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Complex-Valued Neural Network for Estimating the Number of Sources in Radar Systems

IEEE SENSORS JOURNAL 2025年第1期25卷 1746-1755页

作者： Cho, Seonmin Jeong, Taewon Kwak, Seungheon Lee, Seongwook Chung Ang Univ Coll ICT Engn Sch Elect & Elect Engn Seoul 06974 South Korea Korea Aerosp Univ Coll Engn Sch Elect & Informat Engn Goyang 10540 Gyeonggi Do South Korea

This article proposes a method for estimating the number of sources using the time-domain received signal in frequency-modulated continuous-wave radar systems. Conventional approaches typically estimate the number of sources in the frequency domain via the fast Fourier transform (FFT) on the received signals. However, these conventional methods face challenges in distinguishing targets that have identical distances and velocities, resulting in an underestimation of the source count on the range-Doppler (RD) map derived by applying the FFT to the received signal. Consequently, this underestimation can lead to inaccurate results in subspace-based direction-of-arrival (DOA) estimation algorithms. In contrast, our method directly uses the received signal in the time domain, which can distinguish between targets that have identical distance and velocity. We introduce a complex-valued neural network that estimates the number of sources using signal vectors extracted from analog-to-digital converter (ADC) samples of a received signal matrix. The proposed method achieved an average accuracy of 91% in various simulations and experiments, and its effectiveness was verified through comparison with conventional source estimation algorithms. In addition, we can also obtain accurate results in the subspace-based DOA estimation algorithms using the number of sources estimated by the proposed method.

关键词： Estimation Radar Direction-of-arrival estimation Neural networks Fast Fourier transforms Receiving antennas Sensors multiple signal classification Radar antennas Chirp Complex-valued neural network direction-of-arrival (DOA) radar system source estimation

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DOA Estimation for Underwater Acoustic Array in Impulsive Noise Based on Adaptive Kernel Width Mixture Correntropy

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Dai, Zehua Wu, Jinqiu Yin, Jingwei Zhang, Wenbo Qi, Xiaofei Qiao, Gang Peng Cheng Lab Dept Strateg & Adv Interdisciplinary Res Shenzhen 518055 Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin 150001 Peoples R China

This article investigates a direction-of-arrival (DOA) estimation method for underwater acoustic arrays in non-Gaussian impulsive noise environments. Traditional DOA estimation methods for underwater acoustic arrays typically presume that underwater environmental noise follows a Gaussian distribution. This assumption can lead to a significant degradation in estimation accuracy, or even failure, in underwater environments where non-Gaussian impulsive noise is predominant, thereby limiting the detection capabilities of underwater acoustic arrays. To address this issue, this study employs a method based on mixture correntropy, which maximizes the mixture correntropy of the residual fitting error matrix for subspace decomposition of the received data matrix, effectively filtering out impulsive noise. Considering the signal processing performance of correntropy and mixture correntropy depends on the selection of the kernel width, this article introduces a novel adaptive method for updating the kernel width. This method updates the kernel width in each iteration based on the residual fitting error value, setting the square of the kernel width to the sum of the squares of a preset kernel width and the residual fitting error modulus. This approach retains the simplicity and robustness of the maximum mixture correntropy criterion (MMCC) algorithm while enhancing the convergence rate and achieving a lower steady-state excess mean square error. Furthermore, this study applies the classical multiple signal classification (MUSIC) algorithm for DOA estimation. Finally, simulations and sea trials have substantiated the correctness and effectiveness of the method proposed in this article.

关键词： Noise Estimation Direction-of-arrival estimation Kernel Underwater acoustics Filtering signal processing algorithms multiple signal classification Acoustic arrays Array signal processing Adaptive kernel width direction-of-arrival (DOA) estimation mixture correntropy non-Gaussian impulsive noise underwater acoustic arrays

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A Sparse Method for Joint Range and Angle Estimation in OFDM SonarCom Systems With Phase Errors

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4357-4368页

作者： Wu, Min Hao, Chengpeng Wang, Lihui Wu, Yongqing Orlando, Danilo Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

Joint sonar-communication (SonarCom) systems offer interesting and promising perspectives for target detection in military or civilian applications. Compared to the traditional sonar systems, orthogonal frequency division multiplexing (OFDM) SonarCom system suffers from frequency offset. The fluctuation due to array uncertainty and acoustic propagation characteristics lead to random phase errors, which heavily impair the estimation accuracy. To deal with this drawback, we present a compressed sensing-based range-angle estimation algorithm when phase errors are present for OFDM SonarCom systems. In order to overcome multitarget interference, cyclic prefix (CP)-based OFDM technique is applied. Exploiting the structure of the CP-OFDM signal model, the redundant dictionary and minimization problem are defined. The coupling information of sparse data are considered as a global metric to minimize the effect of phase errors and recover super-resolution range-angle estimation. Regardless the presence of random phase errors and strong noise, the approach returns high-quality estimates of range and angle. Moreover, a low complexity solution algorithm is developed to improve the computational complexity and memory requirements. The performance assessment underlines that the proposed approach can be a viable means to solve the joint range and angle estimation problem.

关键词： OFDM Estimation Sonar Phased arrays Symbols Vectors Sensors Optimization multiple signal classification Error compensation Compressed sensing (CS) joint sensing and communication orthogonal frequency division multiplexing (OFDM) phase error compensation range-angle estimation

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Three-Dimensional Localization of Mixed Near-Field and Far-Field Sources Based on a Unified Exact Propagation Model

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IEEE TRANSACTIONS ON signal PROCESSING 2025年 73卷 245-258页

作者： Fang, Jiaxiong Chen, Hua Liu, Wei Yang, Songjie Yuen, Chau So, Hing Cheung Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 610054 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

In applications like speaker localization using a microphone array, the collected signals are typically a mixture of far-field (FF) and near-field (NF) sources. To find the positions of both NF and FF sources, a three-dimensional spatial-temporal localization algorithm based on a unified exact propagation geometry is developed in this paper, which avoids approximating the spatial phase difference with the first-order and second-order Taylor expansions applied to FF and NF sources, respectively. Our scheme utilizes cross-correlation to produce virtual observations for establishing a third-order parallel factor data model with the use of spatial and temporal information. The array's steering vectors can be extracted by trilinear decomposition. The amplitude and phase information of the whole array elements is jointly exploited to classify the source types and obtain the location estimates via a least squares method. Moreover, the proposed algorithm is computationally efficient since no spectral searches, high-order statistics calculations or parameter pairing procedures are required. The deterministic Cram & eacute;r-Rao bound is also derived as a performance benchmark, and numerical results are provided to demonstrate the effectiveness of the developed method.

关键词： Noise measurement Location awareness classification algorithms Geometry Three-dimensional displays signal processing algorithms Phased arrays multiple signal classification Apertures Microphone arrays Array signal processing source localization spatial-temporal near-field far-field

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

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JOURNAL OF VIBRATION AND CONTROL 2025年第3-4期31卷 208-220页

作者： Zheng, Fei Yuan, Shenfang Liu, Yuan Xu, Qiuhui Nanjing Univ Aeronaut & Astronaut Res Ctr Struct Hlth Monitoring & Prognosis State Key Lab Mech & Control Aerosp Struct Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Res Ctr Struct Hlth Monitoring & Prognosis State Key Lab Mech & Control Aerosp Struct 29 Yudao St Nanjing 210016 Peoples R China

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.

关键词： multiple signal classification damage localization self-correcting phase error compensation high efficiency composite structure

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A CNN-Based Approach for Classical Music Recognition and Style Emotion classification

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IEEE ACCESS 2025年 13卷 20647-20666页

作者： Shi, Yawen Henan Univ Sci & Technol Sch Art & Design Luoyang 471000 Henan Peoples R China

Music recognition refers to the process of automatically recognizing and classifying the musical content in audio signals using computer technology and algorithms. Music recognition technology can help people recognize information such as the music title, artist, musical style, rhythm, and the emotions conveyed by the music in the audio, thus enabling applications like automated music information retrieval and recommendation systems. Classical music, due to its vast quantity, diverse types, and time span covering several centuries, presents challenges that existing music recognition software and traditional music recognition algorithms cannot effectively address. In this study, The model based on convolutional neural networks (CNNs) is proposed, allowing people to recognize the classical music title, style, and emotions contained in a piece of music. The proposed model is particularly beneficial for individuals who are interested in classical music but lack extensive knowledge about it, as it provides essential information about the pieces. By extracting multidimensional features from classical music, the model can recognize the title, style, and emotions expressed. To improve the model's recognition accuracy, various noises are introduced to the dataset. Meanwhile, in this study, a novel loss function has been devised to more effectively assess the model's performance. For searching for optimal performance of the model, a novel optimization algorithm also be proposed to find optimal hyperparameters of loss function. The experiment results show average title recognition accuracy is 0.98, average style recognition accuracy is 0.89 and average emotion recognition accuracy is 0.93. These results adequately demonstrate that the proposal model significantly enhances the model's ability to accurately recognize the titles, styles, and emotions of classical music, achieving high recognition rates even in noisy environments.

关键词： Emotion recognition Accuracy Feature extraction multiple signal classification classification algorithms Noise Deep learning Recommender systems Proposals Heuristic algorithms CNN deep learning music recognition music retrieval optimization algorithm

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Direction of Arrival Estimation Based on AR Modeling in the Presence of Colored Noise

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IEEE ACCESS 2025年 13卷 62427-62433页

作者： Bakhshandeh Babarsad, Sina Mahmoudi, Alimorad Namjoo, Ehsan Shahid Chamran Univ Ahvaz Fac Engn Dept Elect Engn Ahvaz *** Iran

This paper investigates the problem of estimating the direction of arrival considering colored noise at the sensors. To this aim, we model the recieved signal at each time instant with an AR system with rank equal to the number of sources and we simultaneously estimated signal and noise parameters by solving a quadratic eigenvalue problem. Simulation results demonstrate that utilizing root mean square error as the accuracy metric, the proposed method outperforms existing subspace-based approaches, particularly when the targets are close to eachother.

关键词： Noise Direction-of-arrival estimation Sensors Colored noise Maximum likelihood estimation Transforms signal processing algorithms multiple signal classification Eigenvalues and eigenfunctions White noise AR modeling colored noise DOA estimation subspace-based methods

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Mc-music algorithm for fast DOA estimation

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WIRELESS NETWORKS 2025年第1期31卷 659-667页

作者： Liu, Fulai Li, Xubin Su, Zhibo Liu, Yubiao Zhang, Aiyi Northeastern Univ Qinhuangdao Lab Cognit Radio & Big Spectrum Data Proc Hebei Key Lab Marine Percept Network & Data Proc Qinhuangdao 066004 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

This paper presents a fast and compressible multiple signal classification algorithm (C-MUSIC) based on the manifold separation technique (MST) for a uniform circular array (UCA), named as MC-MUSIC. Making use of the MST, the steering vector (SV) of the UCA can be converted into a new SV, which has a Vandermonde structure similar to a uniform linear array. Then a noise-like subspace cluster (NLSC) can be generated by applying a new linear relation between the true direction-of-arrival (DOA) and virtual DOA to the new given SV. Finally, the angular field-of-view can be compressed to a small sector by the intersection of the NLSC so that the calculation times of the spatial spectrum can be reduced effectively. Simulations demonstrate the presented method can maintain the high precision of the DOA estimation with low computational complexity.

关键词： Uniform circular array Manifold separation technique Direction-of-arrival multiple signal classification

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SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4962-4976页

作者： Shmuel, Dor Haim Merkofer, Julian P. Revach, Guy van Sloun, Ruud J. G. Shlezinger, Nir Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland

Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of direction of arrival (DoA) estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as multiple signal classification (MUSIC) and Root-MUSIC, rely on several restrictive assumptions, including narrowband non-coherent sources and fully calibrated arrays, and their performance is considerably degraded when these do not hold. In this work we propose SubspaceNet;a data-driven DoA estimator which learns how to divide the observations into distinguishable subspaces. This is achieved by utilizing a dedicated deep neural network to learn the empirical autocorrelation of the input, by training it as part of the Root-MUSIC method, leveraging the inherent differentiability of this specific DoA estimator, while removing the need to provide a ground-truth decomposable autocorrelation matrix. Once trained, the resulting SubspaceNet serves as a universal surrogate covariance estimator that can be applied in combination with any subspace-based DoA estimation method, allowing its successful application in challenging setups. SubspaceNet is shown to enable various DoA estimation algorithms to cope with coherent sources, wideband signals, low SNR, array mismatches, and limited snapshots, while preserving the interpretability and the suitability of classic subspace methods.

关键词： Direction-of-arrival estimation Estimation Covariance matrices multiple signal classification Vectors Narrowband Eigenvalues and eigenfunctions Computational modeling Arrays signal to noise ratio Deep learning DoA estimation subspace methods

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Robust Direction-of-Arrival Estimation With Outliers and Partly Calibrated Uniform Linear Array

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4825-4834页

作者： Xiao, Wudang Li, Yingsong Yu, Canping Zhao, Luyu Pan, Mengguan de Lamare, Rodrigo C. Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Pontif Catholic Univ Rio De Janeiro PUC Rio Ctr Telecommun Res CETUC BR-22451900 Gavea Brazil Univ York Dept Elect Engn York YO10 5DD England

This article presents a three-step direction-of-arrival estimation method aimed at addressing impulsive noise and array gain-phase errors. Initially, a robustness function combining the logarithmic function with least squares is introduced to address impulsive noise. This robust function is utilized to formulate a low-rank matrix approximation problem for acquiring noise-free received data, which is solved using the alternating-direction method of multipliers. Subsequently, exploiting the obtained noise-free received data, the array gain-phase errors are estimated using the root-rank reduction estimator. Finally, based on the acquired noise-free received data and gain-phase errors, a minimization problem incorporating the logarithmic function is formulated. To efficiently solve this minimization problem, it is transformed into a weighted l(2,1)-norm minimization problem, and the fast-iterative-shrinkage-thresholding algorithm is employed. Compared to existing methods, the proposed approach demonstrates superior estimation accuracy and precision while achieving an attractive tradeoff between performance and computational complexity.

关键词： Direction-of-arrival estimation Estimation Vectors signal processing algorithms Sensor arrays multiple signal classification Minimization Manifolds Gaussian noise Fitting Direction-of-arrival (DOA) estimation gain-phase error log-sum minimization outliers partly calibrated array sparse signal recovery

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