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Backward and Sequential Inductions for Antijamming Equilibrium Strategy Generation of Frequency Agile Radar

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 4963-4978页

作者： Wang, Chao Jiu, Bo Pu, Wenqiang Li, Kang Zhao, Yu Liu, Hongwei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

Mainlobe jamming has emerged as a serious threat to the radar and the carrier frequency agility of the radar provides a promising way to address this challenge. In this article, the electromagnetic confrontation between a smart mainlobe jammer and a frequency agile radar is investigated from a game theory perspective. The interaction process between them typically includes multiple pulses, in which the radar determines its carrier frequency sequence, and the jammer synchronously chooses the jamming mode. This bilateral process is modeled as a two-player zero-sum extensive-form game with simultaneous moves. To solve the game, the backward induction (BI) algorithm is introduced to find the subgame perfect Nash equilibrium (SPNE). Although the BI algorithm can exactly find the SPNE, its computational cost grows exponentially with the number of pulses, making it impractical. Therefore, a fast algorithm called the sequential induction (SI) algorithm is proposed to obtain an approximation NE, whose computational complexity is linear with respect to the number of pulses. In addition, the obtained radar's SEQ strategy with history-independent and reuse characteristics is reliable and efficient in a game with a changing number of stages. Simulation results demonstrate the effectiveness of the proposed SI algorithm for approximately finding the NE.

关键词： Radar Jamming Games Radar signal processing Approximation algorithms signal processing algorithms Passive radar Backward induction (BI) extensive-form game frequency agile radar mainlobe jamming sequential induction (SI)

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Development of an Image Encryption Algorithm Based on Compressed Sensing and Chaotic Mapping

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IEEE MULTIMEDIA 2024年第4期31卷 49-59页

作者： Yan, Shaohui Cui, Yu Li, Lin Zhang, Yuyan Jiang, Defeng Zhang, Hanbing Northwest Normal Univ Sch Phys & Elect Engn Lanzhou 710000 Peoples R China Northwest Normal Univ Elect Informat Lanzhou 710000 Peoples R China

A new 2D hyperchaotic mapping is designed based on 2D discrete mapping, featuring two positive Lyapunov exponents, enhanced randomness, and initial offset boosting. Subsequently, an image encryption algorithm is developed, grounded in chaotic systems, compressed sensing theory, and scrambling-diffusion operations. Validation through a histogram, correlation coefficient, and information entropy operations proves the algorithm's effective image encryption. Robustness is verified through plaintext and differential attacks, and metrics such as peak signal-to-noise ratio and structural similarity index affirm the algorithm's strong restorative capability.

关键词： Encryption Chaotic communication Bifurcation Compressed sensing Vectors Boosting signal processing algorithms

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A Radar Compound Jamming Recognition Method Based on Blind Source Separation

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第6期60卷 9073-9084页

作者： Zhou, Hongping Wang, Lei Guo, Zhongyi Hefei Univ Technol Sch Comp & Informat Hefei 230009 Peoples R China

In modern times, radar detection faces the additively compounded jamming signals emitted by multiple jammers. Due to the unknown number, type, and parameters of individual jamming signals, previous jamming recognition algorithms cannot identify all compound cases. During network model training, it becomes necessary to predefine the type of compound jamming signals, and it also limits the number of labels for samples, from which only specific compound cases can be recognized. In this article, a recognition strategy named "Separation + Recovery + Recognition" is proposed to identify all the jamming cases of additively compounded jamming effectively. First, the number of signal sources of the received signals from multiple channels is analyzed. Next, the separated single jamming signal is obtained by source separation method from the received additively compounded signals. And then the radar signal recovery network is used to compensate and recover signals that are incompletely separated due to the time-frequency overlap. Finally, the separated and recovered single jamming signals are put into the designed convolutional neural network model for recognition. The simulation results show that the proposed algorithm demonstrates superior performances in recognition and generalization. When the jamming-to-noise ratio is -10 dB, the recognition accuracy of the compound case of five jamming signals can still reach 90% plus.

关键词： Jamming Radar Compounds Feature extraction signal processing algorithms Time-frequency analysis Radar imaging Blind source separation compound jamming jamming recognition neural network application

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Secure and Controllable Secret Key Generation Through CSI Obfuscation Matrix Encapsulation

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 12313-12329页

作者： Du, Yicong Liu, Hongbo Shao, Ziyu Ren, Yanzhi Li, Shuai Dai, Huan Yu, Jiadi Univ Elect Sci & Technol China Chengdu 610056 Peoples R China Univ Oulu Oulu 90570 Finland Suzhou Univ Sci & Technol Suzhou 215011 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

Physical-layer key generation has emerged as a promising avenue for establishing secret keys using reciprocal channel measurements between wireless devices. However, channel reciprocity may suffer degradation from ambient noise and cause mismatched secret bits, while existing methods mitigating this issue may yet face limitations in key efficiency. The root cause behind such limitations is the heavy reliance on channel measurements, which can be naturally susceptible to channel non-reciprocity attributed to environmental factors. Instead of direct key extraction from channel measurements, we seek to share a pre-defined key and utilize channel measurements as a bearer to facilitate key transmission. We propose an accurate and efficient key generation method (KeyCome) to ensure secure key sharing by encapsulating it with channel state information (CSI) obfuscation matrices through circulant convolution. To this end, we develop a reliable key derivation through a quadratic programming method with matrix equilibration, ensuring stable and rapid solutions. Notably, the transmitter can control the key beforehand for enhanced communication efficiency and combine it with an error correction mechanism for accurate key derivation. Furthermore, a lightweight reconciliation scheme is designed to minimize mismatched bits caused by occasional non-reciprocity. Comprehensive experiments demonstrate KeyCome's high accuracy and efficiency in key generation.

关键词： Quantization (signal) Wireless communication Communication system security Noise Noise measurement Wireless sensor networks signal processing algorithms Physical-layer key generation quadratic program error correction

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Cooperative Multi-Cell Massive Access With Temporally Correlated Activity

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第5期23卷 4347-4363页

作者： Zhu, Weifeng Tao, Meixia Yuan, Xiaojun Xu, Fan Guan, Yunfeng Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Shanghai 200240 Peoples R China Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Peng Cheng Lab Shenzhen 518000 Guangdong Peoples R China

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.

关键词： signal processing algorithms Channel estimation Heuristic algorithms Correlation Performance evaluation Message passing Internet of Things Multi-cell massive access temporally correlated activity cooperative activity detection channel estimation generalized approximate message passing (GAMP)

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Simultaneous Multi-Beam Pattern Synthesis of Subarray via Alternating Projection

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IEEE ACCESS 2024年 12卷 66352-66362页

作者： Jing, Zhongtian Han, Yubing Feng, Yuchen Xiao, Lei Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Jiangsu Peoples R China

For a large-scale array, the subarray technology can save the radio frequency (RF) channels. However, the use of subarray technology often tends to reduce the beamforming performance of the array. In some specific scenarios, it is necessary to use simultaneous multi-beam technology for target search. Exploring methods to simultaneously achieve multi-beamforming while reducing radio frequency channels is a worthwhile research endeavor. This paper introduces an innovative application of hybrid beamforming structure to the realm of beam synthesis and proposes an algorithm for simultaneous multi-beam pattern synthesis in subarrays. This method weights the received signal of the array twice. Initially, the analog signal received by each antenna unit undergoes the first weighted, accomplished by incorporating a phase shifter after each antenna. After the initial weighted, the analog signals proceed into their respective subarray channels, where they undergo a second step of weighted after being converted into digital signals. The second step of weighted involves digital beamforming of the subarray signals, utilizing multiple sets of weights, to achieve simultaneous multibeam. The algorithm can obtain an element weighted vector and a subarray weighted matrix. Based on the alternating projection (AP) algorithm, the property of the Kronecker product, and the definition of the Khatri-Rao product, the optimal element weighted vector and subarray weighted matrix can be obtained by solving the 2-norm minimization problem.

关键词： Vectors Array signal processing Phase shifters Adaptive arrays signal processing algorithms Radio frequency Subarray synthesis algorithm multiple simultaneous patterns alternating projection

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Distributed Proximal Alternating Direction Method of Multipliers for Constrained Composite Optimization Over Directed Networks

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2024年 10卷 539-551页

作者： Yan, Jing Shi, Xinli Guo, Luyao Wan, Ying Wen, Guanghui Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China

In this article, we investigate a constrained composition optimization problem in a directed communication network. Each agent is equipped with a local objective function composed of both smooth and nonsmooth terms, as well as linear equality constraints. The optimization objective is to minimize the sum of all local functions, subject to linear equality constraints, through local computations and information exchange with neighboring agents. Based on the alternating direction method of multipliers (ADMM), a novel distributed optimization algorithm is proposed to address the composite optimization problem. We leverage the composite structure of the objective function, by introducing a linear approximation for the smooth term and a proximal mapping for the nonsmooth term, which simplifies the process of solving the ADMM subproblem. Furthermore, in contrast to the existing algorithms that eliminate the imbalance resulting from directed graphs using a column-stochastic matrix, the proposed algorithm only employs a row-stochastic matrix, thereby avoiding the need for agents to know their outdegree. Moreover, the step sizes of agents are uncoordinated and can be independent of the network topology. Furthermore, we prove that the proposed algorithm achieves a sublinear convergence rate when the local objective functions are convex. Finally, the effectiveness of the proposed algorithm is verified through numerical simulations.

关键词： Optimization Convergence signal processing algorithms Convex functions Linear programming Vectors Distributed composition optimization constrained optimization alternating direction method of multipliers (ADMM) directed graphs finite-time consensus

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Hypformer: A Fast Hypothesis-Driven Rescoring Speech Recognition Framework

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IEEE signal processing LETTERS 2025年 32卷 471-475页

作者： Zhuang, Xuyi Qian, Yukun Wang, Mingjiang Harbin Inst Technol Shenzhen Shenzhen 518000 Peoples R China

Recently, the performance of non-autoregressive ASR models has made significant progress but still lags behind hybrid CTC/attention systems. This paper introduces Hypformer, a fast hypothesis-driven rescoring speech recognition framework. Multiple hypothetical prefixes are realized by fast prefix generation algorithm. With two different rescoring methods, nar-ar rescoring and nar$<^>{2}$ rescoring, Hypformer can flexibly switch between autoregressive and non-autoregressive decoding modes to perform rescoring of hypothesis prefixes. Experiments on the standard Mandarin datasets AISHELL-1 and AISHELL-2 demonstrate that Hypformer outperforms the state-of-the-art Hybrid CTC/Attention systems in ASR performance while achieving a speedup of over six times. Experiments on the Mandarin sub-dialect dataset KeSpeech indicate that Hypformer achieves more accurate recognition by leveraging richer contextual information.

关键词： Decoding Training Iterative decoding Transformers Accuracy Standards Symbols Switches signal processing algorithms Probability distribution ASR E2E non-autoregressive hypothesis prefixes rescoring

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A Hybrid Federated Learning Architecture With Online Learning and Model Compression

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IEEE ACCESS 2024年 12卷 191046-191058页

作者： Odeyomi, Olusola T. Ajibuwa, Opeyemi Roy, Kaushik North Carolina Agr & Tech State Univ Dept Comp Sci Greensboro NC 27411 USA

Federated learning allows distributed devices to jointly train a global model without breaching privacy. Conventional federated learning uses the cross-device setting or the cross-silo setting. However, this work focuses on hybrid architecture that combines the cross-device and cross-silo settings. The hybrid architecture overcomes congestion at the central server in cross-device settings. Also, it overcomes convergence instability in cross-silo settings. Furthermore, this work proposes two online federated learning algorithms that can work well for real-time applications, unlike many existing federated learning algorithms. The first algorithm named Online Federated Learning with Compression (OFedCom) is designed for the full information settings where the time-varying loss function is observable and the loss gradients can be computed. The second algorithm named Online Federated Learning with Compression and Bandit Feedback (OFedCom-B) is designed for the bandit setting where the time-varying loss function is not observable and the loss gradient cannot be computed. Two compression techniques are incorporated into the proposed algorithms to overcome communication bottlenecks while guaranteeing good convergence. Separate regret analyses are designed for both convex and non-convex time-varying loss functions. The simulation results show faster convergence and better regret bound than existing algorithms.

关键词： Federated learning Quantization (signal) Servers Computer architecture signal processing algorithms Accuracy Computational modeling Data models Convergence Training Bandit compression federated learning graph theory online learning

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l0 Penalized Maximum Likelihood Estimation of Sparse Covariance Matrices

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IEEE signal processing LETTERS 2025年 32卷 66-70页

作者： Fatima, Ghania Stoica, Petre Babu, Prabhu Indian Inst Technol Delhi CARE New Delhi 110016 India Uppsala Univ Dept Informat Technol S-75105 Uppsala Sweden

In this letter we present a framework for estimating sparse covariance matrices, wherein we solve the l(0)-norm penalized maximum likelihood estimation problem using the extended Bayesian information criterion (EBIC), a high dimensional model selection rule. The framework combines choosing the sparsity pattern and estimating the covariance matrix in a single step, eliminating the need for any hyper-parameter tuning. Using the framework we propose a cyclic majorization-minimization based technique and apply it to synthetic data to evaluate its performance in terms of normalized root mean square error (NRMSE) and Kullback Leibler (KL) divergence.

关键词： Covariance matrices Maximum likelihood estimation Sparse matrices Minimization Tuning signal processing algorithms Optimization Bayes methods Wireless communication Taylor series Covariance matrix estimation EBIC l(0) penalized maximum likelihood

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