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IEEE TRANSACTIONS ON RADAR SYSTEMS 2023年 1卷 463-467页

作者： Wu, Wenjun Tang, Bo Xiao, Jiong Huang, Ping Natl Univ Def Technol Coll Elect Engn Hefei 230037 Peoples R China

The angle estimation in the presence of interfering sources is an important problem for colocated MIMO radar. In this paper, we design the beamspace transformation matrix to adaptively filter out the interferences in the received signals by assuming that the angular sector covering the target directions is known. To tackle the nonconvex optimization problem encountered in the design, an iterative algorithm based on alternating direction method of multipliers is proposed. Compared with the existing data-adaptive beamspace design algorithm, the beampattern associated with the proposed algorithm achieves a smoother mainlobe and lower sidelobes. Moreover, the designed transformation matrix can maintain the spatial whiteness of the transformed noise, which is important for many angle estimation algorithms. Numerical examples show that the proposed algorithm achieves a higher localization accuracy in the low signal-to-noise ratio region.

关键词： MIMO radar Estimation Radar Interference signal processing algorithms Covariance matrices Training Colocated MIMO radar adaptive beamspace preprocessing robustness angle estimation

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Decentralized Dual Operator Splitting for Nonsmooth Composite Optimization

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第4期9卷 2084-2097页

作者： Li, Huaqing Ding, Wentao Wang, Zheng Lu, Qingguo Li, Yongfu Xia, Dawen Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Intelligent Air Ground Cooperat Control U Coll Automat Chongqing 400065 Peoples R China Guizhou Minzu Univ Coll Data Sci & Informat Engn Guiyang 550025 Peoples R China

Inspired by the advantages of the decentralized network structure in large-scale data processing and system robustness, this paper considers a fully distributed computation strategy for a nonsmooth composite optimization problem. The goal is to solve the problem only by resorting to local computation and local communications. Here, the local objective function is composed of two possibly nonsmooth functions where one is strongly convex and the other is proper closed convex (not necessarily strongly convex). Through associating variables with the agents and edges of the network, a decentralized synchronous algorithm is developed based on the dual operator splitting method. The asynchronous version of the proposed algorithm is further developed based on the randomized block coordinate descent technique, where each agent performs the algorithm only under its independent activation. Then the convergence analysis is provided depending on the fixed point iterations and the property of strong duality. Simulations on regularized problems demonstrate the effectiveness of the proposed decentralized algorithm.

关键词： signal processing algorithms Linear programming Cost function Convex functions Symmetric matrices signal processing Resource management Decentralized optimization multi-agent system nonsmooth problem asynchronous algorithm randomized block coordinate uncoordinated stepsize

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A Real-Time Wideband Subband LMS Algorithm for Full-Duplex Communications

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IEEE ACCESS 2022年 10卷 32566-32573页

作者： Lorenz, Kevin S. Goodman, Joel Mcbeth, Michelle Mckeon, Matthew B. Parrett, David J. US Naval Res Lab Washington DC 20375 USA

In-band full-duplex (IBFD) communications has the potential to nearly double the spectrum efficiency of existing 5G communications. Digital signal processing to mitigate self-interference is one way to realize this full-duplex capability. However, the aggregated wideband nature of 5G coupled with the potential for large delay spreads and short coherence times makes the realistic implementation of such an IBFD processor challenging. In this paper, we address the architectural considerations and describe the practical implementation of a real-time IBFD digital processor and its performance when operating on wideband data collected in a highly dynamic environment.

关键词： Clocks Field programmable gate arrays Computer architecture Real-time systems Full-duplex system signal processing algorithms Hardware Adaptive filters least mean square methods radio transceivers radio communication

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Complex Mutual Information-Based Least-Dependent Component Analysis for ISAR Imaging of Multiple Targets in a Formation Flight

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第6期58卷 5382-5392页

作者： Kim, Min Kim, Kyung-Tae Pohang Univ Sci & Technol Dept Elect Engn Pohang 37673 South Korea

This article proposes a signal decomposition-based approach for inverse synthetic aperture radar (ISAR) imaging of multiple targets in a formation. The procedure of the proposed framework is divided into three steps. The first step is to preprocess the radar echoes received by the spatial multichannel radar via pulse compression. The second step is signal decomposition using the complex-valued mutual information-based least-dependent component analysis proposed in this article. In this step, the range profiles (RPs) of multiple targets are separated into individual RPs to generate an ISAR image for each target. The third step is range-Doppler imaging using the separated RPs. As compared to the conventional methods, the proposed method decomposes the superimposed radar echoes at the raw signal level. Therefore, even if multiple targets overlap in the ISAR image, or RP histories are unaligned owing to the change in the relative positions between multiple targets, a well-focused ISAR image can still be generated. Simulation results using three targets composed of point scatterer centers verify that the proposed method can effectively segment three targets closely flying in a formation.

关键词： Radar imaging Radar Imaging Radar cross-sections signal processing algorithms Receivers Image segmentation Formation flight inverse synthetic aperture radar (ISAR) mutual information-based least component analysis (MILCA) particle swarm optimization (PSO)

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Fundamental Limits of Deep Graph Convolutional Networks for Graph Classification

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第5期68卷 3218-3233页

作者： Magner, Abram Baranwal, Mayank Hero, Alfred O., III SUNY Albany Dept Comp Sci Albany NY 12222 USA TATA Consultancy Serv Mumbai 400021 Maharashtra India Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate their capabilities and limitations for graph classification, we investigate their power to generate well-separated embedding vectors for graphs sampled from different random graph models, which correspond to different class-conditional distributions in a classification problem. It has been recognized that metric properties of learned representations are important for reduction of complexity of classifiers trained on them. Additionally, we show that inability to generate well-separated embedding vectors for two different graph models implies information-theoretic indistinguishability of these models based on noise-perturbed embedding vectors of sample graphs. We consider graph models arising from graphons, which parametrize all infinite exchangeable graph models. We precisely characterize, in terms of degree profile closeness, the set of graphon pairs that are indistinguishable (in metric and information-theoretic senses) by a GCN with depth at least logarithmic in sample graph size. Outside this set, a very simple architecture suffices for distinguishability. We then exhibit a concrete, infinite set of graphon pairs that are well-separated in cut distance and are indistinguishable by a GCN. These results theoretically match empirical observations of several prior works. Finally, we give empirical results on synthetic and real graph classification datasets, giving some indication that degree profile closeness gives rise to indistinguishability of graph distributions in real datasets, even beyond our theoretical framework.

关键词： Measurement Representation learning Task analysis Convolution Upper bound Testing signal processing algorithms Graph convolutional network representation learning graphon deep learning hypothesis testing mixing time stochastic block model

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Coded Demixing for Unsourced Random Access

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2972-2984页

作者： Ebert, Jamison R. Amalladinne, Vamsi K. Rini, Stefano Chamberland, Jean-Francois Narayanan, Krishna R. Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Qualcomm Technol Inc Qualcomm Res San Diego CA 92121 USA Natl Yang Ming Chiao Tong Dept Elect & Comp Engn Hsinchu 300 Taiwan

Unsourced random access (URA) is a recently proposed multiple access paradigm tailored to the uplink channel of machine-type communication networks. By exploiting a strong connection between URA and compressed sensing, the massive multiple access problem may be cast as a compressed sensing (CS) problem, albeit one in exceedingly large dimensions. To efficiently handle the dimensionality of the problem, coded compressed sensing (CCS) has emerged as a pragmatic signal processing tool that, when applied to URA, offers good performance at low complexity. While CCS is effective at recovering a signal that is sparse with respect to a single basis, it is unable to jointly recover signals that are sparse with respect to separate bases. In this article, the CCS framework is extended to the demixing setting, yielding a novel technique called coded demixing. A generalized framework for coded demixing is presented and a low-complexity recovery algorithm based on approximate message passing (AMP) is developed. Coded demixing is applied to heterogeneous multi-class URA networks and traditional single-class networks. Its performance is analyzed and numerical simulations are presented to highlight the benefits of coded demixing.

关键词： Receivers Decoding Compressed sensing Payloads Standards signal processing algorithms Performance evaluation Wireless communication unsourced random access coded compressed sensing convex demixing approximate message passing

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Non-Circular Sources DOA Estimation for Coprime Array With Impulsive Noise: A Novel Augmented Phased Fractional Low-Order Moment

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第10期71卷 10559-10569页

作者： Dong, Xudong Zhang, Xiaofei Zhao, Jun Sun, Meng Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China Key Lab Dynam Cognit Syst Electromagnet Spectrum Nanjing 210000 Jiangsu Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201800 Peoples R China

The coprime array has become an essential part of array signal processing, which can obtain a much higher degrees of freedom (DOFs) and larger aperture than that of the conventional approaches. However, this technique is applied in the ideal condition with white Gaussian noise. In this paper, we propose to extend the coprime array to the practical communication scenario by taking impulsive noise and non-circular signals into account. An augmented phased fractional low-order moment (A-PFLOM) is proposed to suppress impulsive noise, and then reduced-dimension MUSIC (RD-MUSIC) subspace techniques are applied for direction of arrival (DOA) information of non-circular sources. Compared to the existing methods, the proposed method has better estimation performance in a wide range of impulsive noise environments.

关键词： Estimation Direction-of-arrival estimation Phased arrays Array signal processing signal processing algorithms Sun Multiple signal classification Direction of arrival (DOA) coprime array non-circular (NC) signal phased fractional low-order moment (PFLOM) sum-difference co-array

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Design and Synchronization of Chaos-Based True Random Number Generators and Its FPGA Implementation

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IEEE ACCESS 2022年 10卷 8279-8286页

作者： Liao, T. L. Wan, P. Y. Yan, Jun-Juh Natl Cheng Kung Univ Dept Engn Sci Tainan 701 Taiwan Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41107 Taiwan

In this paper, a new chaos-based true random number generator (TRNG) and a sliding mode controller (SMC) to synchronize the proposed TRNGs are proposed. Firstly, the continuous chaotic system is transformed into a discrete system that preserves the original continuous system's chaotic behavior and makes it easy to realize with the field-programmable gate array (FPGA) for synchronization control. Then, a discrete SMC is introduced to solve the synchronization problem of the master-slave discrete chaotic systems. Subsequently, a novel hybrid function integrated with the El-Gamal algorithm is proposed to complete the TRNG design. Finally, the FPGA-based realization of the synchronized master-slave TRNGs is implemented. The randomness quality of the proposed TRNGs has been ensured by using Shannon's entropy and histogram analysis. Furthermore, the National Institute of Standards and Technology (NIST) test suite has also been introduced to evaluate the proposed TRNGs and compare with the existing results in the literature.

关键词： Synchronization Generators Field programmable gate arrays Chaotic communication Main-secondary Heuristic algorithms signal processing algorithms True random number generators chaotic systems synchronization sliding mode control field-programmable gate array

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Enhancing 3-D Land Seismic Data Using Nonlinear Beamforming Based on the Efficiency-Improved Genetic Algorithm

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第5期26卷 1192-1199页

作者： Sun, Yimin Silvestrov, Ilya Bakulin, Andrey Aramco Overseas Co Aramco Res Ctr Delft NL-2628 ZD Delft Netherlands Saudi Aramco EXPEC Adv Res Ctr Dhahran 31311 Saudi Arabia

Seismic data acquired in a desert environment often have a low signal-to-noise ratio, posing a significant challenge to seismic processing, imaging, and inversion. Nonlinear beamforming is one effective method that uses local second-order mathematical operators to describe seismic events and then enhance them. However, estimating the operator coefficients from input data is a nonlinear and compute-intensive optimization problem. We propose a new method for this estimation based on a recently developed efficiency-improved genetic algorithm. We demonstrate that it delivers an excellent balance between improved data quality and computational efficiency. We also introduce a "spatial consistency" feature to further speed up the algorithm by using available nonlinear-beamforming operators as initial trial solutions for the neighboring data ensembles. These findings are supported by applying the new approach to land seismic datasets from the desert environment.

关键词： Receivers Three-dimensional displays Genetic algorithms Array signal processing signal to noise ratio signal processing algorithms Apertures Approximation algorithm geophysical signal processing genetic algorithm (GA) seismic wave signal restoration

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Improve Radar Sensing Performance of Multiple Roadside Units Cooperation via Space Registration

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第10期71卷 10975-10990页

作者： Jiang, Wangjun Wei, Zhiqing Li, Bin Feng, Zhiyong Fang, Zixi Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

Roadside units (RSUs) can help vehicles sense the traffic environment, so as to improve traffic safety. Since the sensing capability of single RSU is limited, we propose a multiple RSUs cooperative radar sensing network (RSU-CRSN) with signal-level fusion technique. Spatial registration is an essential prerequisite and foundation for RSU-CRSN with signal-level fusion. In this paper, we present an adjustable beam enabled spatial registration algorithm (AB-SRA) that makes the sensing area of each RSU coincide by adjusting the sensing beam width of RSU. To adjust the width of sensing beam flexibly, a beamwidth adjustable beamforming algorithm (BABA) is proposed in this paper. Simulation results show that the performance of AB-SRA is close to perfect spatial registration.

关键词： Sensors signal processing algorithms Radar Synchronization Array signal processing Radar tracking Radar antennas Roadside unit signal-level fusion cooperative radar sensing spatial registration beamforming algorithm

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