Signal Classification Algorithm Research Articles

FDA-MIMO Radar Rapid Target Localization via Reconstructed Reduce Dimension Rooting

Frequency diversity array–multiple-input multiple-output (FDA-MIMO) radar realizes an angle- and range-dependent system model by adopting a slight frequency offset between adjacent transmitter sensors, thereby enabling potential target localization. This paper presents FDA-MIMO radar-based rapid target localization via the reduction dimension root reconstructed multiple signal classification (RDRR-MUSIC) algorithm. Firstly, we reconstruct the two-dimensional (2D)-MUSIC spatial spectrum function using the reconstructed steering vector, which involves no coupling of direction of arrival (DOA) and range. Subsequently, the 2D spectrum peaks search (SPS) is converted into one-dimensional (1D) SPS to reduce the computational complexity using a reduction dimension transformation. Finally, we conduct polynomial root finding to further eliminate computational costs, in which DOA and range can be rapidly estimated without performance degradation. The simulation results validate the effectiveness and superiority of the proposed RDRR-MUSIC algorithm over the conventional 2D-MUSIC algorithm and reduced-dimension (RD)-MUSIC algorithm.

Height Measurement Method for Meter-Wave Multiple Input Multiple Output Radar Based on Transmitted Signals and Receive Filter Design

To address the issue of low-elevation target height measurement in the Multiple Input Multiple Output (MIMO) radar, this paper proposes a height measurement method for meter-wave MIMO radar based on transmitted signals and receive filter design, integrating beamforming technology and cognitive processing methods. According to the characteristics of beamforming technology forming nulls at interference locations, we assume that the direct wave and reflected wave act as interference signals and hypothesize a direction for a hypothetical target. Then, the data received are processed to obtain the height of low-elevation-angle targets using a cognitive approach that jointly optimizes the transmitted signal and receive filter. Firstly, a signal model for the meter-wave MIMO radar based on the transmit weight matrix is established under low-elevation scenarios. Secondly, the signal model is analyzed and transformed. Thirdly, the beamforming algorithm that jointly optimizes the transmitted signals and receive filter is derived and analyzed. The algorithm maximizes the output Signal-to-Interference-plus-Noise ratio (SINR) of the receiver by designing the transmit weight matrix and receive filter. The optimization problem based on the SINR criterion is non-convex and difficult to solve. We transformed it into two sub-optimization problems and approximated the optimal solution through an alternating iteration algorithm. Finally, the proposed height measurement algorithm is compared with the Generalized Multiple Signal Classification (GMUSIC) and Maximum Likelihood (ML) height measurement algorithms. Simulation results show that the proposed algorithm can realize the height measurement of low-elevation targets. Compared to the GMUSIC and ML algorithms, it demonstrates superior performance in terms of computational complexity and multi-target elevation estimation.

Effectiveness and Characteristics of Virtual Antennas in the Multiple Signal Classification Algorithm

This paper proposes a novel direction of arrival (DoA) estimation method based on the Multiple Signal Classification (MUSIC) algorithm using virtual antennas. The MUSIC method is a widely used DoA estimation technique known for its high accuracy and high resolution. However, it has a fundamental limitation: when the necessary condition of having more antenna elements at the base station than users is not met, DoA estimation becomes infeasible. To address this limitation, we introduce the concept of virtual antennas, allowing DoA estimation, even under restricted conditions. Virtual antennas are not physically present but can be virtually arranged, enabling the generation of virtual-received signals similar to those of real array antennas. Through simulation experiments, we demonstrate that our approach enables DoA estimation with the MUSIC algorithm even when its necessary conditions are not satisfied. Additionally, this paper explores the characteristics of virtual antennas in detail. We conduct simulation experiments to examine the differences in estimation accuracy between real and virtual antennas, as well as the impact of virtual antenna arrangement and count on estimation accuracy. The results show that, although virtual antennas provide lower estimation accuracy compared to real antennas, their flexible arrangement allows for improved resolution when signal sources are closely spaced by increasing the spacing between virtual antennas. Furthermore, under Additive White Gaussian Noise (AWGN) conditions, increasing the number of virtual antennas enhances estimation accuracy.

Closely Spaced Mode Identification of Skyscraper Buildings via Variational Mode Decomposition with Multiple-Signal Classification Algorithm and Recursive Hilbert Transform

Addressing the limitations of traditional modal analysis methods, which struggle with closely spaced modal frequencies and noise interference, this paper introduces a novel approach that integrates variational mode decomposition (VMD) with multiple signal classification (MUSIC) and the recursive Hilbert transform (RHT). This integration leverages the adaptability of VMD in signal decomposition and exploits the high-resolution spectral identification capabilities of MUSIC to decompose the closely spaced modes accurately. Additionally, the RHT is applied to analyze the instantaneous properties of the decomposed signals. The proposed method was validated through a numerical study on a 2DOF model, demonstrating its effectiveness and robustness in identifying modal parameters under simulated conditions. Further validation was conducted through field measurements from a 420[Formula: see text]m high skyscraper building during Super Typhoon Nesat, confirming the practical applicability and effectiveness of the approach in actual engineering cases. The findings indicate a substantial improvement in the accuracy of closely spaced modal parameter identification, thus enhancing the reliability of structural health monitoring (SHM) systems in skyscraper buildings.

Height Measurement for Meter-Wave MIMO Radar Based on Sparse Array Under Multipath Interference

For meter-wave multiple-input multiple-output (MIMO) radar, the multipath of target echoes may cause severe errors in height measurement, especially in the case of complex terrain where terrain fluctuation, ground inclination, and multiple reflection points exist. Inspired by a sparse array with greater degrees of freedom and low mutual coupling, a height measurement method based on a sparse array is proposed. First, a practical signal model of MIMO radar based on a sparse array is established. Then, the modified multiple signal classification (MUSIC) and maximum likelihood (ML) estimation algorithms based on two classical sparse arrays (coprime array and nested array) are proposed. To reduce the complexity of the algorithm, a real-valued processing algorithm for generalized MUSIC (GMUSIC) and maximum likelihood is proposed, and a reduced dimension matrix is introduced into the real-valued processing algorithm to further reduce computation complexity. Finally, sufficient simulation results are provided to illustrate the effectiveness and superiority of the proposed technique. The simulation results show that the height measurement accuracy can be efficiently improved by using our proposed technique for both simple and complex terrain.

Joint DOA and distance estimation via spatial diversity wideband signal synthesis calibrated by distance parameter

Wideband signal synthesis is a technique designed to achieve large bandwidth detection capabilities by utilizing multiple relatively narrow bandwidth signals. Traditional single-antenna, time-diversity wideband synthesis method is inefficient, which has led to using Multiple-Input Multiple-Output (MIMO) architectures to introduce spatial diversity. However, this method introduces challenges such as frequency-phase inconsistencies in the self-mixing signals from different array elements due to differences in wave travel distances. In previous research, we studied the calibration of frequency and phase for synthesized sub-signals using DOA parameters, which required additional high-precision direction of arrival (DOA) estimation algorithms. In contrast, this paper proposes a novel spatial-diversity wideband synthesis method that utilizes distance parameter calibration for joint DOA and distance estimation (JDDE). A key advantage of this method is the relative simplicity of obtaining distance parameters. To address the issue where the accuracy of distance parameters does not meet the requirements for synthesis, we proposed a grid search synthesis method in this paper. Furthermore, we introduced a search synthesis based on optimization algorithms to reduce computational load and enhance the synthesis performance. Theoretical analysis and simulations confirm the high accuracy of our JDDE method. Under conditions of high signal-to-noise ratios, our method significantly reduces the DOA's root mean square error—approximately halving it compared to the multiple signal classification algorithm within the same wideband self-mixing framework. Additionally, both distance resolution and range accuracy exceed those achieved with pre-synthesis methods.

Direction of Arrival Estimation of Meteors Echoes using Array Radio Antennas

Array antennas have an interesting role in the radio astronomy field. The array antennas allow astronomers to obtain high-resolution signals with high sensitivity to weak signals. This paper estimates the meteors' positions entering the Earth's atmosphere and develops a simulation for array antenna radar to analyze the meteor's echoes. The GNU radio software was used to process the echoes, which is a free open-source software development toolkit that provides signal processing blocks to implement in radio projects. Then, the simulation determines the azimuth and elevation of the meteors. An improved Multiple Signal Classification (MUSIC) algorithm has been suggested to analyze these echoes. The detected power of each meteor echo has a Doppler frequency shift due to the high speed of the meteors, which impacts the accuracy of the Direction of Arrival (DOA) estimation. The Doppler shift was considered in this simulation, and the results showed that the suggested method has low complexity and high resolution and can estimate the meteors' position with the minimum error.

Tactile-GAT: tactile graph attention networks for robot tactile perception classification

As one of the most important senses in human beings, touch can also help robots better perceive and adapt to complex environmental information, improving their autonomous decision-making and execution capabilities. Compared to other perception methods, tactile perception needs to handle multi-channel tactile signals simultaneously, such as pressure, bending, temperature, and humidity. However, directly transferring deep learning algorithms that work well on temporal signals to tactile signal tasks does not effectively utilize the physical spatial connectivity information of tactile sensors. In this paper, we propose a tactile perception framework based on graph attention networks, which incorporates explicit and latent relation graphs. This framework can effectively utilize the structural information between different tactile signal channels. We constructed a tactile glove and collected a dataset of pressure and bending tactile signals during grasping and holding objects, and our method achieved 89.58% accuracy in object tactile signal classification. Compared to existing time-series signal classification algorithms, our graph-based tactile perception algorithm can better utilize and learn sensor spatial information, making it more suitable for processing multi-channel tactile data. Our method can serve as a general strategy to improve a robot’s tactile perception capabilities.

TDOA-AOA Localization Algorithm for 5G Intelligent Reflecting Surfaces

5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an intelligent reflecting surface (IRS) NLOS time difference of arrival–angle of arrival (TDOA-AOA) localization (INTAL) algorithm. First, the algorithm constructs a system model for 5G IRS localization, effectively overcoming the challenges of positioning in NLOS paths. Then, by applying the multiple signal classification algorithm to estimate the time delay and angle, and using the Chan algorithm to obtain the user’s estimated coordinates, an optimization problem is formulated to minimize the distance between the estimated and actual coordinates. The tent–snake optimization algorithm is employed to solve this optimization problem, thereby reducing localization errors. Finally, simulations demonstrate that the INTAL algorithm outperforms the snake optimization (SO) algorithm and the gray wolf optimization (GWO) algorithm under the same conditions, reducing the localization error by 56% and 60% on average, respectively. Additionally, when the signal-to-noise ratio is 30 dB, the localization error of the INTAL algorithm is only 0.2968 m, while the errors for the SO and GWO algorithms are 0.6733 m and 0.7398 m, respectively. This further proves the significant improvement of the algorithm in terms of localization accuracy.

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.

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.

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.

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.

An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction

Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for detecting epilepsy, EEG is currently considered one of the most effective analytical tools due to the abundant information it provides about brain activity. The aim of this study is to explore potential time-frequency and channel features from multi-channel epileptic EEG signals and to develop a patient-specific seizure prediction network. In this paper, an epilepsy EEG signal classification algorithm called Channel Recurrent Criss-cross Attention Network (CRCANet) is proposed. Firstly, the spectrograms processed by the short-time fourier transform is input into a Convolutional Neural Network (CNN). Then, the spectrogram feature map obtained in the previous step is input into the channel attention module to establish correlations between channels. Subsequently, the feature diagram containing channel attention characteristics is input into the recurrent criss-cross attention module to enhance the information content of each pixel. Finally, two fully connected layers are used for classification. We validated the method on 13 patients in the public CHB-MIT scalp EEG dataset, achieving an average accuracy of 93.8 %, sensitivity of 94.3 %, and specificity of 93.5 %. The experimental results indicate that CRCANet can effectively capture the time-frequency and channel characteristics of EEG signals while improving training efficiency.

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.

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.

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.

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.