Multiple Signal Classification Method Research Articles

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.

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.

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.

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.

Spatial Spectrum Estimation of Weak Scattering Wave Signal in Range-Doppler Domain

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain is analyzed in principle, and the SNR gain expression of weak scattering wave signal is derived when constructing multi-snapshots virtual array data. On this basis, the mutual eigenvector singular value decomposition (MESVD) method based on RD domain mode excitation is proposed, which can robustly and effectively estimate the direction of the coherent weak signals. Simulation experiments verify that the RD domain spectral estimation method has the ability to simultaneously obtain the direction of multiple weak target scattering waves, and the direction-finding accuracy can reach the Cramer–Rao bound (CRB) of conventional spectral estimation method. The results of Monte Carlo experiments show that the root-mean-square-error (RMSE) of azimuth estimation of RD domain spatial spectrum estimation method is 5.76° lower than that of a conventional multiple signal classification (MUSIC) method. In addition, the practicability of the proposed method is demonstrated by comparing the DOA estimation results of a set of real data with Automatic Dependent Surveillance-Broadcast (ADS-B) data.

C-Slot Circular Polarized Antenna for Hybrid Energy Harvesting and Wireless Sensing

This paper presents a new hybrid energy harvesting on electromagnetic solar for wireless energy harvesting of ambient from sensors of low-power devices. The axial ratio (AR) requirements produce Left-Hand Circular Polarization (LHCP) and Right-Hand Circular Polarization (RHCP) and simultaneously produce a 90-degree phase difference during energy harvesting, adopting a new design in designing a dual-feed broadband circular polarized (CP) antenna. To get the frequency band 2.3–2.4 GHz, we propose a C-Slot antenna with a circular patch dual feed. To estimate the diversity of the phase and magnitude output of the feed configuration under AR value, we used a 50 Ohm feed network output of the characteristic analysis for a dual feed CP antenna. An Axial ratio frequency range of less than 3 dB is achieved using polarization analysis with different branch channel couplers. To produce a DC output voltage, a high-frequency rectifier circuit embedded with a thin-film solar cell on the antenna is then connected to two T-junction power divider rectifiers, resulting in a high-efficiency design. A complete system-level analysis will include multiple signal classification methods of powered ambient RF energy using a wireless energy harvesting array that proposes a compact structure and demonstrates optimal configuration. Reliable operation in typical indoor environments indicates a self-contained sensor Node. Therefore, it has significant implications for powering small electronics and wireless sensor applications independently of the IoT Network or real implementation telecommunications industry.

Coherent signals direction estimation using high-order quantities in the acoustic field

To meet the demands of underwater remote detection technology, the receiving array must be extended to low frequencies by increasing the array aperture required for detection. Furthermore, the base array aperture must be increased owing to the loss of effective array aperture in coherent targets detection using most decoherent algorithms; however, this prevents its use on small platforms, such as autonomous underwater platforms. Thus, reducing the array aperture while ensuring high resolution is required. In this article, high-order quantities in the acoustic field are utilized to construct a full rank covariance matrix to Multiple Signal Classification Method (MUSIC), which achieves high-resolution results for small aperture arrays with small incident angular intervals in low-frequency coherent targets. The correlated noise of each sensor is considered in the small aperture arrays and the gain of each element in the new matrix is derived. The theoretical analysis, numerical simulations, and experimental verifications show the validity of the method.

Evaluation of the Influence of the Spatial Smoothing Procedure of the Correlation Matrix on the Efficiency of the 2D-MUSIC Method

The occurrence of correlations between radio signals arriving at the antenna array of the base station reduces the effectiveness of subspace methods for positioning user devices in wireless communication networks. For the purpose of radiation sources decorrelation, a spatial smoothing algorithm can be applied dividing the base station antenna array into sub-arrays and then averaging the signal correlation matrices obtained for these sub-arrays. In this paper, we investigate the impact of various ways of dividing a flat equidistant antenna array into subarrays on the performance of the 2D-MUSIC multiple signal classification method in the scenario of ultra-dense networks operating in the FR2 band.

Direction-of-arrival estimation of impulse noise using MUSIC algorithm

Spatial information about the source of sounds can be obtained by measuring amplitude and phase differences among multiple microphones in a microphone array. Various methods have been proposed for estimating the direction of arrival, including beam-forming methods, but generally their spatial spectral resolution degrades with a low-frequency source in the far-field. In architectural acoustics, it is important to obtain high spatial resolution in the low-frequency band, especially in investigations to understand phenomena such as sudden impulse noise. In this paper, direction-of-arrival estimation using the MUltiple SIgnal Classification (MUSIC) method is discussed. We propose a microphone array structure designed according to the Cramer-Rao lower limit. It is shown that this technique improves the accuracy of elevation angle estimates at low frequencies while maintaining good horizontal angle estimation accuracy. The proposed method was tested in a real building in which impulse noise occurred and its effectiveness in identifying the source direction with high spatial resolution even at low frequencies was confirmed.

Fast DOA Estimation Algorithms via Positive Incremental Modified Cholesky Decomposition for Augmented Coprime Array Sensors.

This paper proposes a fast direction of arrival (DOA) estimation method based on positive incremental modified Cholesky decomposition atomic norm minimization (PI-CANM) for augmented coprime array sensors. The approach incorporates coprime sampling on the augmented array to generate a non-uniform, discontinuous virtual array. It then utilizes interpolation to convert this into a uniform, continuous virtual array. Based on this, the problem of DOA estimation is equivalently formulated as a gridless optimization problem, which is solved via atomic norm minimization to reconstruct a Hermitian Toeplitz covariance matrix. Furthermore, by positive incremental modified Cholesky decomposition, the covariance matrix is transformed from positive semi-definite to positive definite, which simplifies the constraint of optimization problem and reduces the complexity of the solution. Finally, the Multiple Signal Classification method is utilized to carry out statistical signal processing on the reconstructed covariance matrix, yielding initial DOA angle estimates. Experimental outcomes highlight that the PI-CANM algorithm surpasses other algorithms in estimation accuracy, demonstrating stability in difficult circumstances such as low signal-to-noise ratios and limited snapshots. Additionally, it boasts an impressive computational speed. This method enhances both the accuracy and computational efficiency of DOA estimation, showing potential for broad applicability.

Combining circular laser sensing array with MUSIC algorithm for fast damage localization

Large plate shell structures are key supporting components for aerospace and transportation equipment. Rapid localization of damages in large-scale thin-walled structures is an urgently needed technology for on-site detection and maintenance. This paper proposed a rapid damage localization method based on a laser ultrasonic circular arrays, combining the advantages of the circular arrays and the MUSIC algorithm. It does not need reference signals in advance, the direct waves and scattered waves can be quickly distinguished and extracted according to the time of arrival (TOA). Then, the two-dimensional multiple signal classification method(2D-MUSIC) under near-field conditions is used to quickly locate damage, which is a feature subspace algorithm in array signal processing technology. Compared with other commonly used imaging algorithms, this method has great advantages in detection efficiency and angular resolution. According to the detection method proposed in this paper, the position of single damage and multiple incoherent damages within 0.12 m2 can be quickly found within 1.3 s, and the angle error and distance error are not more than 5° and 5 cm. However, for the damage of coherent sources, there may be artifacts interfering with the imaging results.

A Higher-Order Singular Value Decomposition-Based Target Localization Algorithm for WiFi Array Systems

Traditional Angle of Arrival (AoA)-based WiFi array indoor localization algorithms do not fuse Channel State Information (CSI) inter-packet data for estimation, which makes WiFi arrays less effective for localization in complex indoor environments. Most algorithms are overburdened leading to inefficient localization. To address these issues, in this article, an indoor positioning algorithm based on Higher-Order Singular Value Decomposition (HOSVD) is proposed. First, the CSI data are reconstructed as a new measurement matrix by borrowing subcarriers, and a third-order tensor is constructed. Next, tensor compression techniques are used to reduce computational complexity and the signal subspace is obtained by HOSVD. Then, the AoA is obtained by the Reduced Dimension Multiple Signal Classification (RD-MUSIC) method. Finally, the coordinates of the target can be obtained by triangulating the AoAs of the three Access Points (APs). According to the simulation experiments, the AoA can be estimated accurately at a low SNR and with low snapshots. In practical experiments, we can successfully estimate the AoA in complex indoor environments with shorter timelines using HOSVD without modifications to commercial hardware and produce a lower AoA error and localization error rates compared to other algorithms. The effectiveness of our proposed algorithm is proven by simulations and practical experiments.

Data-Driven DOA Estimation Methods Based on Deep Learning for Underwater Acoustic Vector Sensor Array

Abstract Direction of arrival (DOA) estimation is a fundamental problem in underwater acoustic vector sensor array signal processing. Because of the advantages of deep learning technology, this paper proposes two categories of data-driven DOA estimation methods for underwater acoustic vector sensor array, which transform the DOA estimation problem into a neural network classification problem. Specifically, one is the DOA estimation method of convolutional neural network based on teacher-student noise reduction (TS-CNN), which considers the covariance matrix as the training data set; the other is a DOA estimation method based on long-short term memory network and attention mechanism (LSTM-ATT), which applies the time-domain signal as the training data set. The experimental simulation results show that: 1) when the number of array elements is small, the accuracy of the DOA estimation method based on TS-CNN is equivalent to that of traditional methods, and it can effectively suppress the influence of noise when the signal-to-noise ratio (SNR) is low; 2) the accuracy of DOA estimation method based on LSTM-ATT is much higher than that of traditional Multiple Signal Classification method, especially in the case of low SNR, which also proves the importance of temporal characteristics for DOA estimation in a real environment.

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.

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.

An efficient damage imaging method for composite structure based on self-correcting phase error compensation MUSIC algorithm

The increasing use of composite materials on aircraft structures, most of which are plate-like, has attracted much attention for damage monitoring based on guided wave as a kind of structural health monitoring (SHM) method. The multiple signal classification (MUSIC) algorithm is a directional scanning and searching method to unbiasedly estimate the signal features in terms of the orthogonal attributes between signal subspace and noise subspace. The MUSIC algorithm is gradually shifted into the field of SHM used for damage localization. However, the anisotropy of the composites and the sensor position errors cause the phase errors of the array response signal, resulting in a decrease in the accuracy of the MUSIC method. In addition, the complexity of the array monitoring network causes a significant increase in the amount of operations for signal information processing, making online real-time monitoring challenging. In order to reduce the impact of multiple forms of array errors on the MUSIC algorithm and to improve the efficiency of it, this paper proposes an efficient damage imaging method for composite structure based on self-correcting phase error compensation MUSIC algorithm, which compensates for the array phase errors caused by structural anisotropy and sensor position errors, and achieves high efficiency. The method has been verified on a stiffened composite structure, and the results show its superiority and effectiveness.

Structured channel covariance estimation from limited samples for large antenna arrays

In massive multiuser multiple antenna systems, the knowledge of the users’ channel covariance matrix is crucial for minimum mean square error channel estimation in the uplink and it plays an important role in several multiuser beamforming schemes in the downlink. Due to the large number of base station antennas, accurate covariance estimation is challenging especially in the case where the number of samples is limited and thus comparable to the channel vector dimension. As a result, the standard sample covariance estimator may yield a too large estimation error which in turn may yield significant system performance degradation with respect to the ideal channel covariance knowledge case. To address such problem, we propose a method based on a parametric representation of the channel angular scattering function. The proposed parametric representation includes a discrete specular component which is addressed using the well-known MUltiple SIgnal Classification (MUSIC) method, and a diffuse scattering component, which is modeled as the superposition of suitable dictionary functions. To obtain the representation parameters, we propose two methods, where the first solves a nonnegative least-squares problem and the second maximizes the likelihood function using expectation–maximization. Our simulation results show that the proposed methods outperform the state of the art with respect to various estimation quality metrics and different sample sizes.

High-Precision Fast Direction-of-Arrival Estimation Method for Planar Array

The multiple signal classification method for direction-of-arrival estimation is widely applied in practical scenarios. However, the multiple signal classification method with planar array requires 2-dimensional on-grid spectrum searches, which would lead to the grid mismatch and high computational complexity. Therefore, a high-precision fast direction-of-arrival estimation method for planar array is proposed. In the proposed method, a 2-stage grid search approach over the 2-dimensional spectrum is firstly applied to obtain a quick coarse estimation of direction of arrival. Then, the estimation of higher precision is achieved via a quadratic surface fitting method. Simulation results verified the effectiveness of the proposed method.

Waveform Design and DoA-DoD Estimation of OFDM-LFM Signal Based on SDFnT for MIMO Radar

In comparison to orthogonal frequency division multiplexing (OFDM), the OFDM linear frequency modulation (OFDM-LFM) signal has similar time-bandwidth product, lower peak-to-average power ratio, and higher sensitivity to Doppler frequency shift, making it a promising candidate waveform for multiple-in multiple-out (MIMO) radar applications. However, the OFDM-LFM signal is unsatisfactory in practical applications due to the challenges in waveform design and its non-stationary property, which significantly degrades the performance of current high-resolution subspace techniques for direction of departure (DoD) and direction of arrival (DoA) angle estimation. In this paper, we first proposed a novel OFDM-LFM waveform design method based on the scale discrete Fresnel transform (SDFnT), which efficiently generates orthogonal LFM signals with unlimited number of subcarriers and can be convieniently implemented using the fast Fourier transform (FFT). Then, for angle estimation in OFDM-LFM MIMO radars, we presented a new scale discrete Fresnel transform multiple signal classification (SDFnT-MUSIC) method. By converting the steering vector into the chirp domain and making it non-time-vary, high resolution angle estimation for OFDM-LFM wideband signals can be achieved. Simulation results shows that the proposed waveform design method requires less computational time than conventional method based based on the fractional Fourier transform (FrFT), whereas the proposed DoA-DoD estimation method outperform the FrFT-MUSIC method in terms of computational complexity and target angle estimate performance.

Super-resolution time delay estimation using exponential kernel correlation in impulsive noise and multipath environments

The time delay estimation (TDE) is an important and fundamental part in wireless communication signal processing, which has been widely used in localization, intelligent navigation, and radio monitoring. However, it remains a challenging task to support such TDE mechanisms with super-resolution in impulsive noise and multipath environments. Aiming at improving the TDE performance in both impulsive noise and multipath environments, in this paper, a novel super-resolution TDE method is proposed by using the defined exponential kernel correlation (EKC) function. In the proposed TDE method, EKC function is defined to suppress the non-Gaussian impulsive noise. Specifically, the theoretical analysis is also given to prove its boundedness and its effectiveness in non-Gaussian impulsive noise suppression. Further, a novel super-resolution TDE method is proposed by combining EKC function and multiple signal classification (MUSIC) method, which can significantly improve the estimation performance in terms of resolution and accuracy with both non-Gaussian impulsive noise and multipath environments. Finally, simulation experiments demonstrate that the proposed super-resolution TDE method maintains good performance even with a low characteristic exponent and low generalized signal-to-noise ratio (GSNR).