Multiple Signal Classification Research Articles

Support Vector Regression for Bluetooth Ranging in Multipath Environments

Ranging solutions for Internet of Things (IoT) localization applications seek to provide high accuracy with low cost of implementation. Among candidate IoT technologies that may fit this criterion, Bluetooth is a desirable choice as Bluetooth Low Energy (BLE) support is ubiquitous in modern smartphones, providing low implementation costs and low power consumption. Recent advancements in BLE ranging technology employ the Multi-Carrier Phase Difference technique which takes two-way channel frequency response (CFR) measurements. However, accurate ranging with these measurements is challenging due to many closely spaced multipath components from squaring the one-way CFR, a single or low number of snapshots, and model imperfections that arise in practical scenarios. To overcome these challenges, we propose a data-driven support vector regression (SVR) approach. Using real-world BLE measurements, our proposed SVR method demonstrates decimeter-level accuracy with single antenna devices, whereas Multiple Signal Classification (MUSIC), a popular model-based method, requires multiple antennas to obtain comparable performance. Moreover, we show robustness in different multipath environments including indoor, outdoor, and non-line-of-sight conditions, we determine generalization capabilities with training size, and we analytically establish the reduction in computational complexity compared to MUSIC.

Drone Detection and Classification Using Music (Multiple Signal Classification) Algorithm

Abstract: The rapid augmentation of drones in different sectors has raised concerns regarding privacy, security, and safety. This project proposes a novel drone detection and classification approach by integrating the YOLOv5 framework with a MUSICbased algorithm to address the above issues. The aim is to postulate an efficient and accurate model capable of identifying and classifying different types of drones in real time. The project leverages the YOLOv5 architecture incorporated with the MUSIC algorithm for superior speed and accuracy. By training YOLOv5 on a large dataset of drone images, the model can learn to identify visual features and specific characteristics unique to drones, permitting effective detection and classification

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

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

Beamforming-based algorithms for recovering information from fetal electrocardiographic sensors

We deal with the extraction of the fetal electrocardiography (ECG) signal from the raw ECG signals of the mother by the beamforming- based algorithms. The foetal ECG sensors bring out signals containing information from the pregnant mother and the infant. Detailed and separate signals are already provided by the foetal ECG instruments; but for some specific studies related to the infant conditions, it is necessary to improve the quality of the signal with a dedicated processing. In this paper, four techniques, with some enhancements, are proposed to perform the processing; we have applied the following techniques: Least Mean Square (LMS) with adaptive noise cancellation technique, Discrete Wavelet Transform (DWT)-based technique, Empirical Wavelet Transform (EWT) technique, and Multiple Signal Classification (MUSIC). The LMS and the MUSIC pertain to beamforming approach. The techniques were used to decompose and identify the different elements constituting the source signal (mother's signal) and noise cancellation by Multivariate Empirical Mode Decomposition (MEMD) technique. The signal was adaptively decomposed by LMS, DWT and MUSIC according to optimised parameters to extract some hidden components of the source signal, such as the foetal features, QRS, heartbeat etc. The results have showed that LMS, with enhancements, is more effective in identifying and removing useless noise. The techniques were applied to the ECG signal of a 30-year-old healthy pregnant woman, which allowed to verify their applicability. The present research leads to the below main contributions among others: separation of the ECG signal of the foetus from the mother, highlighting the functional state of the foetal heart rhythm (heart rate and heartbeat,) and this can show us if the foetal ECG has malfunctions.

Robust DOA Estimation Using VSS-LMS With Low-Rank Matrix Approximation

Direction-of-arrival (DOA) estimation performance of adaptive filtering-based methods, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , least mean square (LMS), degrades significantly in adverse conditions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> at low signal-to-noise ratio (SNR), small number of antenna array elements and snapshots, and in presence of two closely spaced signal sources). Moreover, these estimation methods require many regulating parameters to efficiently update the step size, which are very difficult to tune manually in practical scenarios. Although the subspace decomposition-based methods, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., multiple signal classification (MUSIC), provide somewhat better performance in adverse conditions, they are based on the statistical properties of the signal such as spatial covariance matrix (SCM) and its eigenvalue decomposition (EVD), resulting in high computational complexity. In this paper, a robust DOA estimation method is proposed based on a variable step size LMS algorithm with low rank matrix approximation (LRMA), where the received signal de-noising problem is formulated first as a LRMA problem directly using the received signal observations instead of the SCM, and then the variable step size is calculated from the predicted instantaneous error and the estimated powers of reference and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de-noised</i> signals. The output spatial spectrum is determined by the reciprocal of the antenna array pattern, and the high peaks in the output spatial spectrum give the received signals estimated directions. The proposed method updates the step size efficiently without choosing any regulating parameter, and achieves improved performance even in adverse conditions due to the de-noising feature of LRMA with low computational complexity. Further, we also conduct the convergence and stability analyses of the proposed iterative estimation method with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de-noised</i> signal. Numerical results demonstrate that the proposed method outperforms state-of-the-art methods especially those, which yielded based on adaptive filtering.

A Convolutional Neural Network for Parameter Estimation of the Bi-GTD Model

In this paper, a novel parameter estimation method based on a convolutional neural network (CNN) is proposed to extract geometrical features of radar objects. The CNN’s design is inspired by the inversion process of a physically relevant model, called the geometrical theory of diffraction (GTD) model, whose bistatic form can be used to describe the bistatic scattering response from the target in the netted radar system. This model-inspired inversion method can automatically compensate for phase errors between multiple signal channels and obtain better parameter estimation performance than traditional methods, such as the orthogonal matching pursuit (OMP), the estimation of signal parameters via rotational invariance techniques (ESPRIT) and the multiple signal classification (MUSIC). The experimental results not only verify the validity of the proposed intelligent inversion method but also demonstrate the interpretability and generalization ability of the CNN, whose architecture is designed based on mathematical derivation.

A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave Inspection

Traditional Lamb wave inspection and imaging methods heavily rely on prior knowledge of dispersion curves and baseline recordings, which may not be feasible in the majority of real cases due to production uncertainties and environmental variations. In order to solve this problem, a two-step Lamb wave strategy utilizing adaptive multiple signal classification (MUSIC) and sparse reconstruction of dispersion reconstruction is proposed. The multimodal Lamb waves are initially reconstructed in the f-k domain using random measurements, allowing for the identification and characterization of multimodal Lamb waves. Then, using local polynomial expansion and derivation, the phase and group velocities for each Lamb wave mode could be computed. Thus, the steering vectors of all potential scattering Lamb waves for each grid in the scanning area can be established, thereby allowing for the formulation of the MUSIC algorithm. To increase the precision and adaptability of the MUSIC method, the local wave components resulting from potential scatters are extracted with an adaptive window, which is governed by the group velocities and distances of Lamb wave propagation. As a result, the reconstructed dispersion relations and windowed wave components can be used to highlight the scattering features. For the method investigation, both a simulation and experiment are carried out, and both the dispersion curves and damage locations can be detected. The results demonstrate that damage localization is possible without theoretical dispersion data and baseline recordings while exhibiting a considerable accuracy and resolution.

A Coherent Wideband Acoustic Source Localization Using a Uniform Circular Array.

In modern applications such as robotics, autonomous vehicles, and speaker localization, the computational power for sound source localization applications can be limited when other functionalities get more complex. In such application fields, there is a need to maintain high localization accuracy for several sound sources while reducing computational complexity. The array manifold interpolation (AMI) method applied with the Multiple Signal Classification (MUSIC) algorithm enables sound source localization of multiple sources with high accuracy. However, the computational complexity has so far been relatively high. This paper presents a modified AMI for uniform circular array (UCA) that offers reduced computational complexity compared to the original AMI. The complexity reduction is based on the proposed UCA-specific focusing matrix which eliminates the calculation of the Bessel function. The simulation comparison is done with the existing methods of iMUSIC, the Weighted Squared Test of Orthogonality of Projected Subspaces (WS-TOPS), and the original AMI. The experiment result under different scenarios shows that the proposed algorithm outperforms the original AMI method in terms of estimation accuracy and up to a 30% reduction in computation time. An advantage offered by this proposed method is the ability to implement wideband array processing on low-end microprocessors.

Mixed Near-Field and Far-Field Sources Localization via Oblique Projection

This paper presents a novel mixed source localization algorithm based on high-order cumulant (HOC) and oblique projection techniques. To address the issue of lower accuracy in near-field source (NFS) localization compared to the far-field source (FFS) localization, the presented algorithm further enhances the accuracy of NFS localization. First, the FFS’s direction-of-arrival (DOA) estimate is acquired utilizing a multiple signal classification (MUSIC) spectral peak search. To classify mixed sources more effectively, we utilize the oblique projection technique, which can successfully prevent FFS information from influencing the estimation of NFS parameters. A HOC matrix with solely NFS DOA information is built by choosing array elements in a specific sequence. The estimation of the NFS DOA is then derived using the estimation of signal parameters via a rotational invariance technique (ESPRIT)-like algorithm. Finally, the NFS range is acquired by a MUSIC search. The performance of the presented algorithm is discussed in several aspects. Compared to existing matrix difference methods, the presented algorithm, which adopts the oblique projection method, achieves superior results in the separation of mixed sources. Without excessively increasing the computational complexity, it not only ensures the performance of localization parameter estimation for FFS but also estimates the NFS with higher precision. The numerical simulations attest to the superior performance of the presented algorithm.

Detection and evaluation of load oscillations in induction motors based on MCSA

Load oscillations of induction motors can be the consequence of electrical or mechanical faults. One of the most important techniques to diagnose load oscillations is the so-called motor current signature analysis (MCSA). It has won widespread popularity owing to its non-intrusive and cost-effective performance. The common practice of MCSA is to detect the characteristic harmonic components of stator current caused by load fluctuations. However, in certain scenarios, the characteristic components can be hardly tracked from the current spectrum. Power frequency interference, nonstationary randomness of the sampled signals, and other factors could all limit the use of classical current spectrum analysis. The presented solution is the joint application of variational mode decomposition (VMD), delay addition method, multiple signal classification (MUSIC), and neural network. Specifically, VMD, delay addition and MUSIC are combined to decompose the raw signals, eliminate the power frequency leakage interference and extract the weak oscillation features with enhanced visualization. Meanwhile, a neural network is generated to describe the statistical features of the processed signals, and further realize the automatic evaluation of the load oscillation severity level. Theoretical analysis and experimental verification have been conducted to support this article. The results suggest that the proposed algorithm contributes to a more sensitive detection of the presence of load oscillations, and a more reliable evaluation of their severity with respect to traditional MCSA techniques.

Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array.

Acoustic vector sensor (AVS) is a kind of sensor widely used in underwater detection. Traditional methods use the covariance matrix of the received signal to estimate the direction-of-arrival (DOA), which not only loses the timing structure of the signal but also has the problem of weak anti-noise ability. Therefore, this paper proposes two DOA estimation methods for underwater AVS arrays, one based on a long short-term memory network and attention mechanism (LSTM-ATT), and the other based on Transformer. These two methods can capture the contextual information of sequence signals and extract features with important semantic information. The simulation results show that the two proposed methods perform much better than the multiple signal classification (MUSIC) method, especially in the case of low signal-to-noise ratio (SNR), the DOA estimation accuracy has been greatly improved. The accuracy of the DOA estimation method based on Transformer is comparable to that of the DOA estimation method based on LSTM-ATT, but the computational efficiency is obviously better than that of the DOA estimation method based on LSTM-ATT. Therefore, the DOA estimation method based on Transformer proposed in this paper can provide a reference for fast and effective DOA estimation under low SNR.

Eliminating Space Scanning: Fast mmWave Beam Alignment with UWB Radios

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays. To eliminate the need for complex space scanning, this article presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge of applying this idea in the real world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a small number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUltiple SIgnal Classification (MF-MUSIC) algorithm, which extends the classic MUltiple SIgnal Classification (MUSIC) algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. Extensive real-world experiments and numerical simulations illustrate the advantage of the proposed MF-MUSIC algorithm. MF-MUSIC uses only three antennas to achieve an accurate angle estimation, which is a mere 0.15° (or a relative difference of 3.6%) different from the state-of-the-art 16-antenna-based angle estimation method.

Source localization using recursively applied and projected MUSIC with flexible extent estimation.

Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in-vivo at a high temporal resolution but low spatial resolution. Locating the neural sources underlying the M/EEG poses an inverse problem, which is ill-posed. We developed a new method based on Recursive Application of Multiple Signal Classification (MUSIC). Our proposed method is able to recover not only the locations but, in contrast to other inverse solutions, also the extent of active brain regions flexibly (FLEX-MUSIC). This is achieved by allowing it to search not only for single dipoles but also dipole clusters of increasing extent to find the best fit during each recursion. FLEX-MUSIC achieved the highest accuracy for both single dipole and extended sources compared to all other methods tested. Remarkably, FLEX-MUSIC was capable to accurately estimate the level of sparsity in the source space (r = 0.82), whereas all other approaches tested failed to do so (r ≤ 0.18). The average computation time of FLEX-MUSIC was considerably lower compared to a popular Bayesian approach and comparable to that of another recursive MUSIC approach and eLORETA. FLEX-MUSIC produces only few errors and was capable to reliably estimate the extent of sources. The accuracy and low computation time of FLEX-MUSIC renders it an improved technique to solve M/EEG inverse problems both in neuroscience research and potentially in pre-surgery diagnostic in epilepsy.

Cross Dipole Antennas Solution for Angle of Arrival Estimation

The Multiple Signal Classification (MUSIC) algorithm is the most popular algorithm to estimate the Angle of Arrival (AOA) of the received signals. The analysis of this algorithm (MUSIC) with typical array antenna element ( ) shows that there are two false direction indication in the planaligned with the axis of the array. In this paper a suggested modification on array system is proposed by using two perpendiculars crossed dipole array antenna in spite of one array antenna. The suggested modification does not affect the AOA estimation algorithm. The simulation and results shows that the proposed solution overcomes the MUSIC problem without any effect on the performance of the system.

A Novel Wide-Band Directional MUSIC Algorithm Using the Strength Proportion.

The directional multiple signal classification (Dir-MUSIC) algorithm based on the antenna gain array manifold has been proposed to find the direction of the partial discharge (PD) source in substations. However, PD signals are wideband signals and the antenna gain pattern functions are always different at different frequencies; therefore, the accuracy can be improved using a wideband Dir-MUSIC algorithm. In this paper, wideband Dir-MUSIC algorithms are discussed and a novel wideband Dir-MUSIC algorithm using the strength proportion (DirSP) is proposed. This algorithm estimates a focusing PD signal at a certain frequency using the strength proportion among different directions, and then the Dir-MUSIC algorithm can process the focusing PD signal at this frequency. In simulations, when the antenna gain functions among different frequency bins are quite different, the Dir-MUSIC algorithm loses accuracy; meanwhile, DirDP performs very well. In the experiments, we deal with six sets of samples, and the mean error and standard deviation are both smaller than 4° better than other methods.

Multi-Leakage Source Localization of Safety Valve Based on Uniform Circular AE Array and Improved MUSIC Algorithm.

The safety valve is the core component of the pressure-relief protection device for pressure-bearing special equipment. When the safety valve leaks, the medium of the pressure vessel will be lost and wasted, which may cause safety accidents. With the aim to solve the problem of accurately locating the multiple leakage sources of safety valves, a localization method combining a uniform circular array acoustic emission detection and an improved multiple signal classification (MUSIC) algorithm is proposed. First, an improved wavelet threshold function denoising method is introduced to extract acoustic emission signals with high SNR, thereby reducing the rank of the covariance matrix, weakening the noise dispersion caused by eigenvalue reconstruction, avoiding signal and noise cross-confusion, and improving positioning accuracy. By introducing a windowed fast Fourier transform (FFT) frequency division processing link to obtain narrowband signal, the premise of using MUSIC positioning algorithm is established. In addition, a forward/backward spatial smoothing algorithm is introduced in the decoherence link to reduce co-channel interference, reduce the rank loss of the signal covariance matrix, and improve the positioning accuracy of the algorithm. The results show that when the working pressure is 0.70 MPa, 0.75 MPa, and 0.80 MPa, the deviation between the azimuth angle and elevation angle positioning results of each leakage source obtained by the improved MUSIC algorithm and the actual angle does not exceed 2°, and the relative error does not exceed 3.5%. Therefore, the improved MUSIC algorithm can accurately locate multiple leakage sources of the safety valve, and as the working pressure of the safety valve increases, the positioning accuracy of the improved MUSIC algorithm also increases accordingly.

Block-MUSIC in blade tip timing: Performance study of block snapshot matrix

Periodic nonuniform sampling can be viewed as a combination of uniform sampling and non-uniform sampling. Blade tip timing (BTT) technique conforms to this sampling pattern, where non-uniformity and uniformity are caused by non-uniform probe layout and rotational motion, respectively. By introducing block structure into multiple signal classification (MUSIC) for the extension of snapshot matrix, Block-MUSIC has been proved to be anti-aliasing and can filter out synchronous frequency in BTT application. However, the theoretical justification of the block structure is lacking. In this paper, a performance study of Block-MUSIC is conducted from three aspects: identifiability, stability, and resolution. Additionally, a pseudo 2-dimensional signal model is proposed to represent the periodic nonuniform sampled signal under the assumption of equally spaced probes. To support the performance study, simulations and experiments are carried out to demonstrate how the size of the snapshot matrix, the noise level, and the amplitude of sinusoidal signals affect algorithm performance. Simulation codes are available at https://github.com/superjdg/block-music4btt.

Experimental demonstration of low signal-to-noise ratio matched field processing with a geoacoustic model extracted from noise.

Passive localization of a low signal-to-noise ratio (SNR) source in a shallow water waveguide without prior geoacoustic information is accomplished by combining the mode-extraction method modal-MUSIC (multiple signal classification) with range-coherent matched field processing (MFP). Range-coherent MFP coherently combines snapshots from different resolution cells to obtain gain over noise. Modal-MUSIC uses knowledge of the water column sound speed profile (no bottom information) to extract noisy estimates of modal wavenumbers from ship noise recorded on a partially spanning vertical line array (VLA). A geoacoustic model is then fit to the wavenumber estimates extracted from noise with modal-MUSIC and used to compute replicas for range-coherent MFP. The combination of these two methods applied to a 21-element VLA achieves successful source localization at SNR levels as low as -20 dB using ten tonals transmitted during the SWellEx96 experiment.

High-Performance of Eigenvalue Decomposition on FPGA for the DOA Estimation

For the direction of arrival (DOA) in array signal processing, eigenvalue decomposition (EVD) is one key issue in hardware implementation of the multiple signal classification (MUSIC) algorithm. Therefore, we introduce the look-ahead sim- plified one-sided Jacobi's method to efficiently decompose those symmetric matrices in this article and prove that the new method has the best orthogonality of eigenvector and locates eigenvectors closest to the true solution in theory. Both the numerical perform- ance and real-time are important in engineering, so we present the novel flexible hardware architecture in single floating point arithmetic for EVD on field-programmable gate arrays (FPGAs). Finally, the simulated and raw data are used to investigate the performance of some different approaches in the context of both the EVD and MUSIC algorithm. The experimental results show that our proposed method has the best performance.

Robust Mainlobe Interference Suppression Based on Joint Oblique Projection for Dual Polarization Conformal Array

In this paper, a robust mainlobe interference suppression algorithm is proposed in a space-polarization joint domain based on a dual-polarized conformal array. An oblique projection filter can eliminate the interference while retaining the desired signal information. However, little deviation in signal parameters will lead to significant performance degradation. To solve such problems, the joint steering vector of the desired signal is corrected by solving the quadratic convex optimization problem. Parameters of interferences are first estimated by using multiple signal classification (MUSIC) joint spectrum, and then, the interference covariance matrix can be reconstructed individually to further estimate the interference steering vector. After that, the joint oblique projection operator can be established by the estimated steering vectors. Consequently, an antijamming beamformer can be obtained by the joint oblique projection filter to achieve robustness on the interference parameter estimation deviation and desired signal mismatch. Simulation experiments verify the effectiveness of the proposed method.