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Improved SOCP Relaxation for SRI-Unknown Emitter Localization Using a Moving Receiver

IEEE signal processing Letters 2025年 32卷 2199-2203页

作者： Liu, Sining Yue, Yaxing Zhao, Jiayi Shi, Zhiguo Liao, Guisheng Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

Employing a single moving receiver for emitter localization offers numerous advantages, including low cost, ease of implementation, and elimination of clock synchronization. A novel localization method is proposed for a stationary emitter with an unknown signal Repetition Interval (SRI). We first construct a localization model based on time-of-arrival, taking into account potential missed detections, and then derive the Cramér-Rao Lower Bound. Given that the established model is non-convex, we reformulate the problem into a convex form and convert it to Second-order Cone Programming (SOCP) format. However, the SOCP formulation encounters convex hull issues, which may hinder far-field localization. To address this challenge, we introduce a penalty term to alleviate the convex hull problem for the first time. Simulation results demonstrate the effectiveness of the proposed approach in comparison to existing methods, and the validity of the penalty term is also confirmed. © 1994-2012 IEEE.

关键词： Location awareness Receivers signal processing algorithms Convex hulls Maximum likelihood estimation Vectors Synchronization Lower bound Iterative methods Costs

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signal Power Estimation of All Sensor Network Nodes With Measurements From a Subset of Network Nodes

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 7750-7763页

作者： Liu, Zhaoting Zhang, Huizhe Li, Chunguang Xi, Feng Wu, Duanpo Yao, Yingbiao Zhao, Yinan Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Nanjing Univ Sci & Technol Dept Elect Engn Nanjing 210094 Peoples R China

signal or noise power is an important performance parameter or indicator used for many applications in signal processing and wireless communications. This article investigates the power estimation of random signals on all sensor network nodes by using measurements taken over a subset of nodes, where the power values are assumed to be smooth on the network. This power-estimation problem is formulated as a regularized optimization problem involving the minimization of a sum of nonnegative log-likelihood and the total variation. To find its optimal solution, a primal-dual hybrid gradient (PDHG) algorithm is developed. Moreover, due to the strong demand for low hardware cost and bandwidth/power consumption in wireless communication systems, an extended algorithm, called low-resolution few-bit PDHG (LBit-PDHG), is also developed, which implements the power estimation with low-resolution few-bit quantized measurements. The LBit-PDHG algorithm could achieve competitive estimation performance when increasing the quantization resolution to 2 or 3 b while the energy consumption and implementation costs are still much lower in comparison to the high-resolution sampling implementations. Theoretical analyses prove the convergence of the proposed algorithms. Simulation studies and an application to acoustic source localization further validate the performance of the proposed algorithms.

关键词： Estimation signal processing algorithms Quantization (signal) Wireless sensor networks Wireless communication Power measurement Energy resolution Low bit quantization maximum-likelihood power estimation wireless sensor networks (WSNs)

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Ultra-Low-Complexity algorithms With Structurally Optimal Multi-Group Multicast Beamforming in Large-Scale Systems

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1626-1641页

作者： Zhang, Chong Dong, Min Liang, Ben Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 1A1 Canada Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON Canada

In this work, we propose ultra-low-complexity design solutions for multi-group multicast beamforming in large-scale systems. For the quality-of-service (QoS) problem, by utilizing the optimal multicast beamforming structure obtained recently in [2], we convert the original problem into a non-convex weight optimization problem of a lower dimension and propose two fast first-order algorithms to solve it. Both algorithms are based on successive convex approximation (SCA) and provide fast iterative updates to solve each SCA subproblem. The first algorithm uses a saddle point reformulation in the dual domain and applies the extragradient method with an adaptive step-size procedure to find the saddle point with simple closed-form updates. The second algorithm adopts the alternating direction method of multipliers (ADMM) method by converting each SCA subproblem into a favorable ADMM structure. The structure leads to simple closed-form ADMM updates, where the problem in each update block can be further decomposed into parallel subproblems of small sizes, for which closed-form solutions are obtained. We also propose efficient initialization methods to obtain favorable initial points that facilitate fast convergence. Furthermore, taking advantage of the proposed fast algorithms, for the max-min fair (MMF) problem, we propose a simple closed-form scaling scheme that directly uses the solution obtained from the QoS problem, avoiding the conventional computationally expensive method that iteratively solves the inverse QoS problem. We further develop lower and upper bounds on the performance of this scaling scheme. Simulation results show that the proposed algorithms offer near-optimal performance with substantially lower computational complexity than the state-of-the-art algorithms for large-scale systems.

关键词： Array signal processing Multicast algorithms signal processing algorithms Quality of service Interference Optimization Computational complexity Alternating direction method of multipliers extragradient algorithm large-scale optimization low complexity multicast beamforming optimal beamforming structure

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Method for Locating Single-Phase Disconnection Faults in a Smart Distribution System Based on Refined Composite Multiscale Permutation q-Complexity Entropy and Random Forest

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IEEE SENSORS JOURNAL 2023年第11期23卷 11925-11935页

作者： Zhang, Xiaowen Zhang, Shuqing Dong, Wei Song, Shanshan Wang, Kainan Yanshan Univ Inst Elect Engn Qinhuangdao 066000 Hebei Peoples R China State Grid Jibei Elect Power Co Ltd Zhangjiakou 075000 Peoples R China

In this article, a novel hybrid method of combining refined composite multiscale permutation q-complexity entropy (RCMPQE) and random forest (RF) is developed to locate disconnection faults in a power distribution system. As a feature extraction method, RCMPQE is used to analyze the negative-sequence current to obtain fault features, and then they are input into RF to build a fault location model. The effectiveness of the proposed method is corroborated by referencing a three-feeder distribution network. The results demonstrate that compared with multiscale permutation entropy (MPE), refined composite MPE (RCMPE), and generalized MPE (GMPE), the RCMPQE has stronger feature extraction ability and can abstract more prominent fault information from negative-sequence current signals. The proposed method for locating faults significantly outperforms other commonly employed machine learning models, and it is immune to various levels of noise. Furthermore, the proposed method can perform well with a high fault location accuracy in a new system.

关键词： Entropy Circuit faults Feature extraction Time series analysis signal processing algorithms Complexity theory Radio frequency Disconnection fault distribution network feature extraction random forest (RF) refined composite multiscale permutation q-complexity entropy (RCMPQE)

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Event-Triggered Approximate Byzantine Consensus With Multi-Hop Communication

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1742-1754页

作者： Yuan, Liwei Ishii, Hideaki Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Tokyo Inst Technol Dept Comp Sci Yokohama 2268502 Japan

In this article, we consider a resilient consensus problem for the multi-agent network where some of the agents are subject to Byzantine attacks and may transmit erroneous state values to their neighbors. In particular, we develop two event-triggered update schemes to tackle this problem as well as reduce the communication for each agent. Our approach is based on the mean subsequence reduced (MSR) algorithm with agents being capable to communicate with multi-hop neighbors through relaying process. Since communication delays are critical in such an environment, we provide necessary graph conditions for the proposed algorithms to perform well with delays in the communication. Moreover, a novel multi-hop relay scheme with event-triggered feature is proposed. It can reduce more transmissions than the conventional one-hop event-triggered algorithm. We also highlight that through multi-hop communication, the network connectivity can be reduced especially in comparison with the common one-hop communication case. Lastly, we show the effectiveness of the proposed algorithms by numerical examples.

关键词： signal processing algorithms Relays Spread spectrum communication Delays Robustness Resilience Protocols Resilient consensus distributed algorithms byzantine attack event-triggered updates multi-hop communication

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On Ordered Transmission Based Distributed Gaussian Shift-in-Mean Detection Under Byzantine Attacks

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 3343-3356页

作者： Quan, Chen Bulusu, Saikiran Geng, Baocheng Han, Yunghsiang S. Sriranga, Nandan Varshney, Pramod K. Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 610054 Peoples R China

The ordered transmission based (OT-based) schemes reduce the number of transmissions needed in a distributed detection network without any loss in the probability of error performance. In this paper, we investigate the performance of a conventional OT-based system in the presence of additive Byzantine attacks in Gaussian shift in mean problems. In this work, by launching additive Byzantine attacks, attackers are able to alter the order as well as the data for the binary hypothesis testing problem. We also determine the optimal attack strategy for the Byzantine sensors. Furthermore, we analyze a communication efficient OT-based (CEOT-based) scheme in the presence of additive Byzantine attacks. We obtain the probabilities of error for both the OT-based system and the CEOT-based system under attack and evaluate the number of transmissions they save. We also derive analytical bounds for the number of transmissions saved in both systems under attack. Simulation results show that the additive Byzantine attacks have significant impact on the number of transmissions saved even when the signal strength is sufficiently large. A comparison of detection performance between the conventional OT-based system and the CEOT-based system reveals that the CEOT-based system is more robust to additive Byzantine attacks.

关键词： Sensors Sensor systems Additives Wireless sensor networks Sensor phenomena and characterization signal processing algorithms Testing Ordered transmissions Byzantine attacks wireless sensor networks distributed detection

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A Unifying View on Blind Source Separation of Convolutive Mixtures Based on Independent Component Analysis

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 816-830页

作者： Brendel, Andreas Haubner, Thomas Kellermann, Walter Friedrich Alexander Univ Erlangen Nurnberg Chair Multimedia Communcat & Signal Proc D-91058 Erlangen Germany Fraunhofer Inst Integrated Circuits IIS D-91058 Erlangen Germany

In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem in audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important in this field as they require only few and weak assumptions and allow for blindness regarding the original source signals and the acoustic propagation path. Most of the currently used algorithms belong to one of the following three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON). While the relation between ICA, FD-ICA and IVA becomes apparent due to their construction, the relation to TRINICON is not well established yet. This paper fills this gap by providing an in-depth treatment of the common building blocks of these algorithms and their differences, and thus provides a common framework for all considered algorithms.

关键词： Cost function Microphones signal processing algorithms Time-frequency analysis Acoustics Convolution Probability density function Blind source separation independent component analysis convolutive mixtures indpendent vector analysis trinicon

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A Novel Approach Applied to Transient Short-Circuit Diagnosis in TIMs by Piezoelectric Sensors, PCA, and Wavelet Transform

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IEEE SENSORS JOURNAL 2023年第8期23卷 8899-8908页

作者： Lucas, Guilherme Beraldi De Castro, Bruno Albuquerque Ardila-Rey, Jorge Alfredo Glowacz, Adam Leao, Jose Vital Ferraz Andreoli, Andre Luiz Sao Paulo State Univ Dept Elect Engn BR-17033360 Bauru SP Brazil Univ Tecn Federico Santa Maria Dept Elect Engn Santiago 1680 Chile AGH Univ Sci & Technol Dept Automat Control & Robot PL-30059 Krakow Poland

Noninvasive fault diagnosis of three-phase induction motors (TIMs) is widely used in industrial applications to ensure the integrity of processes. Among different types of TIM failures, transient interturn short circuits (ITSCs) are incipient stator winding faults characterized as short circuits between two or more turns of the coils, that can lead the winding to progressive deterioration and, consequently, the TIM to total failure. In this context, this article proposes a novel approach by using piezoelectric transducers (PZTs), which performs the transient ITSC detection, phase identification, and magnitude classification by using the acoustic emission (AE) technique. To accomplish this analysis, a new statistical index based on the cross-correlation function was proposed to detect the ITSC and classify its magnitude. Besides, wavelet transform and principal component analysis (PCA) stood out as promising tools to identify which phase was affected by the short circuits. A TIM was subjected to ITSCs, and the experimental results showed that the proposed algorithm successfully performed the transient ITSC detection, phase identification, and evolution classification. In addition, this work improve the capabilities of traditional AE systems, since no AE signal processing algorithm has ever been proposed for a comprehensive diagnosis of transient ITSC.

关键词： Principal component analysis Transient analysis Indexes signal processing algorithms Sensors Discrete wavelet transforms Wavelet transforms Acoustic emission (AE) cross-correlation maximum value (CCMV) fault diagnosis piezoelectric sensors principal component analysis (PCA) wavelet transform

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High-Performance of Eigenvalue Decomposition on FPGA for the DOA Estimation

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第5期72卷 5782-5797页

作者： Zhang, Xiao-Wei Yan, Di Zuo, Lei Li, Ming Guo, Jian-Xin Xidian Univ Natl Lab Radar Signal Proc Xian 710126 Peoples R China Xijing Univ Sch Informat Engn Xian 710123 Peoples R China Changan Univ Sch Informat Engn Xian 710064 Peoples R China

For the direction of arrival (DOA) in array signal processing, eigenvalue decomposition (EVD) is one key issue in hardware implementation of the multiple signal classification (MUSIC) algorithm. Therefore, we introduce the look-ahead simplified one-sided Jacobi's method to efficiently decompose those symmetric matrices in this article and prove that the new method has the best orthogonality of eigenvector and locates eigenvectors closest to the true solution in theory. Both the numerical performance and real-time are important in engineering, so we present the novel flexible hardware architecture in single floating point arithmetic for EVD on field-programmable gate arrays (FPGAs). Finally, the simulated and raw data are used to investigate the performance of some different approaches in the context of both the EVD and MUSIC algorithm. The experimental results show that our proposed method has the best performance.

关键词： Matrix decomposition Jacobian matrices Field programmable gate arrays Eigenvalues and eigenfunctions Hardware signal processing algorithms Multiple signal classification Eigenvalue decomposition (EVD) field-programmable gate array (FPGA) MUSIC Jacobi's method matrix inversion partial sorter QR algorithm

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SHATTER: Searching Heterogeneous Combat Network Attack Sequences Through Network Embedding and Reinforcement Learning

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IEEE SYSTEMS JOURNAL 2023年第3期17卷 4497-4508页

作者： Zeng, Chengyi Liu, Hongfu Lu, Lina Chen, Jing Zhou, Zongtan Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China

A distributed combat system consists of various types of combat units interconnected through the network, which can be abstracted as a complex heterogeneous combat network (HCN). It is of great military value to study the disintegration of the operational capability of HCNs for operational mission planning and improving the survivability of operational networks. Heterogeneous networks can effectively fuse more information than homogeneous networks can. The function implementation of heterogeneous networks is also more complex. Finding the key nodes and obtaining an efficient attack sequence is our focus. Most current research on network disintegration has difficulty achieving efficiency and timeliness. In this article, we specifically propose searching heterogeneous combat network attack sequences through network embedding and reinforcement learning (SHATTER) model, which uses the induction algorithm combined with the graph neural network and reinforcement learning to solve the network disintegration sequence problem. Through experiments with synthetic heterogeneous network datasets and real heterogeneous networks, we determined that SHATTER significantly outperformed existing technologies.

关键词： Reinforcement learning Heterogeneous networks Deep learning Task analysis Approximation algorithms signal processing algorithms Training Graph neural network (GNN) heterogeneous combat network (HCN) network disintegration reinforcement learning

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