Multiple Signal Classification Approach Research Articles

DOA Estimation Algorithm for Reconfigurable Intelligent Surface Co-Prime Linear Array Based on Multiple Signal Classification Approach

Co-prime linear arrays (CLAs) provide an additional degree of freedom (DOF) with a limited number of physical sensors, and thus help to improve the resolution of direction of arrival (DOA) estimation algorithms. However, the DOF of traditional CLA is restrained by the structure of the array, which cannot be adjusted after deployment. In this paper, we propose a DOA estimation algorithm for reconfigurable intelligent surface co-prime linear array (RIS CLA) based on the multiple signal classification approach. Specifically, an RIS CLA is first constructed on the ground of RIS antenna, by turning on/off specific elements at different times. Then, the covariance matrix of the received signal is vectorized, so as to construct a virtual difference array, whose aperture is considerably expanded. Finally, a spectral peak search on the noise subspace of the received signal of the difference array is conducted to obtain the DOA estimation result. Simulations verify the improvement of the proposed algorithm in terms of DOF and resolution. To be specific, the DOF provided by RIS CLA outnumbers that of CLA by more than 30%, and the resolution of the proposed DOA estimation algorithm is effectively improved, with its accuracy increased up to 70% under the low signal-noise-ratio (SNR) scenario, compared with existing algorithms.

Multi-Source DoA Estimation of EM Waves Impinging Spherical Antenna Array With Unknown Mutual Coupling Using Relative Signal Pressure Based Multiple Signal Classification Approach

Spherical antenna array (SAA) is a configuration that scans almost all the radiation sphere with constant directivity. It finds applications in spacecraft and satellite communication. Multiple signal classification (MUSIC) is a widely used multiple source direction-of-arrival (DoA) estimation method because of its low complexity implementation in practical applications. Conversely, it is susceptible to noise, which consequently affects its accuracy of localization. In this paper, MUSIC-based methods that operate at low signal-to-noise ratio (SNR) are developed via relative electromagnetic (EM) wave pressure measurements of a SAA. The proposed methods are the relative pressure MUSIC (RP-MUSIC), and in spherical domain (SH-RP-MUSIC). The developed SH-RP-MUSIC algorithm is in spherical domain thereby allows frequency-smoothing approach for the de-correlation of the coherent source signals towards an enhanced accuracy of localization. Both RP-MUSIC and SH-RP-MUSIC algorithms developed have the ability to estimate the number of active sources that is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of the conventional MUSIC algorithm. Numerical experiments were used to demonstrate the adequacy of the developed algorithms. In addition, measured data from experiment, which is the practically acceptable way to examine any procedure is employed to demonstrate the merits of the developed algorithms against the conventional MUSIC algorithm and other recent multiple source localization method in literature. Finally, in order to achieve DoA estimations with adequate localization accuracy at low SNR using SAA, SH-RP-MUSIC algorithm is a better choice.

Damage detection and characterization of a scaled model steel truss bridge using combined complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification approach

This study aims to investigate the performance of a new damage detection method proposed based on the combination of two signal processing techniques which are complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification (CEEMDAN-MUSIC). The proposed damage detection approach begins with determining the power density spectrum, namely, the pseudospectrum, from the acceleration response of a structure. Then, the CEEMDAN algorithm is used to decompose the vibration signal into a set of intrinsic mode functions (IMFs). Furthermore, the MUSIC algorithm is applied to the first IMF of the processed signal to determine the frequency pseudospectrum, prior to and post the damage states of the structure. The effectiveness of the proposed methodology is experimentally validated using a laboratory-scale model of a steel truss bridge exposed to a white noise excitation. The damage states of the truss bridge are implemented by replacing a specified diagonal element with reduced cross-sectional stiffness. The experimental results demonstrate the superiority of the CEEMDAN-MUSIC method in comparison with the performance of pure MUSIC and traditional frequency domain techniques. The advantages of the proposed technique are also discussed in terms of identifying the presence of the damage, addressing its location, and quantifying the damage levels which are summarized as the damage detection and characterization.

Use of the Cross-Spectral Density Matrix for Enhanced Passive Ultrasound Imaging of Cavitation.

Passive ultrasound imaging is of great interest for cavitation monitoring. Spatiotemporal monitoring of cavitation bubbles in therapeutic applications is possible using an ultrasound imaging probe to passively receive the acoustic signals from the bubbles. Fourier-domain (FD) beamformers have been proposed to process the signals received into maps of the spatial localization of cavitation activity, with reduced computing times with respect to the time-domain approach, and to take advantage of frequency selectivity for cavitation regime characterization. The approaches proposed have been mainly nonadaptive, and these have suffered from low resolution and contrast, due to the many reconstruction artifacts. Inspired by the array-processing literature and in the context of passive ultrasound imaging of cavitation, we propose here a robust estimation of the second-order statistics of data through spatial covariance matrices in the FD or cross-spectral density matrices (CSMs). The benefits of such formalism are illustrated using advanced reconstruction algorithms, such as the robust Capon beamformer, the Pisarenko class beamformer, and the multiple signal classification approach. Through both simulations and experiments in a water tank, we demonstrate that enhanced localization of cavitation activity (i.e., improved resolution and contrast with respect to nonadaptive approaches) is compatible with the rapid and frequency-selective approaches of the FD. Robust estimation of the CSM and the derived adaptive beamformers paves the way to the development of powerful passive ultrasound imaging tools.

3-D Spatial Spectrum Fusion Indoor Localization Algorithm Based on CSI-UCA Smoothing Technique

In the indoor wireless localization environment, the non-line-of-sight receiving signal and low signal-to-noise ratio are usually strongly dominant issues due to the heavy existence of multipath signals, which also restrict the final wireless localization performance seriously. In this paper, a novel indoor localization algorithm based on 3-D multi-array spatial spectrum fusion (3-D-MSSF), which also uses the channel state information (CSI) under uniform circular array (UCA) structure, is proposed. First, in the data assembly process, the number of antennas and the number of observations can be virtually extended by applying a beam space transformation and smoothing technique by using the observed CSI information. Then, the existing multiple signal classification approach is applied on the smoothed data to jointly estimate the 2-D direction-of-arrival angles and the time-of-flight information from the resulting spatial spectrums at each UCA array. And in the grid-fusion explorer process, the estimated parameters are subsequently transmitted to the aggregation center to calculate the location results of each point relative to each access point, which introduces a grid-refinement algorithm in the search grid to improve the localization precision. While the parameters of interest for the final target position can be estimated from a single fused spatial spectrum, which results from fusing all maximum noise subspaces corresponding to the minimum error between each estimated point in the search grid and every set in the 3-D space-time searching grid. Computer simulation results together with the real application experiments in the indoor environment in terms of source position estimation and corresponding RMSE values are given. The proposed 3-D-MSSF method proves a significant indoor positioning performance, which can achieve the final localization accuracy below 1 m even if the line-of-sight signal is blocked, and there exist only multipath path signals at the receiver.

An improved two-dimensional multiple signal classification approach for impact localization on a composite structure

Efficient impact monitoring techniques are important to be researched to ensure the safety of in-service composite structures. Multiple signal classification–based method is a new promising method. This method uses small aperture sensor array arrangement instead of sparse sensor arrangement. It has the advantage of easy sensor array arrangement. By processing the whole sensor array outputs using the multiple signal classification algorithm, transfer function and material property of the structure are not needed. Although some researches have shown the potential of this new method, most of the current researches have been performed based on the far-field assumption on aluminum structures. In engineering applications, composite structures are the main structures which need impact monitoring. Deep research needs to be addressed. Aiming at these problems, an improved two-dimensional near-field multiple signal classification processing algorithm is presented for impact localization on composite structure in this article. The proposed method can simultaneously estimate the impact occurring direction and distance. A uniform carbon fiber composite plate and a real aircraft oil tank made of carbon composite structure with varied thickness are adopted to evaluate the presented method. To deal with the anisotropy performance of the complex composite, the two-dimensional multiple signal classification processing algorithm is also improved by taking into account the existing anisotropy performance.

A multiple signal classification approach for photoplethysmography signals in healthy and athletic subjects

In this paper, various spectral analysis methods are applied to investigate the spectral behaviour of Photoplethysmography (PPG) signals for both healthy normal athletic and non–athletic subjects. The paper investigates the contribution of the PPG spectral components in revealing time trends and how they minimise artefacts that conceal these spectral contents. Both parametric and non–parametric spectral methods are utilised to study the PPG signal. However, eigenstructure methods such as multiple signal classification are found to be more suitable to localise spectral contents of the signal and robustly estimate the heart rate. The heart rate detection correlation is found to be 99.62% and the PPG detection accuracy is 99.61%. These findings show that PPG can be used as an effective tool in investigating cardiovascular disorders as well rather than in investigating only blood oxygenation and heart rate measurements. Furthermore, it can advance the research for existing heart rate variability methods.

Localization of defects in magnetic permeable bar by multiple signal classification approach using rotating dipole model

A simplified search algorithm based on multiple signal classification (MUSIC) approach with rotating dipole model has been proposed to identify the defect locations inside a magnetically permeable bar. This new algorithm identifies the defect locations and the moments of the modelled anti-dipoles without the necessity to decompose the single column lead vector by singular value decomposition (SVD). Here, we find the location of the first dipole by using the rotating dipole model of MUSIC approach and the rest of the defect locations are identified using the orthogonal projection method coupled with rotating dipole model of MUSIC. Three deeply buried defects were modelled as anti-dipoles and the leakage fields due to these three deeply buried defects were simulated by direct forward calculation. Using the calculated data as an input, it has been possible to identify the number of defects and their locations by this new approach with significant reduction in computations, even in the presence of reasonable levels of additive noise.

Defect localization by orthogonally projected multiple signal classification approach for magnetic flux leakage fields

Magnetic flux leakage technique is used for defect detection inside a magnetically permeable bar by measuring the leakage fields outside the bar. Defects of varying sizes in a magnetically permeable bar have been modelled as localized anti-dipoles with different moments. These defect locations and moments have to be determined based on the measurement of the leakage fields in the presence of random noise. Multiple Signal Classification (MUSIC) approach has been used to identify the defect locations and the moments of these defects. After finding the location of the first dipole representing the larger defect, using orthogonal projection of the measured magnetic field data away from the first defect location, location of the next dipole is identified by MUSIC. This process is continued until all the defects are exhausted. The leakage fields from three deeply buried defects were simulated by direct forward calculation and the resulting data were utilized for inversion using this approach. It has been possible to identify the number of defects and their locations by this approach even in the presence of reasonable levels of additive noise.

A Whole-Head SQUID System in a Superconducting Magnetic Shield

Sensitivity of a SQUID system in a superconducting magnetic shield is studied. The SQUID sensors are made of SNS(superconductor/normal metal/superconductor) junctions. Sensitivities of the SQUID system of SNS junctions in a superconducting magnetic shield are more than 100 times better than those of the same SQUID system in a magnetically shielded room of permalloy. The best sensitivity of the SQUID system of SNS junctions is 0.9 fT/(Hz) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/2</sup> at 100 Hz in a superconducting magnetic shield. We performed a experiment of somatosensory evoked magnetic held of human brains to study the N20 component at 20 ms after stimulation. We estimated locations in the brain of the evoked current dipoles for N20 by a extension of a radar theory named MUSIC(multiple signal classification approach). The locations of the evoked current dipole for N20 estimated by MUSIC is confirmed exactly the same as the locations of the current dipole estimated by the traditional iterative least-squares fitting. This is the best agreement between the MUSIC estimation and the conventional estimation of current dipoles to our knowledge.

Efficiency Evaluation of the Unconditional Maximum Likelihood Estimator for Near-Field DOA Estimation

In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA) of near-field sources is evaluated. Four parameters are considered in this evaluation: angular separation among sources, signal-to-noise ratio (SNR), number of snapshots, and number of sources (multiple sources). Simulations are conducted to illustrate the UML performance to compute the DOA of sources in the near-field. Finally, results are also presented that compare the performance of the UML DOA estimator with the existing multiple signal classification approach. The results show the capability of the UML estimator for estimating the DOA when the angular separation is taken into account as a critical parameter. These results are consistent in both low SNR and multiple-source scenarios.