Multiple Signal Classification Algorithm Research Articles

Non‐contact monitoring of human heartbeat signals using mm‐wave frequency‐modulated continuous‐wave radar under low signal‐to‐noise ratio conditions

Radar has been shown potentially to be used for non-contact sensing of biosignals in a more comfortable and easier way than wearable and contact devices. By detecting and extracting body motions linked to physiological activities, accurate estimation of the heart rate is possible. However, the detection and estimation of heartbeat signal in practical applications is disturbed seriously by noise and respiration harmonic signal. In this study, a multi-level coarse-to-fine method is proposed for heartbeat rate (HR) estimation with a frequency-modulated continuous-wave radar operating at 60 GHz utilised to extract the heartbeat information of human body under low signal-to-noise ratio conditions. First, the frequencies, which are likely to be the HR, are extracted in the coarse estimation part. Then, evaluations about the extracted frequencies are conducted in the fine estimation part. Finally, the multiple signal classification algorithm is utilised to separate the HR frequency from the respiration harmonics, which solves the frequency resolution issue caused by the observation window size, and achieves high accuracy estimation of HR. The experiments on measured data demonstrate the effectiveness of the proposed method. Compared with the state of the art, the proposed method has superiority in terms of estimation accuracy of HR.

Spatial Covariance Matrix Reconstruction for DOA Estimation in Hybrid Massive MIMO Systems With Multiple Radio Frequency Chains

Multiple signal classification (MUSIC) has been widely applied in multiple-input multiple-output (MIMO) receivers for direction-of-arrival (DOA) estimation. To reduce the cost of radio frequency (RF) chains at millimeter-wave bands, hybrid analog-digital structure has been adopted in massive MIMO transceivers. In this situation, received signals at the antennas are unavailable to the digital receiver, and as a consequence, the spatial covariance matrix (SCM), which is essential in MUSIC algorithm, cannot be obtained using traditional sample average approach. Based on our previous work, we propose a novel algorithm for SCM reconstruction in hybrid massive MIMO systems with multiple RF chains in this paper. By switching the analog beamformers to a group of predetermined DOAs, SCM can be reconstructed through the solutions of a set of linear equations. Furthermore, a low-complexity algorithm, as well as a careful selection of the predetermined DOAs, will be also presented in this paper. Simulation results show that the proposed algorithms can reconstruct the SCM accurately so that MUSIC algorithm can be well used in hybrid massive MIMO systems with multiple RF chains.

DOA Estimation Aided by Magnitude Measurements

This correspondence discusses the estimation of direction-of-arrival (DOA) in a magnitude-aided antenna array (MA-AA), where magnitude-only radio frequency (RF) chains are introduced into the classical AA to acquire magnitude measurements. DOAs are initially estimated by the multiple signal classification (MUSIC) algorithm based on the complex-valued measurements from the conventional antennas. After griding the neighborhoods of these initial DOAs, the DOA estimation problem with the hybrid observations in MA-AA is converted as the recovery problem of sparse signals, which can be resolved by generalized approximate message passing (GAMP). Due to the “spatial leakage” effect, non-zero clusters appear around true DOAs. Their positions (i.e., non-zero supports) provide DOAs estimations. Moreover, DOAs and supports remain unchanged in several snapshots, then common supports are shared. Therefore, the cluster-sparse property of sparse signals is exploited by modeling a hidden Markov-tree (HMT) in the shared supports, on which belief propagation (BP) is executed to recover the binary probabilities of supports. Some unknown hyper-parameters in GAMP and BP are learned by expectation-maximization (EM). In comparison to existing estimators, EM-BP-GAMP shows advantages on DOA estimation, computational complexity, and DOA resolution. With the EM-BP-GAMP estimator, MA-AA is more energy-efficient than the classical AA. These advantages are successfully validated by experimental results.

Direction-of-Arrival Estimation of Electromagnetic Wave Impinging on Spherical Antenna Array in the Presence of Mutual Coupling Using a Multiple Signal Classification Method

A spherical antenna array (SAA) is the configuration of choice in obtaining an antenna array with isotropic characteristics. An SAA has the capacity to receive an electromagnetic wave (EM) with equal intensity irrespective of the direction-of-arrival (DoA) and polarization. Therefore, the DoA estimation of electromagnetic (EM) waves impinging on an SAA with unknown mutual coupling needs to be considered. In the spherical domain, the traditional multiple signal classification algorithm (SH-MUSIC) is faced with a computational complexity problem. This paper presents a one-dimensional MUSIC method (1D-MUSIC) for the estimation of the azimuth and elevation angles. An intermediate mapping matrix that exists between Fourier series and the spherical harmonic function is designed, and the Fourier series Vandermonde structure is used for the realization of the polynomial rooting technique. This mapping matrix can be computed prior to the DoA estimation, and it is only a function of the array configuration. Based on the mapping matrix, the 2-D angle search is transformed into two 1-D angle findings. Employing the features of the Fourier series, two root polynomials are designed for the estimation of the elevation and azimuth angles, spontaneously. The developed method avoids the 2-D spectral search, and angles are paired in automation. Both numerical simulation results, and results from experimental measured data (i.e., with mutual coupling effect incorporated), show the validity, potency, and potential practical application of the developed algorithm.

Performance evaluation of direction-finding techniques of an acoustic source with uniform linear array

PurposeThe purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).Design/methodology/approachThe relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.FindingsVariations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.Originality/valueDAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.

Fast Pseudospectrum Estimation for Automotive Massive MIMO Radar

Subspace methods, e.g., multiple signal classification algorithm (MUSIC), show great promise to high-resolution environment sensing in the 6G-enabled mobile Internet of Things (IoT), e.g., the emerging unmanned systems. Existing schemes, aiming to simplify the computational 1-D search of the MUSIC pseudospectrum, unfortunately have still an unaffordable complexity or the compromised accuracy, especially when the millimeter-wave massive multiple-input–multiple-output (MIMO) radar is considered. In this work, we address the fast and accurate estimation of the high-resolution pseudospectrum in massive MIMO radars. To enable real-time automotive sensing, we first formulate this computational procedure as one matrix product problem, which is then solved by leveraging randomized matrix sketching techniques. To be specific, we compute the large matrix product <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">approximately</i> by the product of two small matrices abstracted via random sampling. To minimize the approximation error, we further design another sampling, pruning, and recomputing (SaPRe) algorithm, which refines the approximated results and thus attains the exact pseudospectrum. Finally, the theoretical analysis and numerical simulations are provided to validate the proposed methods. Our fast approaches dramatically reduce the time complexity and simultaneously attain the accurate Direction-of-Arrival (DoA) estimation, which have the great potential to real time and high-resolution automotive sensing with massive MIMO radars.

An Improved co-Prime Parallel Array With Conjugate Augmentation for 2-D DOA Estimation

This paper proposes an improved co-prime parallel array (CPPA) configuration for two-dimensional direction of arrival (2-D DOA) estimation. Specifically, the proposed array consists of a CPPA and two extra sensors along the vertical axis. The array aperture is extended through two steps. Firstly, a virtual parallel symmetric co-prime array (ViP-SCPA), consisting of two symmetric co-prime arrays(SCPAs), is constructed by using the conjugate augmented processing(CAP). Secondly, a one-dimensional (1-D) virtual co-array of ViP-SCPA is derived by exploiting the difference and sum co-array of the CPPA. Based on the obtained 1-D virtual co-array of ViP-SCPA, the 2-D DOA estimation is cast as a 1-D DOA estimation problem, for which both subspace-based methods and sparse representation algorithms can be adopted. To evaluate the array apertures of the proposed array, analytical expressions are derived to quantify the achievable number of unique lags, and the maximum number of consecutive lags of the resultant 1-D virtual co-array of ViP-SCPA. Simulation results verify the effectiveness of the proposed configuration by using the classical multiple signal classification (MUSIC) and sparse representation(SR) algorithm respectively.

Near-Field Propagation Analysis for Vivaldi Antenna Design: Insight into the propagation process for optimizing the directivity, integrity of signal transmission, and efficiency

Refined optimization of complex curve–linear-shaped radiators, such as traveling-wave Vivaldi antennas, can be achieved by considering simulated near fields to interpret in detail the structural influences of a design. The relationships between the space and time distributions of electromagnetic (EM) energy clusters and the geometric features are revealed with appropriate use of impulse response analysis combined with the multiple signal classification (MUSIC) algorithm. This article reports a deeper approach when applied to the adjustment of the geometric features of a traveling-wave antenna based on an analysis of near-field propagation features.

Height Measurement with Meter Wave Polarimetric MIMO Radar: Signal Model and MUSIC-like Algorithm

Inview of the advantages of polarization diversity and waveform diversity, polarimetric multiple-input multiple-output (MIMO) radar, which combines the two advantages, is a subject worthy of in-depth study. Height measurement is one of the key problems to be solved for meter wave radar. Therefore, this paper focuses on height measurement with meter wave polarimetric MIMO radar. First, we derive the height measurement signal model for flat ground reflection combined with the two diversity techniques. Then, we properly classify the signal model and modify it to make it suitable for the classical super-resolution height measurement algorithms, including the generalized multiple signal classification(MUSIC) algorithm and steering vector synthesis (SVS) MUSIC algorithm. The split beam question in SVS-MUSIC is found and the resolution is provided. The proposed algorithms are also extended to long dipoles and large loop MIMO radar. Moreover, the computational complexity of the two algorithms and the derivation of Cramer Rao bound (CRB) for polarimetric MIMO radar are given. These methods do not require decoherence. The innovation of this paper is to provide a height measurement signal model for meter wave polarimetric MIMO radar along with its effective solution. The simulation results prove the accuracy of this approach.

Subspace based Multiple Constrained Minimum Variance (SMCMV) beamformers

In Magnetoencephalography (MEG) and Electroencephalography (EEG) two popular approaches are often used for spatial localization of focal task- or stimuli-related brain activations. One is Multiple Signal Classification (MUSIC) approach in the form of Recursively Applied and Projected (RAP) or Truncated RAP (TRAP) MUSIC algorithms. Another one is Multiple Constrained Minimum Variance (MCMV) beamformer method capable of dealing with multiple correlated activations. Considering simplicity, accuracy and computational efficiency both have their advantages and disadvantages. Using these two techniques as a starting point, three main developments were made in this study. First, we introduced novel Subspace based MCMV (or SMCMV) beamformers whose localizer functions combine MUSIC and MCMV localizers. Second, we showed analytically that SMCMV localizers in principle allow precise identification of n arbitrarily correlated sources irrespective to their strength in exactly n scans of the brain volume. Third, using extensive simulations and ANOVA statistical analyses we showed that on average SMCMV outperforms both the TRAP MUSIC and MCMV Multi-step Iterative Approach (MIA) procedure, currently the most accurate MCMV algorithm to our knowledge, with respect to localization accuracy and the number of successfully identified sources. Importantly, this was demonstrated for situations when the noise covariance could not be estimated precisely, signal to noise ratios were small, source correlations were significant and larger numbers of sources were involved. SMCMV advantage held for both MEG and EEG modalities. In addition we illustrated the SMCMV method by applying it to a real MEG Auditory Steady State Response (ASSR) experiment.

FDA-MIMO radar covariance matrix estimation via shrinkage processing

Frequency diverse array multiple-input multiple-output (FDA-MIMO) produces an angle-range-dependent and time-varying transmit beampattern due to the small frequency increment across its array elements, which provides potential applications in new radar techniques. Due to this particularity, multiple signal classification (MUSIC) algorithm can be used to simultaneously estimate the angle and range of the target through a two-dimensional search. The MUSIC need to estimate the covariance matrix. In this paper, we introduce a shrinkage processing for estimating large covariance matrices when the number of samples is substantially fewer than the number of variables which is more common in FDA-MIMO than phase array (PA). Firstly, we propose a receiving model of multi-channel mixing multi-matched filter to eliminate time-varying effect. Secondly, the taper processing, Toeplitz rectification processing and shrinkage-to-identity processing are analyzed. Then, we propose two algorithms, namely, shrinkage-to-taper and shrinkage-to-Toeplitz-rectification on the basis of these algorithms. The proposed processing method improves upon both shrinkage and Toeplitz rectification or tapering processing by shrinking the sample covariance matrix (SCM) to its Toeplitz rectification or tapered version. Thirdly, a closed form expression of shrinkage coefficient for the shrinkage-to-Toeplitz-rectification, and shrinkage-to-taper are used. Finally, simulations results for FDA-MIMO radar show that the proposed algorithm is superior to the sample covariance matrix, shrinkage LW algorithm proposed by Ledoit-Wolf and shrinkage RBLW algorithm which is improved shrinkage LW according to the Rao-Blackwell theorem, in the sense of minimum mean-squared error (MMSE).

Blind Super-Resolution Approach for Exploiting Illumination Variety in Optical-Lattice Illumination Microscopy

Optical-lattice illumination patterns help in pushing high spatial frequency components of the sample into the optical transfer function of a collection microscope. However, exploiting these high-frequency components require precise knowledge of illumination if reconstruction approaches similar to structured illumination microscopy are employed. Here, we present an alternate blind reconstruction approach that can provide super-resolution without the requirement of extra frames. For this, the property of exploiting temporal fluctuations in the sample emissions using “multiple signal classification algorithm” is extended aptly toward using spatial fluctuation of phase-modulated lattice illuminations for super-resolution. The super-resolution ability is shown for sinusoidal and multiperiodic lattice with approximately 3- and 6-fold resolution enhancements, respectively, over the diffraction limit.

Synthetic Aperture Radar Tomography in Urban Area Based on Compressive MUSIC

Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation for three-dimensional (3-D) imaging. Spectral estimation is the conventional TomoSAR imaging method, such as multiple signal classification (MUSIC). For the sparse elevation distribution, compressive sensing (CS) is a favorable technique for the elevation reconstruction. Compressive sensing multiple signal classification algorithm (CS-MUSIC) is a combination of CS and MUSIC algorithm. It takes the advantage of both conventional spectral estimation and CS technology, and hence overcomes the drawbacks of existing methods and obtains the super-resolution ability. In this paper, the effectiveness of CS-MUSIC in TomoSAR imaging of urban area has been validated by simulated and real TerraSAR-X data. CS-MUSIC algorithm can effectively solve the problem of acquiring high-resolution TomoSAR imaging with small amount of data.

Polarization quaternion DOA estimation based on vector MISC array

This paper examines the direction of arrival (DOA) estimation for polarized signals impinging on a sparse vector sensor array which is based on the maximum interelement spacing constraint (MISC). The vector array effectively utilizes the polarization domain information of incident signals, and the quaternion model is adopted for signals polarization characteristic maintenance and computational burden reduction. The features of MISC arrays are crucial to the mutual coupling effects reduction and higher degrees of freedom (DOFs). The quaternion data model based on vector MISC arrays is established, which extends the scalar MISC array into the vector MISC array. Based on the model, a quaternion multiple signal classification (MUSIC) algorithm based on vector MISC arrays is proposed for DOA estimation. The algorithm combines the advantages of the quaternion model and the vector MISC array to enhance the DOA estimation performance. Analytical simulations are performed to certify the capability of the algorithm.

Towards Low Latency and Resource-Efficient FPGA Implementations of the MUSIC Algorithm for Direction of Arrival Estimation

The estimation of the Direction of Arrival (DoA) is one of the most critical parameters for target recognition, identification and classification. MUltiple SIgnal Classification (MUSIC) is a powerful technique for DoA estimation. The algorithm requires complex mathematical operations like the computation of the covariance matrix for the input signals, eigenvalue decomposition and signal peak search. All these signal processing operations make real-time and resource-efficient implementation of the MUSIC algorithm on Field Programmable Gate Arrays (FPGAs) a challenge. In this paper, a novel design approach is proposed for the FPGA-implementation of the MUSIC algorithm. This approach enables a significant reduction in both FPGA resources and latency. In more detail, the proposed design enables the estimation of DoA in real-time scenarios in 2μsec with 30% to 50% fewer resources as compared to existing techniques.

The improved fault location method for flexible direct current grid based on clustering and iterating algorithm

The fault location method based on the natural frequency of the fault travelling wave has outstanding superiority in high-voltage dc system, because of high operation speed, acceptable sampling rate, and so on. However, in flexible dc grid, the natural-frequency characteristic is quite different from that in conventional high-voltage dc system, meaning the existing fault location method cannot be directly used. This paper analyses the distance-frequency characteristics in different stages of fault period (from fault occurrence to fault isolation) in flexible dc grid, to find a suitable stage for fault location. On the basis of this, the improved fault location method based on clustering and iterating algorithms is proposed. Compared with the traditional method which directly uses the multiple signal classification algorithm to estimate the natural frequency for fault location, the proposed method can effectively eliminate the large error caused by signal dimension fluctuation and false spectrum peak of multiple signal classification algorithm. Finally, simulation cases are carried out to prove the feasibility and superiority of the proposed method.

A focusing MUSIC algorithm for baseline-free Lamb wave damage localization

The dispersion effect of Lamb waves can cause different degree of phase distortion of array signals, which will make the classical multiple signal classification (MUSIC) algorithm no longer valid in Lamb wave based damage localization. At the same time, baseline subtraction and estimation of the number of scattering sources are required in the classical MUSIC algorithm, which limits its application. A focusing MUSIC algorithm is proposed for baseline-free Lamb wave damage localization on isotropic materials. Based on the virtual time reversal technique, the dispersive wave packets adaptively back propagate according to their respective propagation distances so that the dispersion can be completely compensated and focusing signals are obtained. Wave packet truncation is implemented on the focusing signals and then let the truncated signals forward propagate with the group velocity at center frequency. The two operations, back propagation and forward propagation, can compensate the dispersion effect and avoid estimating of the number of scattering sources as well as baseline subtraction. Experiments are conducted on an aluminum plate to investigate the performance of the proposed algorithm. A uniform linear PZT array is glued on the aluminum plate to transmit and receive Lamb waves. Experimental results of the proposed algorithm as well as the classical MUSIC algorithm are given and compared. The comparison verifies the effectiveness of the proposed focusing MUSIC algorithm.

3D Imaging Algorithm of Directional Borehole Radar Based on Root-MUSIC

A directional borehole radar consists of one transmitting antenna in the borehole and four receiving antennas distributed at equal angles in a ring. The receiving antennas can determine the depth and orientation of targets beside the borehole. However, the problem of target orientation determination and 3D imaging algorithms remains a technological challenge. The MUSIC (multiple signal classification) algorithm requires a peak search, so the accuracy of the operation is limited by the angle interval. Based on the MUSIC algorithm, the Root-MUSIC algorithm is proposed and implemented. By replacing the spectral peak search with calculating the roots of the polynomials greatly improves the orientation recognition accuracy. Finally, the results obtained using the above algorithm are verified with synthetic data and compared with the results of the MUSIC algorithm. The results show that both the MUSIC algorithm and the Root-MUSIC algorithm can achieve very good orientation determination and 3D imaging results. In terms of accuracy, the Root-MUSIC algorithm has an obvious improvement compared with the MUSIC algorithm.

Fast DOA Estimation Based on the Transform Domain Weighted Noise Subspace Fitting Algorithm for Generalized Sparse Array

Compared with uniform arrays, a generalized sparse array (GSA) can obtain larger array aperture because of its larger element spacing, which improves the accuracy of DOA estimation. At present, most DOA estimation algorithms are only suitable for the uniform arrays, while a few DOA estimate algorithms that can be applied to the GSA are unsatisfactory in terms of computational speed and accuracy. To compensate this deficiency, an improved DOA estimation algorithm which can be applied to the GSA is proposed in this paper. First, the received signal model of the GSA is established. Then, a fast DOA estimation method is derived by combining the weighted noise subspace algorithm (WNSF) with the concept of “transform domain” (TD). Theoretical analysis and simulation results show that compared with the traditional multiple signal classification (MUSIC) algorithm and the traditional WNSF algorithm, the proposed algorithm has higher accuracy and lower computational complexity.

Ameliorated-multiple signal classification (Am-MUSIC) for damage imaging using a sparse sensor network

Multiple Signal Classification (MUSIC) – a directional scanning and searching algorithm, has gained its prominence in phased array-facilitated nondestructive evaluation. Nevertheless, prevailing MUSIC algorithms are largely bound up with the use of a dense linear array, which fail to access the full planar area of an inspected sample, leaving blind zones to which an array fails to scan, along with the incapability of differentiating multiple damage sites that are close one from another. To break above limitations, conventional MUSIC algorithm is ameliorated in this study, by manipulating the signal representation matrix at each pixel using the excitation signal series, instead of the scattered signal series, which enables the use of a sparse sensor network with arbitrarily positioned transducers. In the ameliorated MUSIC (Am-MUSIC), the orthogonal attributes between the signal subspace and noise subspace inherent in the signal representation matrix is quantified, in terms of which the Am-MUSIC yields a full spatial spectrum of the inspected sample, and damage, if any, can be visualized in the spectrum. Am-MUSIC is validated, in both simulation and experiment, by evaluating single and multiple sites of damage in plate-like waveguides with a sparse sensor network. Results verify that i) detectability of Am-MUSIC-driven damage imaging is not limited by damage quantity; ii) Am-MUSIC has full access to a sample, eliminating blind zones; and iii) the amelioration expands conventional MUSIC from phased array-facilitated nondestructive evaluation to health monitoring using built-in sparse sensor networks.