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The Proximal Operator of the Piece-Wise Exponential Function

IEEE signal processing LETTERS 2024年 31卷 894-898页

作者： Liu, Yulan Zhou, Yuyang Lin, Rongrong Guangdong Univ Technol Sch Math & Stat Guangzhou 510520 Peoples R China

This letter characterizes the proximal operator of the piece-wise exponential function $1\!-\!e{-|x|/\sigma }$ with a given shape parameter $\sigma \!>\!0$, which is a popular non-convex surrogate of the $\ell _{0}... 详细信息

This letter characterizes the proximal operator of the piece-wise exponential function $1\!-\!e<^>{-|x|/\sigma }$ with a given shape parameter $\sigma \!>\!0$, which is a popular non-convex surrogate of the $\ell _processing$-norm in support vector machines, compressed sensing, neural networks, etc. Although Malek-Mohammadi et al. [IEEE Transactions on signal processing, and 64(21):5657-5671, 2016] once worked on this problem, the expressions they derived were regrettably inaccurate. In a sense, it was lacking a case. Using the Lambert W function and an extensive study of the piece-wise exponential function, we have rectified the formulation of the proximal operator of the piece-wise exponential function in light of their work. We have also undertaken a thorough analysis of this operator. A comparative analysis of eleven sparse-promoting functions in compressed sensing demonstrates the effectiveness and efficiency of the piece-wise exponential function.

关键词： Optimization Compressed sensing signal processing algorithms Vectors Mathematical models Shape Neural networks Lambert W function piece-wise exponential penalty proximal operator

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An High Efficiency Angle and Range Estimation Method for Three-Dimensional Near-Field Sources Based on Higher-Order Cumulants

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第10期13卷 2857-2861页

作者： Deng, Zhongliang Ma, Ziyao Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

In this letter, we propose a new method to estimate the pitch angle, azimuth angle and range of 3D near-field sources. The signal reception model of the 3D near-field sources is established, the range parameter is eliminated by constructing a fourth-order non-Hermite cumulant matrix. We use the Lagrange multiplier method to reduce the two-dimensional angle search to a one-dimensional angle search problem, and use the Multiple signal Classification (MUSIC) algorithm to finely estimate the azimuth and elevation *** fourth-order cumulant matrix enriched with the range parameter is constructed and searched for the distance. Compared with other algorithms based on higher-order cumulants, this algorithm effectively reduces the dimension of the fourth-order cumulant matrix and reduces the computational complexity. Simulation results show the effectiveness of the method.

关键词： Transmission line matrix methods Estimation Azimuth Search problems Multiple signal classification Matrix decomposition signal processing algorithms High-order cumulants near-field uniform planar array MUSIC

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An Iterative Motion Compensation Algorithm for Synthetic Aperture Passive Positioning

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IEEE signal processing LETTERS 2024年 31卷 2680-2684页

作者： Zhang, Yuan Bu, Xiangxi Ge, Xuyang Xin, Jihao Liang, Xingdong Chinese Acad Sci Aerosp Informat Res Inst Natl Key Lab Microwave Imaging Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Synthetic aperture passive positioning (SAPP) has gradually become a research hotspot in the field of single-station passive positioning. Except for the acoustic band, almost all research in the microwave band only focuses on the spaceborne scenarios. In this paper, we attempt to extend SAPP to more scenarios, such as airborne and vehicle-mounted applications, and introduce the motion error problem. The difficulty is that, different from the problem of SAR motion compensation, the target area of passive positioning is unknown, and the compensation matrix cannot be calculated directly. We propose an iterative motion compensation (IMC) algorithm to solve it. Both simulation and vehicle experiment results verify the effectiveness of the IMC algorithm. The results show that IMC improves the positioning accuracy by two orders of magnitude in the presence of motion error and exhibits strong convergence.

关键词： Motion compensation signal processing algorithms Convergence Trajectory Iterative algorithms Accuracy Matched filters Passive positioning synthetic aperture iterative motion compensation

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A Radar signal Deinterleaving Method Based on Complex Network and Laplacian Graph Clustering

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IEEE signal processing LETTERS 2024年 31卷 2580-2584页

作者： Guo, Qiang Huang, Shuai Qi, Liangang Li, Daren Kaliuzhnyi, Mykola Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Kharkiv Natl Univ Radio Elect Adv Res Lab UA-61166 Kharkov Ukraine

Radar signal deinterleaving is an essential step in perceiving the battlefield situation and mastering military initiative in the information battlefield. Complex radar systems are rapidly updated and iterated, which exacerbates the possibility of "increasing batch" and "mistaken batch" during radar signal deinterleaving. In this letter, a novel method based on complex networks and Laplacian graph clustering is proposed to improve the accuracy of deinterleaving. First, a complex network is constructed to mine the spatial correlation relationships of the same radar signals. Then, based on the graph characteristics of the Laplacian matrix, the number of cluster centers is solved. Finally, this letter employs Laplacian spectral clustering based on graph segmentation to accomplish radar signal deinterleaving. The results of the experimental simulation demonstrate that the method is capable of effectively tackling the "increasing batch" and "mistaken batch" problems of radar signal deinterleaving, and could reach 99.88% deinterleaving accuracy with high robustness.

关键词： Radar Laplace equations Complex networks Clustering algorithms Correlation signal processing algorithms Eigenvalues and eigenfunctions Complex network increasing batch Laplacian graph clustering mistaken batch radar signal deinterleaving

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Collective Matrix Completion via Graph Extraction

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IEEE signal processing LETTERS 2024年 31卷 2620-2624页

作者： Zhan, Tong Mao, Xiaojun Wang, Jian Wang, Zhonglei Fudan Univ Sch Data Sci Shanghai 200437 Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Shanghai 201101 Peoples R China Xiamen Univ Wang Yanan Inst Studies Econ Xiamen 361005 Peoples R China

Collective matrix completion (CMC) offers a straightforward approach to dealing with data with entries from various sources. Benefiting from the joint structure in the collective matrix, CMC often achieves fast convergence. However, since CMC conducts matrix-level operations, it neglects the entry-wise information that can potentially be very useful for matrix completion. In this paper, to capture the entry-wise information, we propose a method called graph collective matrix completion (GCoMC). Specifically, our method integrates a graph pattern extraction module into CMC via a relational graph convolutional network. Experiments on simulated and real-world datasets show that our method significantly outperforms some existing counterparts.

关键词： Feature extraction Data mining Vectors Transforms Recommender systems Training signal processing algorithms Collective matrix completion graph extraction graph neural network recommendation system

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Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第7期13卷 1913-1917页

作者： Zhuang, Wenhao Mao, Yuyi He, Hengtao Xie, Lei Song, Shenghui Ge, Yao Ding, Zhi Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Nanyang Technol Univ Continental NTU Corp Lab Singapore 637553 Singapore Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are critical. Although graph neural network (GNN)-based data detectors can achieve decent detection accuracy at reasonable computational cost, they fail to best harness prior information of transmitted data. To further minimize the data detection error of OTFS systems, this letter develops an AMP-GNN-based detector, leveraging the approximate message passing (AMP) algorithm to iteratively improve the symbol estimates of a GNN. Given the inter-Doppler interference (IDI) symbols incur substantial computational overhead to the constructed GNN, learning-based IDI approximation is implemented to sustain low detection complexity. Simulation results demonstrate a remarkable bit error rate (BER) performance achieved by the proposed AMP-GNN-based detector compared to existing baselines. Meanwhile, the proposed IDI approximation scheme avoids a large amount of computations with negligible BER degradation.

关键词： Detectors Symbols Graph neural networks signal processing algorithms Time-frequency analysis Message passing Indexes Orthogonal time frequency space (OTFS) modulation data detection approximate message passing (AMP) graph neural network (GNN)

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RIS Array Diagnosis for mmWave Communication Systems

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IEEE signal processing LETTERS 2024年 31卷 1980-1984页

作者： Li, Liping Ying, Run Li, Yingsong He, Lei Diniz, Paulo S. R. Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Univ Fed Rio Janeiro BR-21941972 Rio De Janeiro Brazil

Reconfigurable Intelligent Surface (RIS) can obtain huge passive beamforming gains. However, due to imperfect hardware and deployment environments, RIS is subject to hardware impairments (HWI) and partial elements blockage, resulting in a significant loss of gain. Hence, RIS array diagnosis is of great significance for normal operations of the RIS system. We consider a RIS assisted millimeter-wave (mmWave) communication system where some RIS elements suffer from HWI. A conjugate gradient complex soft threshold (CG-CST) algorithm is proposed to diagnose the RIS array, reducing the overhead and increasing the diagnostic accuracy. Furthermore, the proposed CG-CST algorithm is effective in both single-input single-output (SISO) systems and systems with multiple antennas. Numerical results confirm that the presented CG-CST algorithm has superior performance compared to existing methods.

关键词： Millimeter wave communication Vectors Antenna arrays signal processing algorithms Array signal processing Transmitting antennas Iterative methods RIS array diagnosis HWI compressed sensing mmWave communication

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A New Active Sonar Detector Based on Beamformed Deep Neural Network

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IEEE JOURNAL OF OCEANIC ENGINEERING 2025年第2期50卷 1370-1386页

作者： Peng, Cong Wang, Lei Gao, Juncheng Zhang, Shuhao Ji, Haoran Hunan Univ Coll Elect Engn Natl Elect Energy Transformat & Control Engn Techn Changsha 410082 Peoples R China Hunan Normal Univ Sch Phys & Elect Changsha 410081 Peoples R China

This article proposes a new active sonar detector based on a beamformed deep neural network (BDNN) in three steps. The process involves a preprocessing step, a deep neural network (DNN) application step, and a subsequent postprocessing step. In the preprocessing step, partial spectra are extracted from multiple directions through frequency-domain beamforming. These partial spectra from different directions serve as DNN input, yielding estimated target probabilities as output in the DNN application step. In the postprocessing step, a multiframe probability multiplication technique is proposed, and the number of frames is determined adaptively. The proposed BDNN generates a gridded azimuth-distance graph, where each grid cell represents the probability of a target's presence at a specific azimuth and distance. To guarantee real-time application, we also propose a graphics processing unit based parallel acceleration method, which increases the computation speed of the beamforming process by nearly two orders of magnitude compared to the CPU. The proposed BDNN is verified through sea and lake trials. The results demonstrate that the proposed BDNN achieves better detection performance compared to the conventional matched filter method and exhibits remarkable generalization capabilities.

关键词： Sonar Feature extraction Sonar detection Array signal processing signal processing algorithms Frequency-domain analysis Detectors Noise signal processing Training Active sonar beamforming deep neural network (DNN) parallel acceleration

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Enhanced Long Baseline Underwater Target Localization With Adaptive Track-Before-Detect Method

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IEEE signal processing LETTERS 2024年 31卷 1710-1714页

作者： Jin, Tao Wang, Bo Lou, Yi Zhao, Yunjiang Qi, Bin Harbin Inst Technol Weihai Coll Informat Sci & Engn Weihai 264269 Peoples R China Yichang Testing Tech Res Inst Yichang 443003 Peoples R China Harbin Engn Univ Underwater Acoust Engn Harbin 150001 Peoples R China

In recent years, the particle filter (PF)-based track-before-detect (TBD) method has garnered attention in long baseline (LBL) localization algorithms. This approach can overcome the measurement-to-track association (MTA) challenges and complex underwater environments. In this paper, LBL underwater target localization capability is enhanced by designing the likelihood ratio function and constructing adaptive thresholds. Specifically, our contributions are as follows: First, we design the likelihood ratio function to enable automatic tracking management decisions and reduce the convergence time. Second, we construct adaptive thresholds to cope with the dynamically changing environment. Based on simulation results, the proposed algorithm outperforms the traditional localization algorithm and PF-TBD algorithm in dynamically changing environments, especially when signal-to-noise ratios are low, and has superior tracking and location capabilities.

关键词： Target tracking Radar tracking Location awareness signal processing algorithms Heuristic algorithms Convergence signal to noise ratio Particle filter (PF) track-before-detect (TBD) measurement-to-track association (MTA) long baseline (LBL) likelihood ratio function adaptive thresholds

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On Binary Sequence Design via PSL Minimization

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IEEE signal processing LETTERS 2024年 31卷 151-155页

作者： Lin, Ronghao Chen, Yutao So, Hing Cheung Li, Jian Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230026 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

We introduce an efficient gradient based algorithm to minimize the peak sidelobe level (PSL) for binary sequence designs with or without low correlation zone requirements. The proposed algorithm calculates the effective step sizes by leveraging fast Fourier transform operations and employing low-complexity updates, and its local convergence is proved. Numerical examples are provided to demonstrate that our approach can outperform the state-of-the-art algorithms in generating binary sequences with lower PSL values.

关键词： signal processing algorithms Autocorrelation Linear programming Sensors Measurement Radar Minimization Autocorrelation function binary sequence gradient based method low correlation zone peak sidelobe level minimization

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