Multiple Signal Classification Method Research Articles

Angular Superresolution of the Antenna-Array Signals Using the Root Method of Minimum Polynomial of the Correlation Matrix

We propose a new variant of the superresolution method of minimum polynomial of the correlation matrix for estimating the number and angular coordinates of the closely located sources of the signals recorded by the antenna array. The method ensures the direction finding of the sources by determining roots of the denominator of the pseudospectral function (the root method). Special attention is paid to the cases of strongly correlated signal sources and a short sample of the input process when the number of samples is smaller than that of array elements. The efficiencies of the angular superresolution for the root and spectral variants of the method of minimum polynomial, as well as the root MUSIC (MUltiple SIgnal Classification) method are compared.

Damage identification for plate-like structures using ultrasonic guided wave based on improved MUSIC method

The ultrasonic guided wave has emerged as one of the most prominent and promising tools for metal and composite structures in the fields of structural health monitoring (SHM) and nondestructive testing (NDT). This paper presents a novel model-based 2D multiple signal classification (MUSIC) damage identification algorithm for plate-like structures. Unlike the conventional MUSIC algorithm, the proposed model-based 2D MUSIC damage identification algorithm is deduced based on the assumption of near-field according to the propagation model of guided waves. Since scattered signals contain the location information of damage, the cross-correlation function of residual signals received by experiment and scattered signals received by damage scattering model are developed for spatial spectrum estimation MUSIC algorithm. Due to the uncorrelation of signal and noise, the damage can be successfully identified by searching the peak point of spatial spectrum in the monitored area employing the orthogonality of signal subspace and noise subspace. The accuracy and effectiveness of the proposed method are firstly validated by numerical simulation on aluminum plate, and the general applicability is further verified by experiments for the damage identification of laminated composite plate. The numerical and experimental results demonstrate the proposed damage identification algorithm is appropriate for damage identification of plate-like structures with high estimation accuracy and resolution.

Assessment of MUSIC-Based Noise-Robust Sound Source Localization with Active Frequency Range Filtering

We have studied sound source localization, using a microphone array embedded on a UAV (unmanned aerial vehicle), for the purpose of detecting for people to rescue from disaster-stricken areas or other dangerous situations, and we have proposed sound source localization methods for use in outdoor environments. In these methods, noise robustness and real-time processing have a trade-off relationship, which is a problem to be solved for the practical application of the methods. Sound source localization in a disaster area requires both noise robustness and real-time processing. For this we propose a sound source localization method using an active frequency range filter based on the MUSIC (MUltiple Signal Classification) method. Our proposed method can successively create and apply a frequency range filter by simply using the four arithmetic operations, so it can ensure both noise robustness and real-time processing. As numerical simulations carried out to compare the successful localization rate and the processing delay with conventional methods have affirmed the usefulness of the proposed method, we have successfully produced a sound source localization method that has both noise robustness and real-time processing.

Integrated polarisation and diversity smoothing algorithm for DOD and DOA estimation of coherent targets

This study proposes a novel integrated polarisation and diversity smoothing (IPDS) algorithm to solve the direction estimation problem of coherent targets in bistatic multiple-input multiple-output (MIMO) radar with electromagnetic vector sensors (EVSs). The IPDS algorithm partitions the transmit and receive arrays into multiple subarrays based on the sensor elements and the spatial phase shift factors. Then the covariance matrices of the received signals from coherent targets associated with these subarrays are smoothened to restore the rank inadequacy of the reflection coefficient covariance matrix. After applying the IPDS algorithm, the direction of departure (DOD) and the direction of arrival (DOA) are estimated using the estimation of signal parameters via rotational invariance technique and the multiple signal classification methods. The effectiveness of the proposed algorithm in terms of decorrelation factor, estimation accuracy, precision and the spatial spectrum is evaluated through computer simulations. Cramér–Rao lower bound is derived for the bistatic MIMO radar with EVSs to substantiate the proposed algorithm. The simulation results are compared with the diversity smoothing and the polarisation smoothing algorithms available in literature. The results obtained by using the proposed algorithm were found to be impressive.

Mixed Incoherent Far-Field and Near-Field Source Localization under Uniform Circular Array.

A high-accuracy algorithm is presented for the localization of mixed incoherent near-field and far-field narrow-band sources under uniform circular array (UCA). Herein, considering that it is difficult to classify the mixed sources, we first decouple mixed sources’ angles and ranges by calculating centro-symmetric sensors’ phase differences. Then, as the phase differences including only sources’ angles can be transformed as indefinite equations, each source’s azimuth angle and elevation angle are obtained by performing the least squares method. After that, on the basis of the estimated angles of the mixed sources, one-dimensional (1-D) multiple signal classification (MUSIC) method and corresponding spatial spectrum are utilized to identify the mixed sources and estimate the ranges of the near-field sources. Finally, simulation and comparison results verify the superior performance of our proposed algorithm.

High Resolution Methods for Angle of Departure (AOD) and Angle of Arrival (AOA) Estimation in Bistatic Multiple-Input-Multiple-Output (MIMO) Radar Systems

Objectives: In this paper we make a comparative study between two adapted methods for the estimation of the angle of departure and the angle of arrival in a Bistatic multiple input multiple output radar system. Methods: We have adapted the high resolution methods known in the literature to the estimation of the parameters of the sources with a Bistatic radar equipped with an antennas array on the transmit station and on that of the receive one. The two methods are a Derived Multiple Signal Classification method (D-MUSIC) and the Derived Propagator method (D-Propagator). Findings: Through simulation results we have conducted a comparative study between the different adapted methods. We take advantage of each one for different pcolpscenarios in terms of accuracy and resolution.The two proposed methods showed good performances even for low signal-to-noise ratio. In addition, the parameters of the radar systems have a great influence on the performance of the estimators. For this reason, a thorough study on the precision of the high resolution methods according to these parameters was made. Application: By varying the Radar Cross Section we noticed the superiority of D-MUSIC method compared to D-Propagator method for a limited reflection surface what was not the case in the other scenarios where the propagator showed remarkable superiority. Keywords: AOA, AOD, Bistatic Radar, -MUSIC Method, D-Propagator Method, MIMO

Extended-Aperture Unitary Root MUSIC-Based DOA Estimation for Coprime Array

Direction of arrival (DOA) estimation using coprime array is studied, and an extended-aperture unitary root multiple signal classification (MUSIC)-based method is proposed. The geometry of prototype coprime array is modified through some translations, which enable one subarray of the coprime array to achieve aperture extension after unitary transformation. Then, the aperture of the other subarray can also be extended based on the rotational invariance of the extended subarray, and 2-D parameter estimations from the two extended subarrays can be achieved in succession via 1-D root MUSIC-based technique. Finally, unique DOA is determined from the intersection of the two automatically paired and coprime estimations. In contrast to the partial search MUSIC method, the estimation of signal parameters via rotational invariance technique based method, the root MUSIC-based method, and the real-valued cross covariance matrix based method, the proposed method gives better DOA estimation results and manages more sources. Furthermore, it has low complexity for the real-valued decomposition. Simulation results verify the improvement of the proposed approach.

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles.

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

Direct Position Determination of Unknown Signals in the Presence of Multipath Propagation.

A novel geolocation architecture, termed “Multiple Transponders and Multiple Receivers for Multiple Emitters Positioning System (MTRE)” is proposed in this paper. Existing Direct Position Determination (DPD) methods take advantage of a rather simple channel assumption (line of sight channels with complex path attenuations) and a simplified MUltiple SIgnal Classification (MUSIC) algorithm cost function to avoid the high dimension searching. We point out that the simplified assumption and cost function reduce the positioning accuracy because of the singularity of the array manifold in a multi-path environment. We present a DPD model for unknown signals in the presence of Multi-path Propagation (MP-DPD) in this paper. MP-DPD adds non-negative real path attenuation constraints to avoid the mistake caused by the singularity of the array manifold. The Multi-path Propagation MUSIC (MP-MUSIC) method and the Active Set Algorithm (ASA) are designed to reduce the dimension of searching. A Multi-path Propagation Maximum Likelihood (MP-ML) method is proposed in addition to overcome the limitation of MP-MUSIC in the sense of a time-sensitive application. An iterative algorithm and an approach of initial value setting are given to make the MP-ML time consumption acceptable. Numerical results validate the performances improvement of MP-MUSIC and MP-ML. A closed form of the Cramér–Rao Lower Bound (CRLB) is derived as a benchmark to evaluate the performances of MP-MUSIC and MP-ML.

Robust Phase Velocity Dispersion Estimation of Viscoelastic Materials Used for Medical Applications Based on the Multiple Signal Classification Method.

Ultrasound shear wave elastography (SWE) is emerging as a promising imaging modality for the noninvasive evaluation of tissue mechanical properties. One of the ways to explore the viscoelasticity is through analyzing the shear wave velocity dispersion curves. To explore the dispersion, it is necessary to estimate the shear wave velocity at each frequency. An increase of the available spectrum to be used for phase velocity estimation is significant for a tissue dispersion analysis in vivo. A number of available methods suffer because the available spectrum that one can work with is limited. We present an alternative method to the classical 2-D Fourier transform (2D-FT) that uses the multiple signal classification (MUSIC) technique to provide robust estimation of the -space and phase velocity dispersion curves. We compared results from the MUSIC method with the 2D-FT technique twofold: by searching for maximum peaks and gradient-based strategy. We tested this method on digital phantom data created using finite-element methods (FEMs) in viscoelastic media as well as on the experimental phantoms used in the Radiological Society of North America Quantitative Imaging Biomarker Alliance effort for the standardization of shear wave velocity in liver fibrosis applications. In addition, we evaluated the algorithm with different levels of added noise for FEMs. The MUSIC algorithm provided dispersion curves estimation with lower errors than the conventional 2D-FT method. The MUSIC method can be used for the robust evaluation of shear wave velocity dispersion curves in viscoelastic media.

Computationally Efficient Radio Frequency Source Localization for Radio Interferometric Arrays

AbstractRadio frequency interference (RFI) is an ever‐increasing problem for remote sensing and radio astronomy, with radio telescope arrays especially vulnerable to RFI. Localizing the RFI source is the first step to dealing with the culprit system. In this paper, a new localization algorithm for interferometric arrays with low array beam sidelobes is presented. The algorithm has been adapted to work both in the near field and far field (only the direction of arrival can be recovered when the source is in the far field). In the near field the computational complexity of the algorithm is linear with search grid size compared to cubic scaling of the state‐of‐the‐art 3‐D MUltiple SIgnal Classification (MUSIC) method. The new method is as accurate as 3‐D MUSIC. The trade‐off is that the proposed algorithm requires a once‐off a priori calculation and storing of weighting matrices. The accuracy of the algorithm is validated using data generated by low‐frequency array while a hexacopter was flying around it and broadcasting a continuous‐wave signal. For the flight, the mean distance between the differential GPS positions and the corresponding estimated positions of the hexacopter is 2 m at a wavelength of 6.7 m.

A Low-Complexity Robust Beamforming Using Diagonal Unloading for Acoustic Source Localization

In sensor array beamforming methods, a class of algorithms commonly used to estimate the position of a radiating source, the diagonal loading of the beamformer covariance matrix is generally used to improve computational accuracy and localization robustness. This paper proposes a diagonal unloading (DU) method which extends the conventional response power beamforming method by imposing an additional constraint to the covariance matrix of the array output vector. The regularization is obtained by subtracting a given amount of white noise from the main diagonal of the covariance matrix. Specifically, the DU beamformer aims at subtracting the signal subspace from the noisy signal space and it is computed by constraining the regularized covariance matrix to be negative definite. It is hence a data-dependent covariance matrix conditioning method. We show how to calculate precisely the unloading parameter, and we present an eigenvalue analysis for comparing the proposed DU beamforming, the minimum variance distortionless response (MVDR) filter and the multiple signal classification (MUSIC) method. Theoretical analysis and experiments with acoustic sources demonstrate that the DU beamformer localization performance is comparable to that of MVDR and MUSIC. Since the DU beamformer computational cost is comparable to that of a conventional beamformer, the proposed method can be attractive in array processing due to its simplicity, effectiveness and computational efficiency.

Fast direction-of-arrival estimation algorithm for multiple wideband acoustic sources using multiple open spherical arrays

The orthonormal propagator method (OPM) is extended to the spherical harmonic (SH) domain using multiple open spherical microphone arrays (SMAs). Compared with the multiple signal classification method (MUSIC), the computational loads of the OPM can be lower (by one or two orders), because the OPM does not require eigendecomposition of the cross spectral matrix (CSM) of the received sensor signals. Moreover, multiple open SMAs have wider frequency ranges of operation because their large radii reduce their lower frequency bounds and their small radii increase their higher frequency bounds. In this work, the performances of the OPM using both the spherical Fourier transform (SFT) components in the SH domain (SH-SFT-OPM) and the cross spectral matrix of the SFT components in the SH domain (SH-CSM-OPM) are analyzed theoretically and then evaluated in terms of their root-mean-square errors under various signal-to-noise ratio (SNR) conditions and direction-of-arrival estimation snapshots. The performance results are then compared with those of the SH-MUSIC. The simulation results confirm that the SH-CSM-OPM performs similarly to the SH-MUSIC, even for low SNRs, and that the computational complexity of the SH-SFT-OPM is significantly lower than that of the SH-MUSIC.

Direction of arrival estimation of multiple acoustic sources using a maximum likelihood method in the spherical harmonic domain

Direction of arrival estimation (DOA) of multiple acoustic sources has been used for a wide range of applications, including room geometry inference, source separation and speech enhancement. The beamformer-based and subspace-based methods are most commonly used for spherical microphone arrays; however, the former suffers from spatial resolution limitations, while the later suffers from performance degradation in noisy environment. This letter proposes a multiple source DOA estimation approach based on the maximum likelihood method in the spherical harmonic domain and implements an efficient sequential iterative search of maxima on the cost function in the spherical harmonic domain. The proposed method avoids the division of the spherical Bessel function, which makes it suitable for both rigid-sphere and open-sphere configurations. Simulation results show that the proposed method has a significant superiority over the commonly used frequency smoothing multiple signal classification method. Experiments in a normal listening room and a reverberation room validate the effectiveness of the proposed method.

Axial localization using time reversal multiple signal classification in optical scanning holography.

This paper presents a method to identify the axial location of targets in an optical scanning holography (OSH) system. By combining time reversal (TR) technique with the multiple signal classification (MUSIC) method in OSH, axial location can be detected with high resolution. Both simulation and experimental work have been carried out to verify the feasibility of the proposed work.

Numerical Analysis of CNC Milling Chatter Using Embedded Miniature MEMS Microphone Array System

With the increasingly common use of industrial automation for mass production, there are many computer numerical control (CNC) machine tools that require the collection of data from intelligent sensors in order to analyze their processing quality. In general, for high speed rotating machines, an accelerometer can be attached on the spindle to collect the data from the detected vibration of the CNC. However, due to their cost, accelerometers have not been widely adopted for use with typical CNC machine tools. This study sought to develop an embedded miniature MEMS microphone array system (Radius 5.25 cm, 8 channels) to discover the vibration source of the CNC from spatial phase array processing. The proposed method utilizes voice activity detection (VAD) to distinguish between the presence and absence of abnormal noise in the pre-stage, and utilizes the traditional direction of arrival method (DOA) via multiple signal classification (MUSIC) to isolate the spatial orientation of the noise source in post-processing. In the numerical simulation, the non-interfering noise source location is calibrated in the anechoic chamber, and is tested with real milling processing in the milling machine. As this results in a high background noise level, the vibration sound source is more accurate in the presented energy gradation graphs as compared to the traditional MUSIC method.

Precision evaluation method of measuring frequency modulated continuous wave laser distance

With the rapid development of industrial manufacturing, people are stricter and stricter for measuring accuracy and demanding for measurable objects. The demand for a new generation of industrial measurement has evolved from the cooperative target toward the diffuse surface object with faster measurement speed and higher precision. Frequency modulated continuous wave (FMCW) laser ranging technology has proved to be an efficient method in the high-precision ranging fields for absolute distance measurement of a diffuse reflecting target.However, its accuracy is subjected to the stability of continuous-wave light source which cannot scan frequency linearly, which further leads to the instability of beat frequency and poor spectrum resolution. Generally, this problem could be solved by the active linearization technique and the post-processing technique. The most popular method is the non-uniform interval resampling technique, which belongs to the post-processing scheme and uses the zero-crossings or peaks of a long delay Mach-Zehnder interferometer signal as triggers for acquiring the measurement signal data. This technique is low cost, easy to be integrated into FMCW ladar system, and especially suitable for short-range small-band scanning measurements. However, in the large-bandwidth long-distance measurement cases, due to the jitter and dispersion of a long fiber, the spectrum obtained by this method is deteriorated such as the spectral broadening and distance shifting, so the range position cannot be determined precisely. To improve the precision, the fast Fourier transform, chirp Z transform and the multiple signal classification methods are used to obtain the distance spectral information. There are also other methods to solve this problem, but there is no common precision evaluation method to test the validities of these methods.In this paper, a precision evaluation method of measuring the FMCW absolute distance based on two-fiber interferometer is presented. A lower Cramer-Rao lower bound on the variance of distance parameter of the resampled signal in the presence of noise is derived. It shows that the precision of absolute distance is affected by the signal-to-noise ratio of the system and chirp bandwidth. This result is verified experimentally.Besides, the proposed method is not restricted to any distance estimation algorithm. According to this boundary, an optimal distance estimation method could be chosen. Moreover, a simulation of range precision versus signal-to-noise ratio and bandwidth is also demonstrated. When the chirped bandwidth is equal to 20 nm and the signal-to-noise ratio of absolute distance measurement interferometer is raised to more than 70 dB, the obtained precision is below 1 μm. This method can provide a theoretical reference for improving the precision of FMCW distance measurement and it could be widely used in the future.

Joint Estimation of Azimuth and Elevation via Manifold Separation for Arbitrary Array Structures

Direction-of-arrival estimation is a ubiquitous task in array processing. The conventional MUltiple SIgnal Classification (MUSIC) method is often computationally expensive, particularly in the application of joint azimuth and elevation estimation. In this paper, we propose an efficient way to compute the two-dimentional (2D) spatial spectrum. By using the manifold separation technique, we find that the 2-D null-spectrum has the form of discrete Fourier transform (DFT). Moreover, the coefficients of the DFT form can be truncated and computed in parallel. Then by exploiting the fact that the nonzero coefficients are concentrated only at the top-left corner, we present a partial 2-D DFT to compute the 2-D spatial spectrum, which can reduce the latency significantly. In order to improve the estimation performance, we formulate an iterative multidimensional-subspace-fitting estimator in which we apply the first-order Taylor expansion to expand the projection operator. At each iteration, the estimation errors can be computed by solving a set of linear equations. Numerical results demonstrate that the proposed estimator offers better estimation performance compared with the classical 2-D MUSIC.

Efficient implementation of the ML estimator for high‐resolution angle estimation in an unmanned ground vehicle

Using a maximum-likelihood (ML) estimator for high-resolution angle estimation is well-suited for automotive radars. It is superior to other estimators in cases of small antenna aperture, small samples, and correlated signals. However, the computing power it requires prevents its widespread implementation. In this study, the authors propose a novel implementation method of the ML estimator that achieves appropriate memory requirements and computational costs by defining new variables. The proposed method is appropriate for practical implementation because calculation of the objective function is based only on independent multiply-add operations and can be easily parallelised. Real measured data from their radar of an unmanned ground vehicle system demonstrates the performance of the proposed method. Experimental results show that the proposed ML estimator is more robust than the conventional MUltiple SIgnal Classification method under conditions of low signal-to-noise ratio and a small number of snapshots.

Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto- or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications.