Multiple Signal Classification Research Articles

A high precision vital signs detection method based on millimeter wave radar

Millimeter wave (mmWave) radar technology has potential applications in vital signs detection and medicine. In order to minimize the influence of human micro-movements and respiratory harmonics on heart rate estimation, the vital signs detection method based on mmWave radar is studied in this paper. First, we use median filtering to eliminate baseline drift caused by human micromotion. Next, a differential recursive least squares multiple classification (DR-MUSIC) algorithm is proposed based on the combination of recursive least squares-based adaptive filter (RLS) and multiple signal classification (MUSIC) algorithm. This algorithm effectively suppresses respiratory harmonics and separates respiratory and heartbeat signals. Finally, heart rate value can be precisely estimated using spectral peak search. We invite a number of people to participate in the experiment, which demonstrate that the method successfully suppresse the impact of respiratory harmonics at low SNR. The error rate between the estimated heart rate and the reference heart rate is only 1.69% to 2.61%, which is significantly better than the existing algorithms.

Landmine Detection Using Electromagnetic Time Reversal‐Based Methods: 1. Classical TR, Iterative TR, DORT and TR‐MUSIC

AbstractIn this paper, we present a review and classification of the published works on the use of Electromagnetic Time Reversal (EMTR)‐based methods to locate landmines. Different approaches for landmine localization using EMTR are investigated. Specifically, the classical time‐domain EMTR, iterative EMTR, EMTR‐DORT (Décomposition de l’Opérateur du Retournement Temporel), and EMTR‐MUSIC (MUltiple SIgnal Classification) are implemented using different numerical techniques. The main properties of the mentioned EMTR‐based approaches are reviewed and the TR‐MUSIC method is selected as the most promising approach for the problem of interest, among all the reviewed methods. In the TR‐MUSIC method, the transfer matrix is calculated in the first step. Then, the singular value decomposition of the transfer matrix is performed. In the last step, the location of the landmines is obtained through the evaluation of the pseudospectrum. As opposed to other EMTR‐based techniques, TR‐MUSIC features very high resolution (in the order of /10 or higher) with a reasonable number of sensors, allowing the detection of multiple closely spaced targets.

Exploiting High-Precision AoA Estimation Method using CSI from a Single WiFi Station

The accuracy of estimating the angle of arrival (AoA) using wireless fidelity (WiFi) channel state information (CSI) has been a topic of intense interest in the fields of the Internet of Things, location-based services, etc. We propose a high-precision method of AoA estimation of the direct path (DP) using WiFi CSI from a single station. It contains three stages: data preprocessing, AoA-time of flight (ToF) joint estimation for all paths, and the DP's AoA estimation. Firstly, phase calibration, linear transform, and multiple-layer filtering are accordingly conducted after CSI collection in the preprocessing stage to output the denoised CSI. Then, the AoA and ToF values for all paths are simultaneously obtained utilizing a spatial smoothing multiple signal classification (MUSIC) algorithm. Finally, the density-based spatial clustering for noise applications (DBSCAN) algorithm divides all the AoA and ToF values into several clusters. The target cluster that meets the requirements of maximum counts and minimum mean ToF is subsequently selected. The weighted centroid AoA value of the target cluster is regarded as the AoA of the DP. AoA estimation experiments using different sampling packets are conducted in a small conference room with an Intel 5300 network interface card along a straight line. The proposed method could recognize the DP with a rate of 100 percent and estimate the AoA of the DP with a mean absolute error of 2° and root mean square error of 2.82°. Compared with SpotFi and hierarchical clustering–logistic regression systems, the proposed method improves AoA estimation accuracy by at least 75%. Therefore, the proposed method could achieve a high-precision estimation of the AoA of the DP in the case 26 of different short distances.

Landmine Detection Using Electromagnetic Time Reversal‐Based Methods: 2. Performance Analysis of TR‐MUSIC

AbstractIn this paper, a series of numerical simulations are conducted for various 2D and 3D configurations to demonstrate the performance of the Time Reversal MUltiple SIgnal Classification (TR‐MUSIC) method. The results reveal the excellent performance of TR‐MUSIC, taking into account the effects of noise, soil types (both homogeneous and layered), and their electrical parameters, as well as different types of targets (varying in number, size, shape, and location). Additionally, unlike other electromagnetic TR‐based techniques, TR‐MUSIC offers very high resolution (on the order of /10 or higher) with a reasonable number of sensors, enabling the detection of multiple closely spaced targets. In TR‐based methods, reflections from the object(s) or landmine(s) are crucial and are determined by the difference between the constitutive parameters (e.g., permittivity, permeability, and conductivity) of the landmine(s) and their surrounding medium. Therefore, TR‐based approaches, similar to conventional GPR‐based approaches, are suitable for detecting objects or landmines with significant differences in constitutive parameters compared to their immersion medium. This research primarily focuses on metallic objects or landmines.

Sound Source Identification of Vehicle Noise Based on a Microphone Array With a Modified Multiple Signal Classification Algorithm

In this paper, a modified multiple signal classification (MUSIC) algorithm tailored for sound source identification (SSI) of vehicle noise is introduced and experimentally validated. A uniform planar microphone array (UPMA) is formulated for mathematical modeling, with its SSI-oriented parameters selected based on the primary frequency spectrum of vehicle noise. Simulations are conducted to compare the SSI accuracy of two conventional spatial spectra estimation (SSE) algorithms: the Capon algorithm and the MUSIC algorithm. The results demonstrate that the MUSIC algorithm, which relies on the eigenvalues of a covariance matrix to estimate signal direction, exhibits superior SSI resolution under low signal-to-noise ratio (SNR) conditions. However, it faces challenges in distinguishing between coherent or closely spaced signals. To address this, a modified MUSIC algorithm is proposed by reconstructing the covariance matrix of received signals and the SSE function. Simulation outcomes indicate that the modified MUSIC significantly outperforms the conventional version, owing to its enhanced SSI resolution. The accuracy of the SSI system, incorporating the UPMA and the modified MUSIC, is verified using a low-frequency volume source. Ultimately, the devised SSI system is successfully deployed to identify noise sources in a vehicle at different operational conditions, further validating the efficacy of the modified MUSIC. The UPMA and the modified MUSIC presented in this study have direct applicability in vehicle noise source identification and may be extended to other sound-related engineering fields for SSI purposes.

Arrayed ultrasonic wind speed and direction measurement based on the BNF-FLOC-MUSIC algorithm

An arrayed ultrasonic wind measurement method based on the BNF-FLOC-MUSIC algorithm is proposed to address the issue of low measurement accuracy and poor noise suppression capabilities of current array wind measurement methods in impulse noise backgrounds. The proposed method utilizes an array structure consisting of one transmitting ultrasonic sensor and five receiving sensors. Continuous sampling is performed leveraging this structure, and the received array signals are processed using a bounded nonlinear function (BNF). Subsequently, the fractional lower-order covariance (FLOC) operations are applied to suppress impulse noise’s influence further. Finally, combining these steps with the Multiple Signal Classification (MUSIC) algorithm enables high-precision wind speed and direction measurement. The effectiveness and superiority of the method are examined through simulation experiments and actual measurement systems, and the errors of wind speed and wind direction angle in actual measurement are 1.2% and 2°, respectively, which satisfy the design requirements of the ultrasonic anemometer.

Sound source localization based on microphone array mounted on unmanned aerial vehicles

Abstract Microphone array installed on unmanned aerial vehicles (UAV) can be used for acoustic detection in scenes, such as detecting noise sources in factories or monitoring corona in power grids. However, using acoustic sensors on UAV has great challenges. The motor rotation of UAV and the non-stationary noise generated by propeller make it difficult for microphone array to obtain useful information for sound source localization. In order to reduce self-noise, an improved least mean square (LMS) adaptive filtering algorithm is proposed. Combining filtering algorithm and multiple signal classification (MUSIC) algorithm to locate the sound source, the noise spectrum characteristics of UAV in outdoor free flight mode are analyzed. The microphone array is used to collect data and realize sound source localization in low Signal-to-Noise Ratio (SNR) environment. The simulation and experimental results show that the algorithm improves the robustness of the sound source localization algorithm in low SNR environment, and realizes the real-time localization of the target sound source during the flight of UAV with microphone array.

High-resolution ultrasound imaging based on spatio-temporal MUSIC

Abstract Time-reversal MUltiple SIgnal Classification (TR-MUSIC) is known for its super-resolution capability in locating point scatterers. However, a prerequisite for the application of the TR-MUSIC method is a thorough understanding of the properties of the medium and its background Green’s function. In general, it is difficult to obtain an accurate model of the Green’s function in advance for non-uniform or complex media, necessitating the use of approximate Green’s functions. When TR-MUSIC relies on these approximations, the impact on lateral resolution may be minimal, but the axial resolution is significantly reduced. In addition, we have proposed the Range-MUSIC (R-MUSIC) method, which exploits the linear relationship between the rotation of the echo phases and the positions of the scatterers. This approach uses subbands of different frequencies, differentiating multiple point scatterers by the speed of phase rotation associated with each frequency. By analyzing the profile in the time domain of echoes within the noise subspace, R-MUSIC achieves super-resolution in the axial direction without improving lateral resolution. Therefore, our study combines R-MUSIC with TR-MUSIC to improve axial resolution while maintaining lateral resolution. Through simulation and experimental evidence, we have shown that this combined approach yields better imaging performance than either method used independently.

Application of MUSIC-type imaging for anomaly detection without background information

It has been demonstrated that the MUltiple SIgnal Classification (MUSIC) algorithm is fast, stable, and effective for localizing small anomalies in microwave imaging. For the successful application of MUSIC, exact values of permittivity, conductivity, and permeability of the background must be known. If one of these values is unknown, it will fail to identify the location of an anomaly. However, to the best of our knowledge, no explanation of this failure has been provided yet. In this paper, we consider the application of MUSIC to the localization of a small anomaly from scattering parameter data when complete information of the background is not available. Thanks to the framework of the integral equation formulation for the scattering parameter data, an analytical expression of the MUSIC-type imaging function in terms of the infinite series of Bessel functions of integer order is derived. Based on the theoretical result, we confirm that the identification of a small anomaly is significantly affected by the applied values of permittivity and conductivity. However, fortunately, it is possible to recognize the anomaly if the applied value of conductivity is small. Simulation results with synthetic data are reported to demonstrate the theoretical result.

Non-Intrusive System for Honeybee Recognition Based on Audio Signals and Maximum Likelihood Classification by Autoencoder.

Artificial intelligence and Internet of Things are playing an increasingly important role in monitoring beehives. In this paper, we propose a method for automatic recognition of honeybee type by analyzing the sound generated by worker bees and drone bees during their flight close to an entrance to a beehive. We conducted a wide comparative study to determine the most effective preprocessing of audio signals for the detection problem. We compared the results for several different methods for signal representation in the frequency domain, including mel-frequency cepstral coefficients (MFCCs), gammatone cepstral coefficients (GTCCs), the multiple signal classification method (MUSIC) and parametric estimation of power spectral density (PSD) by the Burg algorithm. The coefficients serve as inputs for an autoencoder neural network to discriminate drone bees from worker bees. The classification is based on the reconstruction error of the signal representations produced by the autoencoder. We propose a novel approach to class separation by the autoencoder neural network with various thresholds between decision areas, including the maximum likelihood threshold for the reconstruction error. By classifying real-life signals, we demonstrated that it is possible to differentiate drone bees and worker bees based solely on audio signals. The attained level of detection accuracy enables the creation of an efficient automatic system for beekeepers.

Integrated MUSIC array for high-precision damage diagnosis in complex composite structures

Guided Wave (GW)-based Multiple Signal Classification (MUSIC) damage imaging presents several advantages, such as high resolution, which makes it a promising technique for localizing damage in composite structures. However, the application of this technology in aircraft is confronted with various challenges. The variability in performance of MUSIC array sensors is attributed to material and manufacturing process dispersion. Additionally, the conventional wiring of MUSIC array sensors adds considerable weight and is not compatible with complex structural configurations. Furthermore, within intricate configurations, the attenuation of scattering signals induced by structural damage impacts the accuracy of imaging. Moreover, the manual and individual placement of sensors on structures, along with structural anisotropy, may introduce phase errors in the signals detected by MUSIC array sensors. This can lead to a reduction in the accuracy of MUSIC imaging and result in compromised long-term sensor reliability. This paper proposes a high-precision integrated MUSIC array for the diagnosis of complex composite damage. This approach aims to address the challenges related to damage imaging in materials with complex structures. Impedance curve screening and surface-mount co-curing technology are utilized to manage the performance variation of MUSIC array sensors, enhance layout uniformity, and improve long-term stability. Subsequently, a focus compensation algorithm is proposed within the integrated MUSIC design to enhance precision, reduce weight, and adapt to complex structures. The effectiveness of the proposed method is confirmed through experimental validation on an actual complex composite wing box segment, demonstrating a maximum error of 2 cm in locating impact damage.

Sparse reconstruction methods for wideband source localization in spherical harmonics domain

Wideband acoustic source localization is a challenging task especially in the presence of noise. Generally wideband source localization is done by averaging the Direction of Arrival (DOA) estimates obtained over multiple frequencies or narrow subbands by the method of frequency smoothing. Localization of multiple wideband sources which are correlated is even more challenging. In this work acoustic source localization under these challenging conditions is addressed. A sparse reconstruction framework is developed for wideband source localization in the Spherical Harmonics (SH) domain. The proposed framework jointly computes both the azimuth and elevation. In contrast to earlier methods of source localization in the SH domain, this work utilizes the expression for the SH coefficients of the amplitude density of the plane wave, to develop the sparse reconstruction framework. An expression for Cramér Rao Lower Bound (CRLB) on the DOA using this framework is also derived for the wideband sources. This CRLB is shown to easily generalize for narrowband sources as well. The performance of the proposed method is compared with MUltiple Signal Classification in SH domain (MUSIC-SH), Steered Response Power in SH domain (SRP-SH), MUSIC with Direct Path Dominance test in SH domain (MUSIC-DPD-SH) and Pressure based Compressive Sensing in SH domain (PCS-SH). The performance is evaluated for correlated narrowband and wideband sources through simulations, conducting experiments in an anechoic chamber and under reverberant conditions. Using the proposed framework, it is shown that correlated narrowband and wideband sources can be resolved reasonably well when compared to conventional methods of acoustic source localization in SH domain.

DOA Estimation on One-Bit Quantization Observations through Noise-Boosted Multiple Signal Classification.

Due to the low-complexity implementation, direction-of-arrival (DOA) estimation-based one-bit quantized data are of interest, but also, signal processing struggles to obtain the demanded estimation accuracy. In this study, we injected a number of noise components into the receiving data before the uniform linear array (ULA) composed of one-bit quantizers. Then, based on this designed noise-boosted quantizer unit (NBQU), we propose an efficient one-bit multiple signal classification (MUSIC) method for estimating the DOA. Benefiting from the injected noise, the numerical results show that the proposed NBQU-based MUSIC method outperforms existing one-bit MUSIC methods in terms of estimation accuracy and resolution. Furthermore, with the optimal root mean square (RMS) of the injected noise, the estimation accuracy of the proposed method for estimating DOA can approach that of the MUSIC method based on the complete analog data.

Adapting UWB AoA estimation towards unseen environments using transfer learning and data augmentation

Ultra-wideband technology has become increasingly prevalent in localization systems, particularly with the emergence of multi-antenna systems capable of estimating both distance and angle of arrival (AoA) for incoming signals. However, most scientific research analyzes the accuracy of AoA in one specific environment for which the estimator is trained. In this paper, we analyze the performance of various AoA estimation algorithms, such as deep convolutional neural networks (DCNN), multiple signal classification (MUSIC), and phase difference of arrival (PDoA), in unseen environments. Prior work already demonstrated the superior performance of ML for AoA estimation compared to PDoA or MUSIC. We show that MUSIC, PDoA and ML solutions suffer from degradation in unseen environments at the 90th percentile of error, with ML-based AoA estimation degrading by about 14 degrees in unseen environments compared to 4 degrees for PDoA. We demonstrate that while PDoA more effectively corrects AoA at the median level in unseen environments, ML-based methods excel at correcting higher-percentile AoA errors, including outliers. Finally, we propose a novel framework to further improve the correction of AoA outliers for DCNN-based AoA estimators using data augmentation and transfer learning, resulting in a median angular error of only 5 degrees in unseen environments, even considering a field of view up to 90 degrees.

A tailor-made cylindrical concrete implantable module for damage imaging in plate-like concrete structures

In this research, a tailor-made cylindrical concrete implantable module (CCIM) was proposed for damage imaging in concrete structures. The CCIM can be directly implanted into inspected structure to transmit and receive probing stress waves, which allows conveniently removing and updating CCIM when devices failed to work. Moreover, a CCIM-based multiple signal classification (MUSIC) algorithm is proposed to realize two-dimensional damage visualization. Based on meso-level finite element simulation, considering concrete as a three-phase composite material, the stress wave propagation characteristics can be revealed and the effectiveness of CCIM-based MUSIC imaging method has been investigated. To verify the proposed method experimentally, a pair of CCIMs were fabricated and implanted into two concrete specimens with different configurations. The results could effectively pinpoint damage location within inspected area. The proposed hardware and algorithm has great potential to overcome the disadvantage of short service period and could be applied for damaging imaging in concrete structures.

Electromagnetic informed data model considerations for near-field DOA and range estimates

Localizing sources in the near-field is one of the emerging challenges for array signal processing, which has received a great deal of attention in recent years. The development of accurate localization algorithms requires the definition of a reliable model of the received signal that takes into account all wavefront characteristics, such as angle, range, and polarization, as well as electromagnetic effects, such as mutual coupling between antennas and the amplitude and phase behaviour of electromagnetic wavefronts. A system model that considers the electromagnetic-informed wave behaviour effects, independent of the type of receiver antennas, array structure, degree of correlation of sources signals and other electromagnetic effects, is considered an “ exact model ” in the literature. However, due to the mathematical complexity of this modeling approach, simplifications using several approximations are conventionally used. For instance, the phase of the exact model is approximated using the Fresnel approximation, while the magnitude of the exact model is simplified by assuming equal distances between the source and all elements in the array. In this work, we evaluate the accuracy of a localization algorithm, the multiple signal classification (MUSIC), using the exact and approximated models in the near-field region. Through a series of simulations, we demonstrate that the localization algorithm designed based on the electromagnetic-informed exact model outperforms the one designed using the approximated model. We also show that considering electromagnetic factors in the system model through the exact model results in a 13% improvement in the direction of arrival (DOA) root mean square error (RMSE) and a 57.7% improvement in range RMSE at signal-to-noise ratio (SNR) of 15 dB.

GW-MUSIC focusing algorithm with high accuracy for composite damage diagnosis

The Guided Wave (GW)-based Multiple Signal Classification (MUSIC) method for damage imaging in composite structures offers high-resolution and flexible sensor array placement, demonstrating considerable potential for precise damage localization. However, the application to real aircraft composite structures, characterized by complex structural features, presents substantial challenges. These include complex boundary reflections, pronounced anisotropy, and diminished scattered signals from damage sites. This paper presents a novel single-peak anisotropy-compensated MUSIC method for damage imaging of complex composites. This method, designed for improved localization accuracy, combines a self-excited calibration strategy to mitigate anisotropy with single wave peak focusing to amplify signals and reduce boundary reflections. Experimental validation on a complex composite wing structure demonstrated significant enhancements in localization precision, affirming the method's efficacy.

Robust DOA estimation for two‐level nested arrays in unknown mutual coupling

AbstractAiming at solving the problem that the capability of direction of arrival method for estimation of nested arrays decreases sharply under unknown mutual coupling, an iterative weighted low‐rank matrix reconstruction algorithm is proposed here. First, the received data covariance matrix is extended, and a weighted low‐rank matrix recovery problem is formulated for joint estimations of the Toeplitz matrix and mutual coupling coefficients. Next, the direction of arrival from recovered Toeplitz matrix is retrieved by using the root multiple signal classification algorithm. Simulation results show that iterative weighted low‐rank matrix reconstruction algorithm can effectively avoid the influence of mutual coupling while solving the grid‐mismatch problem so that the accuracy and the resolution of estimation are improved.

Characterizing the consistency of motion of spermatozoa through nanoscale motion tracing

To demonstrate nanoscale motion tracing of spermatozoa and present analysis of the motion traces to characterize the consistency of motion of spermatozoa as a complement to progressive motility analysis. Anonymized sperm samples were videographed under a quantitative phase microscope, followed by generating and analyzing superresolution motion traces of individual spermatozoa. Not applicable. Centrifuged human sperm samples. Not applicable. Precision of motion trace of individual sperms, presence of a helical pattern in the motion trace, mean and standard deviations of helical periods and radii of sperm motion traces, speed of progression. Spatially sensitive quantitative phase imaging with a superresolution computational technique MUltiple SIgnal Classification ALgorithm allowed achieving motion precision of 340 nm using ×10, 0.25 numerical aperture lens whereas the diffraction-limited resolution at this setting was 1,320 nm. The motion traces thus derived facilitated new kinematic features of sperm, namely the statistics of helix period and radii per sperm. Through the analysis, 47 sperms with a speed >25 μm/s were randomly selected from the same healthy donor semen sample, it is seen that the kinematic features did not correlate with the speed of the sperms. In addition, it is noted that spermatozoa may experience changes in the periodicity and radius of the helical path over time. Further, some very fast sperms (e.g., >70 μm/s) may demonstrate irregular motion and need further investigation. Presented computational analysis can be used directly for sperm samples from both fertility patients with normal and abnormal sperm cell conditions. We note that MUltiple SIgnal Classification ALgorithm is an image analysis technique that may vaguely fall under the machine learning category, but the conventional metrics for reporting found in Enhancing the QUAlity and Transparency Of health Research network do not apply. Alternative suitable metrics are reported, and bias is avoided through random selection of regions for analysis. Detailed methods are included for reproducibility. Kinematic features derived from nanoscale motion traces of spermatozoa contain information complementary to the speed of the sperms, allowing further distinction among the progressively motile sperms. Some highly progressive spermatozoa may have irregular motion patterns, and whether irregularity of motion indicates poor quality regarding artificial insemination needs further investigation. The presented technique can be generalized for sperm analysis for a variety of fertility conditions.

DIRECTION OF ARRIVAL ESTIMATION BASED ON SMOOTH MUSIC ALGORITHM COMBINED SPECTRUM SELECTION TECHNIQUES OF NOISE CHARACTERISTICS OF MARINE TARGETS EQUIPPED PROPELLER

This article demonstrates a proposal for a direction of arrival (DOA) estimation algorithm for passive sonar systems based on the combination of the smooth multiple signal classification (Smooth MUSIC) algorithm and frequency bin selection (BS) of the sound of marine propeller-equipped targets, so-called BS-SMUSIC. The harmonics of propeller shaft and blade frequencies are considered useful information for detecting targets and estimating their parameters, including DOA. Low frequency analysis recording (LOFAR) is utilized to implement BS. By analyzing MUSIC-based algorithm together BS, passive DOA estimation algorithms have been significantly improved. Simulation results have been given in both cases: uncorrelated and correlated sources. Outcomes demonstrate that the BS-SMUSIC algorithm has the best performance in comparison to three MUSIC-based algorithms (MUSSIC, Modified MUSIC and Root MUSIC) in all investigation cases: narrow band signals at 5 dB of signal to noise (SNR); wideband signals at -5 dB of SNR. Besides, BS-SMUSIC algorithm also archives the lowest root mean square error (RMSE) in SNR range -5 dB to 20 dB. In the SNR range, the RMSE of BS-SMUSIC algorithm remains stable and is about 0.3 ◦ at -5 dB of SNR. This performance reflects its ability to ensure stable operation, super resolution and high accuracy of BS-SMUSIC algorithm for the passive sonar systems.