technology 2014.

Improving GSM Services in Nigeria Through Interference Mitigation

Since the early days of wireless communication till today, there has been the simple dipole (oneelement) antenna which functions by radiating and receiving radio frequency (RF) energy equally well in all directions. This rather unfocussed/indiscriminate approach of radiating RF signals is just adequate for simple RF environments where no specific knowledge of the whereabouts of the gsm user is available. It is very clear that as huge amounts of RF power is radiated into the environment, only a small portion of it is received. Given this limitation therefore, this present and obsolete technique attempts at overcoming its challenges by simply boosting the power levels of the signal which it pushes blindly into the atmosphere. Unfortunately, all the gsm operators in Nigeria and indeed in many countries, have continued to deploy obsolete antenna systems (Omnidirectional and Sector antennas) which are characterized by colossal waste in RF energy and interference. This paper presents the result of an investigation and combination of two techniques of Direction of Arrival (DOA) Estimation and Beam Pattern Synthesis (BPS) for suitable implementation in antenna system, the combination of which will ensure that (a) RF energy is not available in areas where gsm users are non-existent (b) RF energy is available in only the areas where there is at least one gsm user with wireless device turned on. The DOA estimation technique proposed in this paper is the Multiple Signal Classification (MUSIC). For the BPS, the Least Mean Square (LMS) technique is used. Validation of the workings of these two applications, which form the Digital Signal Processing (DSP) algorithm for the antenna system, was carried out using the Matlab as the simulation tool. In the case of the MUSIC technique, the simulation was run for four different signals originating from different directions (or angles) using a fixed number of antenna elements. The results show clearly the ability of the system to identify each gsm signal and its direction of arrival. In the case of LMS technique, simulation was run for two signals, one desired and the other undesired (interferer). The results for the pair of signals are displayed graphically, where the unwanted signal is seen mitigated by the system. The combination of DOA and BPS for implementation in gsm networks ensures that the huge waste in RF energy and interference will be a thing of the past as the energy will be available only in the area needed. DOI: 10.5901/jesr.2013.v3n4p121

MUSIC-based Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors Using Flux Signal

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

Multiple signal classification technique for phase estimation from a fringe pattern

The paper introduces a multiple signal classification technique based method for fringe analysis. In the proposed method, the phase of a fringe pattern is locally approximated as a polynomial. The polynomial phase signal is then transformed to obtain signals comprising of only even- or odd-order polynomial coefficients. Subsequently, covariance matrix formulation is applied, and the two sets of coefficients are jointly estimated from the noise subspace of the covariance matrix using the multiple signal classification technique. The method allows simultaneous estimation of multiple coefficients and provides phase without the requirement of complex unwrapping algorithms. The effectiveness of the proposed method is validated through numerical simulation.

Probabilistic inversion of Rayleigh-wave dispersion data: an application to Mt. Etna, Italy

We present a methodology for determining the elastic properties of the shallow crust from inversion of surface wave dispersion characteristics through a fully nonlinear procedure. Using volcanic tremor data recorded by a small-aperture seismic array on Mount Etna, we measured the surface waves dispersion curves with the multiple signal classification technique. The large number of measurements allows the determination of an a priori probability density function without the need of making any assumption about the uncertainties on the observations. Using this information, we successively conducted the inversion of phase velocities using a probabilistic approach. Using a wave-number integration method, we calculated the predicted dispersion function for thousands of 1-D models through a systematic grid search investigation of shear-wave velocities in individual layers. We joined this set of theoretical dispersion curves to the experimental probability density function (PDF), thus obtaining the desired structural model in terms of an a posteriori PDF of model parameters. This process allowed the representation of the objective function, showing the non-uniqueness of the solutions and providing a quantitative view of the uncertainties associated with the estimation of each parameter. We then compared the solution with the surface wave group velocities derived from diffuse noise Green’s functions calculated at pairs of widely spaced (~5–10 km) stations. In their gross features, results from the two different approaches are comparable, and are in turn consistent with the models presented in several earlier studies.

Cross-ambiguity function domain multipath channel parameter estimation

A new array signal processing technique is proposed to estimate the direction-of-arrivals (DOAs), time delays, Doppler shifts and amplitudes of a known waveform impinging on an array of antennas from several distinct paths. The proposed technique detects the presence of multipath components by integrating cross-ambiguity functions (CAF) of array outputs, hence, it is called as the cross-ambiguity function direction finding (CAF-DF). The performance of the CAF-DF technique is compared with the space-alternating generalized expectation–maximization (SAGE) and the multiple signal classification (MUSIC) techniques as well as the Cramér–Rao lower bound. The CAF-DF technique is found to be superior in terms of root-mean-squared-error (rMSE) to the SAGE and MUSIC techniques.

Performance Evaluation of Music and Minimum Norm Eigenvector Algorithms in Resolving Noisy Multiexponential Signals

Abstract— Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper presents a successful attempt at testing and evaluating the performance of two of the most popular types of subspace techniques in determining the parameters of multiexponential signals with real decay constants buried in noise. In particular, MUSIC (Multiple Signal Classification) and minimum-norm techniques are examined. It is shown that these methods perform almost equally well on multiexponential signals with MUSIC displaying better defined peaks. Keywords — Eigenvector, minimum norm, multiexponential, subspace. I. I NTRODUCTION HE task of analyzing multiexponential signals with real decay constants has been the subject of many research efforts for more than five decades. This is because, straightforward as their analysis may appear, such signals do not form an orthogonal base. The signals are usually of the form: 1 () () k Mkk SAen τ −λτ τ==+ ∑ (1) The unknown parameters are usually the number of components (M), the amplitudes (A