Multiple Signal Classification Algorithm Research Articles

Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array

The power transformer is one of the most critical core devices for energy exchange in power systems, and its safe and stable operation is directly related to the reliability of the power grid. Partial discharge is the main cause of insulation degradation and failure of high-voltage electrical equipment. Online monitoring and accurate localization of partial discharge can provide information on the aging of power equipment, which is of great value for improving the safe operation and maintenance of the power grid. Internal dual partial discharge in transformer windings is a more complex type of fault. Since it is located inside the windings, the signal is attenuated and distorted, making it difficult for traditional monitoring methods to capture such partial discharge signal The Fabry–Perot optical fiber sensor is an ultrasonic detection method that can be built into the transformer interior. This sensor has high sensitivity and a small size, enabling flexible placement at different locations inside the transformer for precise partial discharge detection. Especially for the narrow space inside the high and low voltage windings, F–P sensors can form an array, utilizing the array’s directivity to locate the fault points. In this study, an ultrasonic detection system based on the F–P optical fiber sensor array was developed. The system utilizes a directional cross-localization algorithm based on the multiple signal classification (MUSIC) algorithm to accurately locate dual partial discharge sources. This partial discharge detection system was applied to a 35 kV single-phase transformer, enabling the localization of dual partial discharge sources within the high and low voltage windings. Combined with experimental results, this method exhibits high localization accuracy and is particularly suitable for detecting partial discharge phenomena that occur within or between transformer windings.

The angle of arrival estimation of frequency-hopping cooperative object based on software-defined radio

The primary objective of this research is to propose and examine a technique for accurately determining the azimuth angle of cooperative objects. The proposed methodology aims were to achieve fast processing, increased precision, and enhanced safety. A transmitter emitting a continuous wave with a hopping frequency was utilized in combination with interferometry techniques to measure the angle of arrival (AOA) between two antennas. The efficient method incorporates a coarse-to-fine strategy that improves processing speed and azimuth angle accuracy and to effectively eliminate any signal interference, a Fourier bandpass filter is utilized. The coarse estimation is performed using the fast Fourier transform (FFT) algorithm, while the fine estimation is achieved using the multiple signal classification (MUSIC) algorithm. The fine estimation involves utilizing coarse angle input and a scan limit of 2.5° that is determined from the largest simulated root mean square error (RMSE) value. Modelling and outdoor testing using software defined radio (SDR) have been carried out to assess the proposed methodology. The results from the analysis indicate that the proposed method produces the desired RMSE estimation of less than 1°, thereby validating its accuracy and effectiveness.

Enhanced angle estimation in MIMO radar: Combine RD-MUSIC and SDP optimization

Multiple Input Multiple Output (MIMO) radars are increasingly employed. However, with a growing array of antenna components, the dimension of the sampling covariance matrix and the computational complexity of traditional Direction of Arrival (DOA) and Direction of Departure (DOD) estimation algorithms increase exponentially. In order to accurately and effectively identify and locate signal sources and to optimize the computational complexity, this paper presents an innovative approach for detecting target numbers based on a Semi-Definite Programming (SDP) problem, which generates efficient solutions even for higher dimensions. In addition, the SDP’s robustness is developed through shrinkage adaptation using Large Covariance Matrices (LCMs), which are crucial for angle estimation. Following the effective detection of a target, the tapering estimation is applied to the LCMs under the computationally efficient Reduced-Dimension Multiple Signal Classification (RD-MUSIC) algorithm, which is more accurate and less computationally intensive than 2D search algorithms. Simulation outcomes demonstrate that the efficacy of our proposed model achieves high success rates in target detection and enhanced precision in DOA and DOD estimations.

Direction of Arrival Estimation Method Based on Eigenvalues and Eigenvectors for Coherent Signals in Impulsive Noise

In this paper, a Toeplitz construction method based on eigenvalues and eigenvectors is proposed to combine with traditional denoising algorithms, including fractional low-order moment (FLOM), phased fractional low-order moment (PFLOM), and correntropy-based correlation (CRCO) methods. It can improve the direction of arrival (DOA) estimation of signals in impulsive noise. Firstly, the algorithm performs eigenvalue decomposition on the received covariance matrix to obtain eigenvectors and eigenvalues, and then the Toeplitz matrix is created according to the eigenvectors corresponding to its eigenvalues. Secondly, the spatial averaging method is used to obtain an unbiased estimate of the Toeplitz matrix, which is then weighted and added based on the corresponding eigenvalues. Next, the noise subspace of the Toeplitz matrix is reconstructed to obtain the one that has less angle information. Finally, the DOA of the coherent signal is estimated using the Multiple Signal Classification (MUSIC) algorithm. The improved method based on the Toeplitz matrix can not only suppress the effect of impulsive noise but can also solve the problem of aperture loss due to its decoherence. A series of simulations have shown that they have better performances than other algorithms.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave (FMCW) radar is an advantageous sensor scheme for target estimation and environmental perception. However, existing algorithms based on discrete Fourier transform (DFT), multiple signal classification (MUSIC) and compressed sensing, etc., cannot achieve both low complexity and high resolution simultaneously. This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar. Firstly, we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy. Secondly, we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data, which improves the efficiency of continuous target tracking. These two approaches have improved the algorithm efficiency by nearly 2–4 orders of magnitude without losing accuracy and resolution. Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

On the Generalization of Deep Learning Models for AoA Estimation in Bluetooth Indoor Scenarios

Indoor positioning remains as one of the major challenges of the Internet of Things (IoT), where assets are usually deployed over environments where the Global Navigation Satellite Systems (GNSS) are denied. Angle-of-arrival (AoA) estimation techniques are among the most prominent candidates to address this challenge and multiple techniques based on conventional signal processing, such as the Multiple Signal Classification (MUSIC) algorithm, have been extensively studied. However, they require a deep knowledge of the elements involved in the AoA system, such as the characterization of the receivers’ antennas. On the other hand, Deep Learning (DL) models are considered as promising solutions, as they can provide AoA estimations without resorting to a deep knowledge of the physical system. Instead, they only need to be trained with labeled AoA signal measurements. Nevertheless, in the training process, non-desired effects like overfitting appear, yielding models with poor generalization capabilities. Although these models have already been considered in the literature, the problem of DL models generalization has not been addressed in depth. Therefore, this manuscript first compares the performance of DL models to the MUSIC algorithm as a baseline, to then analyze their generalization to different situations. These include changing the position of the receivers within the same area, considering measurements at different time instants and deployments in new scenarios, specifically in locations different from the training one. The assessment showed that the generalization of the DL models is weak compared to the MUSIC algorithm, especially when applied to environments not previously observed.

Research on Direction Finding Method under Impulsive Noise Based on Nonuniform Linear Array

Direction of arrival (DOA) estimation under impulsive noise has always been an important research area in array signal processing. The traditional methods under impulsive noise mostly rely on prior parameters and have high computational complexity. Based on the filtering theory, we present an effective pretreatment filtering technology to cut out the impulse mixed in the array received data and employ the nonuniform linear array to improve the estimation performance further. First, according to the amplitude characteristics of impulse noise, the pretreatment filtering technology is proposed to cut out the impulse based on the median filter and sliding average filter, which is valid for both strong and weak impulsive noise. Second, the minimum redundant array is adopted to carry out array virtual expansion so that the array aperture can be increased and the estimation performance can be improved. Finally, based on the idea of matrix reconstruction, we propose the improved estimation of signal parameters via rotational invariance techniques algorithm and an improved root multiple signal classification algorithm for DOA estimation. Theoretical analysis and simulation results show that the proposed method has a simple processing process, small calculation load, good array expansion ability, and excellent noise adaptability. Moreover, the proposed methods greatly improve the direction-finding performance under the condition of low signal-to-noise ratio and strong impulsive noise.

Transmitter Precoder Design to Improve the Performance of the MUSIC Algorithm

Using the MUSIC (MUltiple SIgnal Classification) algorithm to estimate the angle-of-arrival (AoA), we derive a new asymptotic error variance bound when the transmitted signal can be pre-processed. We next propose a precoder design to achieve this bound. However, such an optimal precoder requires channel state information at the transmitter (CSIT) exclusive of the receiver array which cannot be separately estimated practically. A more feasible precoder design, which leverages on the feedback CSIT estimated at the receiver, is next proposed. Using the performance of the optimal precoder which achieves the bound asymptotically as a benchmark, the practical precoder design performs closely to the optimal precoder even in high-resolution scenario. Both precoder schemes exhibit performance improvement compared with the case when no precoder is used.

Evaluation of Antenna Pattern Measurement of HF Radar using Drone

The High-Frequency Radar (HFR) is an equipment designed to measure real-time surface ocean currents in broad maritime areas.It emits radio waves at a specific frequency (HF) towards the sea surface and analyzes the backscattered waves to measure surface current vectors (Crombie, 1955; Barrick, 1972).The Seasonde HF Radar from Codar, utilized in this study, determines the speed and location of radial currents by analyzing the Bragg peak intensity of transmitted and received waves from an omnidirectional antenna and employing the Multiple Signal Classification (MUSIC) algorithm. The generated currents are initially considered ideal patterns without taking into account the characteristics of the observed electromagnetic wave propagation environment. To correct this, Antenna Pattern Measurement (APM) is performed, measuring the strength of signals at various positions received by the antenna and calculating the corrected measured vector to radial currents.The APM principle involves modifying the position and phase information of the currents based on the measured signal strength at each location. Typically, experiments are conducted by installing an antenna on a ship (Kim et al., 2022). However, using a ship introduces various environmental constraints, such as weather conditions and maritime situations. To reduce dependence on maritime conditions and enhance economic efficiency, this study explores the possibility of using unmanned aerial vehicles (drones) for APM. The research conducted APM experiments using a high-frequency radar installed at Dangsa Lighthouse in Dangsa-ri, Wando County, Jeollanam-do. The study compared and analyzed the results of APM experiments using ships and drones, utilizing the calculated radial currents and surface current fields obtained from each experiment.

Antenna Pattern Calibration Method for Phased Array of High-Frequency Surface Wave Radar Based on First-Order Sea Clutter

The problem of accurate source localization has been an area of focus in high-frequency surface wave radar (HFSWR) applications. However, antenna pattern distortion (APD) decreases the direction-of-arrival (DOA) estimation performance of the multiple signal classification (MUSIC) algorithm. Up to now, limited studies have been conducted on the calibration of antenna pattern distortion for phased arrays in HFSWR. In this paper, we first analyze the effect of APD on the performance of the MUSIC algorithm through estimation of accuracy and angular resolution. We demonstrate that using the actual pattern (or say APD) can improve DOA estimation performance. Based on this proposition, we propose a novel iterative calibration method that employs the first-order sea clutter data and can jointly estimate DOA and APD in an iterative way. To obtain available calibration points, we introduce the extraction methods of the first-order sea clutter spectrum and single-DOA spectrum points. Meanwhile, in each iteration, the Beamspace MUSIC algorithm and artificial hummingbird algorithm (AHA) are utilized to estimate the DOA and APD, respectively. Numerical results reveal a good coincidence between the actual pattern and the estimated APD. We also apply this method to process the experimental data of HFSWR. We obtain the APD vector of the real phased array and improve the direction-finding performance of several real ship targets using this vector. Both numerical and experimental results prove the correctness of our proposed calibration method.

Performance evaluation of sound source localisation and tracking methods using multiple drones

This study evaluates methods to improve sound source localisation and tracking performance using microphone arrays from multiple drones. Previous studies have demonstrated the feasibility of multi-drone sound source tracking by triangulating their respective direction estimations using the MUltiple SIgnal Classification (MUSIC) algorithm. In addition, we evaluate techniques to reduce the influence of rotor noise. Namely, this includes a Wiener filter that utilises rotor noises' power spectral density (PSD) estimations and a noise correlation matrix (NCM) estimation scheme exploiting the geometries of the drone and the microphone array. This is to reduce the rotor noise's influence on the microphone array input correlation matrix prior to localising with MUSIC. Results show that applying the rotor noise reduction method improves localisation and tracking accuracy under a wider range of signal-to-noise ratios, regardless of the sound source tracking method.

Localization of mechanical and electrical defects in dry-type transformers using an optimized acoustic imaging approach.

This paper presents an acoustic imaging localization system designed to pinpoint common defects in dry-type transformers by analyzing the unique sounds they produce during operation. The system includes an optimized microphone array and an improved multiple signal classification algorithm. Sound signal characteristics of typical defects, such as foreign object intrusion, screw loosening, and partial discharge, are investigated. A 64-element, 8-arm spiral microphone array is designed using a particle swarm optimization algorithm. The multiple signal classification algorithm enhances acoustic imaging quality in field environments by transforming the input from time-domain to preprocessed frequency-domain signals. The power spectra of subarray and main array are combined, forming the optimization algorithm's output. Experimental results demonstrate the system's effectiveness and accuracy.

Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources.

In this paper, we propose a novel simultaneous Correlative Interferometer (CI) technique that elaborately estimates the Direction of Arrival (DOA) of multiple source signals incident on an antenna array. The basic idea of the proposed technique is that the antenna-array-based receiver compares the phase of the received signal with one of the candidates at each time sample and jointly exploits these multiple time samples to estimate the DOAs of multiple signal sources. The proposed simultaneous CI-based DOA estimation technique collectively utilizes multiple time-domain samples and can be regarded as a generalized version of the conventional CI algorithm for the case of multiple time-domain samples. We first thoroughly review the conventional CI algorithm to comprehensively explain the procedure of the direction-finding algorithm that adopts the phase information of received signals. We also discuss several technical issues of conventional CI-based DOA estimation techniques that are originally proposed for the case of a single time-domain sample. Then, we propose a simultaneous CI-based DOA estimation technique with multi-sample diversity as a novel solution for the case of multiple time-domain samples. We clearly compare the proposed simultaneous CI technique with the conventional CI technique and we compare the existing Multiple Signal Classification (MUSIC)-based DOA estimation technique with the conventional CI-based technique by using the DOA spectrum as well. To the best of our knowledge, the simultaneous CI-based DOA estimation technique that effectively utilizes the characteristics of multiple signal sources over multiple time-domain samples has not been reported in the literature. Through extensive computer simulations, we show that the proposed simultaneous CI technique significantly outperforms both the conventional CI technique in terms of DOA estimation even in harsh environments and with various antenna array structures. It is worth noting that the proposed simultaneous CI technique results in much better performance than the classical MUSIC algorithm, which is one of the most representative subspace-based DOA estimation techniques.

Exploration on 2D DOA estimation of linear array motion: Uniform linear motion

Generally, due to the limitation of the dimension of the array aperture, linear arrays cannot achieve two-dimensional (2D) direction of arrival (DOA) estimation. But the emergence of array motion provides a chance for that. In this paper, a generalized motion scheme and a novel method of 2D DOA estimation are proposed by exploring the linear array motion. To be specific, the linear arrays are controlled to move along an arbitrary direction at a constant velocity and snap per fixed time delay. All the received signals are processed to synthesize the comprehensive observation vector for an extended 2D virtual aperture. Subsequently, since most of 2D DOA estimation methods are not universal to our proposed motion scheme and the reduced-dimensional (RD) method fails to handle the case of the coupled parameters, a decoupled reduced-complexity multiple signals classification (DRC MUSIC) algorithm is designed specifically. Simulation results demonstrate that: a) our proposed scheme can achieve underdetermined 2D DOA estimation just by the linear arrays; b) our designed DRC MUSIC algorithm has the good properties of high accuracy and low complexity; c) our proposed motion scheme with the DRC method has better universality in the motion direction.

Frequency estimation of multiple complex sinusoids using noise suppressing predictive FIR filter

The paper presents a predictive filter based frequency estimation algorithm (PF-FE) for multiple complex sinusoids corrupted by additive white Gaussian noise. The proposed frequency estimator combines the noise suppression capability of an FIR filter with a least squares approach minimizing the sum of the squared errors of a prediction filter. The FIR filter coefficients are chosen such that the sinusoidal components are predicted with minimum distortion. Computer simulation results are given to contrast the performance of the proposed PF-FE method with six other state-of-the-art frequency-estimators and the Cramer-Rao lower bound. Furthermore, the paper provides a complexity analysis of the PF-FE method and compares it with those of the conventional MUltiple SIgnal Classification algorithm and some selected state-of-the-art methods from the literature. Performance of the proposed PF-FE method is validated with effective mean square error (EMSE) plots for model orders of three and five and under different observation lengths and signal-to-noise ratio values. Finally, the paper demonstrates how EMSE varies for the proposed method when the prediction filter length is increased from 20 to 75. After extensive simulations, it is observed that when filter length is larger than half the observation length the EMSE will start to degrade.

Computationally efficient angle estimation of bistatic MIMO radar based on multimodal optimization

AbstractIn this letter, a computationally efficient multiple signal classification (MUSIC)‐based evolutionary algorithm for angle estimation of bistatic multiple‐input multiple‐output (MIMO) radar is proposed. The existing MUSIC algorithms require a computationally cumbersome two‐dimensional (2D) peak searching and the performance is highly related to the grid that set, which leads to a conflict between the computational efficiency and estimation performance. To address this difficulty, a multimodal quantum‐inspired salp swarm algorithm, integrating kmeans clustering technique, is proposed to substitute the 2D peak searching to obtain multiple maxima of the MUSIC algorithm. The resulting computationally efficient algorithm obviously reduces the computational complexity of the MUSIC algorithm, avoids grid errors, and further exploits the potential of the MUSIC algorithm. Numerical simulations in various scenarios are carried out to verify the superiority of the method.

Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT

Integrated sensing and communication (ISAC) in the Industrial Internet of Things (IIoT) presents unique challenges in terms of localization techniques. While three-dimensional (3D) environments offer extra challenges to enhanced accuracy and realism, research in this area remains limited. To bridge this gap, we propose a novel localization technique assisted by a single base station (BS) in 3D IIoT scenarios. Our approach employs the MUltiple SIgnal Classification (MUSIC) algorithm to jointly estimate the angle of arrival (AoA) in azimuth and elevation, as well as the time of arrival (ToA). Compared to conventional multi-BS-assisted or MUSIC-based algorithms, our technique offers flexibility, easy implementation, and low computational cost. To improve performance, we integrate the Taylor-series into the iterative process after a MUSIC-based joint azimuth, elevation angle and delay estimation (JAEDE), resulting in a significant 99% reduction in computational complexity compared to a two-step MUSIC-based approach utilizing coarse-fine grid searching. Through numerical simulations, we compare our algorithm with three other MUSIC-based joint or separate estimation approaches, demonstrating its superior performance in azimuth angle of arrival (AoAz), elevation angle of arrival (AoAe), TOA, and overall location estimation across varying signal-to-noise ratio (SNR) conditions.

An Enhanced Spatial Smoothing Technique of Coherent DOA Estimation with Moving Coprime Array.

This paper investigates the direction of arrival (DOA) estimation of coherent signals with a moving coprime array (MCA). Spatial smoothing techniques are often used to deal with the covariance matrix of coherent signals, but they cannot be used in sparse arrays. Therefore, super-resolution algorithms such as multiple signal classification (MUSIC) cannot be applied in the DOA estimation of coherent signals in sparse arrays. In this study, we propose an enhanced spatial smoothing method specifically designed for MCA. Firstly, we combine the signals received by the MCA at different times, which can be regarded as a sparse array with a larger number of array sensors. Secondly, we describe how to compute the covariance matrix, derive the signal subspace by eigenvalue decomposition, and prove that the signal subspace is also equivalent to a received signal. Thirdly, we apply enhanced spatial smoothing to the signal subspace and construct a rank recovered covariance matrix. Finally, the DOA of coherent signals are well estimated by the MUSIC algorithm. The simulation results validate the improved performance of the proposed algorithm compared with traditional methods, particularly in scenarios with low signal-to-noise ratios.

Partial broken rotor bar fault diagnosis using signal injected and generated Hilbert method

Diagnosing induction motor BRB fault at an incipient level, that is, detecting a partial (half) BRB fault, is a challenging task, especially when motor is operating under light-load conditions. Several methods, including MUltiple SIgnal Classification (MUSIC), have been applied in literature for diagnosing this type of fault, especially under light-load conditions. However, this method is time-consuming, making it computationally expensive and unfeasible. Herein, we present a new algorithm that can reduce the computational time required for detecting half-BRB fault. The proposed algorithm captures the original current and modulates the same by injecting it with a high-amplitude signal of known frequency. Next, it applies Hilbert transform to extract the envelope of the mixed signal. This proposed method ultimately reduces the computational time in comparison to applying MUSIC algorithm directly. Experiments performed under both healthy and faulty conditions to demonstrate the validity of the proposed Signal Injected and Generated Hilbert (SInGH) method.

DOA Estimation Based on Eigen Space and Toeplitz Processing

In order to efficiently and accurately estimate the direction of arrival of coherent signals, a Toeplitz estimation method based on eigen space multiple signal classification (MUSIC) algorithm is proposed. Firstly, the coherent signal is processed by Toeplitz, and then the MUSIC algorithm of the eigen space is used to estimate the DOA accurately. The computer simulation results show that the method can realize the accurate estimation of the angle under the condition of signal coherence, and the accuracy is improved compared with some other algorithms.