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Research on UAV Jamming signal Generation Based on Intelligent Jamming

IEEE ACCESS 2025年 13卷 14686-14701页

作者： Xue, Haonan Zhuo, Zhihai Yan, Weihao Zhang, Yuexia Beijing Informat Sci & Technol Univ Dept Informat & Commun Engn Beijing 100101 Peoples R China Def Ind Secrecy Examinat & Certificat Ctr Beijing 100080 Peoples R China

With the widespread application of UAV technology, various countries are facing new threats in both military and civilian domains. To effectively counter these threats, UAV communication jamming has become a reliable countermeasure. Traditional communication jamming methods face several challenges. These include low efficiency, difficulties in managing dynamic and complex electromagnetic environments, and a heavy reliance on prior knowledge about the signals. This study presents intelligent jamming techniques for UAV signal processing to overcome the limitations found in current communication jamming technologies. The aim is to achieve precise jamming of individual target signals using intelligent jamming methods. This paper introduces an enhanced jamming signal generation algorithm built on the traditional convolutional autoencoder. Without relying on prior signal knowledge, the algorithm introduces complex convolutional networks and residual modules. The model leverages advanced convolutional networks to integrate the real and imaginary components of the signal for feature extraction, while the residual modules further refine signal processing and enhance feature learning. The model generates jamming signals by altering hidden layer features. These signals closely resemble the original communication waveforms. Simulation results show that, across different communication systems, the generated jamming signal waveforms exhibit strong similarity to the original signal waveforms. Moreover, under various signal-to-interference ratios (SIR), the jamming performance of the generated signals surpasses that of conventional jamming signals such as Gaussian noise and amplitude modulation noise.

关键词： Jamming Convolution Feature extraction Autoencoders Autonomous aerial vehicles Training Decoding Image reconstruction Deep learning signal processing algorithms Intelligent jamming communication jamming complex convolutional autoencoder jamming signal generation

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An Improved Digital signal processing for signal Enhancement and Interference Mitigation

An Improved Digital Signal Processing for Signal Enhancement...

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Automation and Computation (AUTOCOM), International Conference on

作者： Intekhab Alam Paramjit Baxi Barnali Sahoo Mohan Garg Roopashree R Thaj Mary Delsy T Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Digital signal processing has become a fundamental tool in signal processing applications, such as speech and image processing, due to its ability to manipulate and examine signals in a digital form skillfully. A primary challenge in signal processing involves the presence of unwanted noise and interference, which can negatively impact the precision and quality of the processed signal. The proposed advanced DSP approach aims to improve signal enhancement and interference mitigation techniques using sophisticated algorithms and methods. The proposed state-of-the-art DSP approach includes noise reduction algorithms, such as adaptive filtering and spectral subtraction, to eliminate unwanted noise from the signal. Additionally, cutting-edge signal processing techniques, such as wavelet transforms and time-frequency analysis, are used for signal enhancement and demising. The proposed approach employs adaptive equalization techniques to mitigate interference from other signals and substantially improve the overall signal quality. The enhanced DSP approach is evaluated through simulation and real-world experiments, demonstrating its effectiveness in improving signal quality and reducing interference considerably. The proposed approach has the potential to significantly improve the accuracy and reliability of various signal processing applications considerably, making it a valuable tool in fields such as telecommunications, audio and video processing, and medical signal analysis.

关键词： Wireless communication Wavelet transforms Time-frequency analysis Accuracy Prevention and mitigation signal processing algorithms Interference Digital signal processing Wavelet analysis Telecommunications

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Unbiased Euclidean Direction Search Algorithm for Partial Discharge Location in Cable Systems

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wang, Jie Lu, Lu Zhu, Guangya Yin, Kai-Li Zhou, Kai Chen, Badong Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Sichuan Univ Coll Elect Engn High Voltage Lab Chengdu 610065 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266520 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot IAIR Xian 710049 Peoples R China

Utilizing the unbiasedness criterion, this article proposes a bias-compensated normalized Euclidean direction search (BC-NEDS) algorithm with noisy inputs, which can effectively mitigate the impact of the bias by estimating the statistical properties of inputs. Moreover, the theoretical analysis of the BC-NEDS algorithm is conducted in a transient regime. Simulations verify the validity of the theoretical analysis and showcase the improved performance of the BC-NEDS algorithm. Partial discharge (PD) location techniques can be utilized to effectively monitor the condition of the electrical apparatus. Considering the performance of the conventional location algorithms based on the time difference of arrival (TDOA) method may notably degrade with noisy inputs. Hitherto, scarce literature concentrates on the PD location problem by leveraging adaptive filtering techniques. Such problem is essentially characterized by a noisy input model. This work aims to propose a new one-step algorithm to simultaneously denoise and achieve improved location accuracy with noisy input. The BC-NEDS algorithm is employed for solving the PD location problem. The BC-NEDS algorithm demonstrates the effectiveness in mitigating the bias arising from noisy inputs in both the direct and reflected PD signals and estimating the time difference to locate the PD signal of cable systems. Simulations and experimental studies exhibit that the BC-NEDS algorithm verifies the effectiveness and achieves enhanced location accuracy for cable systems.

关键词： Noise measurement signal processing algorithms Noise Power cables Cable insulation Accuracy Estimation Cable shielding Vectors Transient analysis Cable systems location normalized Euclidean direction search (EDS) partial discharge (PD) transient behavior

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Systematic Study on Improving the All-Weather Monitoring Accuracy of Horse Walking Rooms Using signal processing Technology

Systematic Study on Improving the All-Weather Monitoring Acc...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Zhuo Sun Xin Yan Wuhan Business University Wuhan China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This study proposes an all-weather monitoring system for horse walking rooms based on signal processing technology. The fast Fourier transform (FFT) algorithm is used to optimize the monitoring accuracy and achieve efficient and accurate real-time monitoring. The system consists of a sensor array, a signal processing module, and a data analysis module, which can monitor key parameters such as temperature, humidity, and motion status. First, the system collects the original signals of multiple sensors, suppresses noise through a filtering algorithm, and extracts effective information; then, the FFT algorithm is used to perform frequency domain analysis on the signal, which improves the efficiency and accuracy of extracting key signal features. The simulation test results show that the monitoring error of the optimized system is reduced to 0.85% under typical conditions, which is significantly better than the 1.8% of the traditional method. In high-noise and complex environments, the system signal interference reduction rate is 45%, and the robustness is improved by about 30%. The experiment verifies the monitoring accuracy and stability of the system under all-weather conditions, providing strong support for the intelligent management of horse walking rooms.

关键词： Legged locomotion Temperature sensors Accuracy Fast Fourier transforms signal processing algorithms signal processing Feature extraction Real-time systems Robustness Monitoring

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AI in Telecommunication: Revolutionizing signal processing and Communication Networks in Smart Grid

AI in Telecommunication: Revolutionizing Signal Processing a...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Shweta Dewangan Geetha Manoharan Srishti Shaifali Minz Sanjeev Kumar Mohammad Badruddoza Talukder Krishan Arora Department of Commerce ICFAI University Raipur India School of Business SR University Warangal Telangana India Department of Commerce Govt. Rajmohini Devi Girls P.G college Ambikapur School of Hotel Management and Tourism Lovely Professional University Phagwara India College of Tourism and Hospitality Management International University of Business Agriculture and Technology Dhaka Bangladesh School of Electronics and Electrical Engineering Lovely Professional University Punjab

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Artificial Intelligence (AI) is revolutionising the telecommunications industry by enhancing signal processing, network management, and overall system performance. With the rise of 5G and IoT, AI has become a critical technology driving innovation and efficiency. AI algorithms have improved error correction, data compression techniques, and bandwidth allocation, ensuring networks can handle growing data traffic volumes. AI's ability to predict, analyse, and adapt to real-time data has improved network management capabilities, allowing more intelligent resource allocation, reduced latency, and increased network reliability. AI-driven network automation has enabled providers to shift from reactive to proactive management, minimising downtime and enhancing user experience. This shift boosts operational efficiency and reduces costs by enabling predictive maintenance and optimising energy consumption in data centres and network infrastructure. The future of telecommunications will be shaped by AI technologies like machine learning, deep learning, and natural language processing, leading to advanced services like self-healing networks, real-time traffic management, and enhanced cybersecurity measures. As the telecommunication industry embraces AI-driven innovations, it will unlock new opportunities for growth and transform the everyday communication experience for billions of people worldwide.

关键词： Technological innovation signal processing algorithms signal processing Prediction algorithms Real-time systems Telecommunications Smart grids Artificial intelligence Monitoring Predictive maintenance

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A Convex Combination-Based Distributed Momentum Methods Over Directed Graphs

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IEEE signal processing LETTERS 2025年 32卷 1835-1839页

作者： Huang, Siyuan Gao, Juan Dong, Qiao-Li Zhang, Cuijie Civil Aviat Univ China Coll Sci Tianjin 300300 Peoples R China Tianjin Univ Technol Inst Operat Res & Syst Engn Tianjin 300384 Peoples R China

In this article, we introduce a convex combination-based distributed momentum method (CDM) for solving distributed optimization to minimize a sum of smooth and strongly convex local objective functions over directed graphs. The proposed method integrates the convex combination, row- and column-stochastic weights, and the adapt-then-combination rule. By selecting different parameters, it can be reduced to other distributed momentum methods, such as the parametric distributed momentum. CDM converges to the optimal solution at a global R-linear rate for any smooth and strongly convex function when the step-size and momentum coefficient satisfy some bounded conditions. Numerical results for some distributed optimization problems demonstrate that CDM yields a performance that is superior to that of the state-of-the-art methods.

关键词： Convergence Vectors Optimization Directed graphs signal processing algorithms Linear programming Iterative methods Convex functions Visualization Training Convergence rate convex combination directed graphs distributed optimization machine learning

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Enhancing Underwater DOA Estimation Accuracy With Limited Datasets Using Task-Restructured Deep Mutual Learning

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IEEE signal processing LETTERS 2025年 32卷 966-970页

作者： Zhang, Qinzheng Wang, Haiyan Shen, Xiaohong Yan, Yongsheng Zhao, Zhongda Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Ocean Acoust & Sensing Minist Ind & Informat Technol Xian 710072 Peoples R China Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Peoples R China

This paper aims to address the issue of low accuracy in underwater Direction of Arrival (DOA) estimation using Deep Learning (DL) methods, which arises due to the scarcity of underwater data caused by the difficulties in conducting underwater experiments. For multi-snapshot sampled signals, we segment the snapshots and reconstruct the task into a problem of processing few-snapshot data within an expanded dataset. By utilizing the new task, we employ a deep mutual learning (DML) model to enhance the accuracy of the original task's DOA estimates. Experimental results demonstrate that under conditions of small and limited datasets, our approach effectively improves the accuracy of DL-based DOA estimation methods.

关键词： Direction-of-arrival estimation Estimation Accuracy Training signal processing algorithms Hands Fitting Deep learning Underwater acoustics Feature extraction Enhancing underwater DOA estimation DML limited datasets task restructuring

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A signal processing Method for Vortex Flowmeter Based on Multi-Channel Parallel Filtering

A Signal Processing Method for Vortex Flowmeter Based on Mul...

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Sensors and Information Technology (ICSI), International Conference on

作者： Jie Chen Shuxu Wan Bin Li Department School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Vortex flowmeters occupy an important position in the field of flow measurement. Their advantages include having no moving mechanical parts, the ability to adapt to various media, and low-pressure loss. After the vortex signal detected by the piezoelectric element is amplified and filtered through a series of analog and digital circuits, an accurate flow value can be obtained. To enhance the dynamic response performance of the vortex flowmeter, a multi-channel parallel signal processing method with 1/f 2 amplitude-frequency characteristic modulation has been designed. This method employs filtering units with a 1/f 2 attenuation characteristic (-40 dB/dec) combined with the physical relationship of the vortex signal's amplitude-frequency characteristics to expand the measurement range. The measurement channels are divided into several parallel channels, and a fast channel selection method is designed to effectively address the slow dynamic response speed of traditional vortex flowmeter signal processing methods. The results show that the vortex flowmeter integrated with the four-channel algorithm achieves 0.5-grade accuracy and exhibits superior dynamic response performance compared to Yokogawa flowmeters.

关键词： Dynamic response Accuracy Filtering Heuristic algorithms signal processing algorithms signal processing Media Flowmeters Attenuation Velocity measurement

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Source Number Estimation in 2D Uniform Circular and L-Shaped Arrays With Unknown Nonuniform Noise

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第1期72卷 348-352页

作者： He, Mengxia Chan, S. C. Univ Hong Kong Dept Elect & Elect Engn Hong Kong SAR Peoples R China

Classical source number estimators are usually derived under the assumption of uniform white noise, which may degrade substantially with unknown nonuniform sensor noise. Advanced estimators designed for nonuniform noise are however computationally expensive with limited performance in unfavorable conditions, such as low signal-to-noise ratio, small number of snapshots, close angular separations and sources with different transmitter powers. This brief proposes a new likelihood ratio statistics-based method for source number estimation under uncorrelated nonuniform noise. Using the asymptotic theory, it is shown that the likelihood ratio follows a chi-square distribution. Hence, the number of sources can be estimated via a sequence of hypothesis tests using maximum likelihood estimators (MLEs) of the covariance matrix with different assumed source numbers. The low complexity subspace algorithm is proposed to obtain the ML estimates. Theoretical analysis demonstrates that the proposed estimator is consistent in the general asymptotic regime. Simulation results on 2D arrays such as uniform circular and L-shaped arrays show that the proposed estimator achieves a higher correct detection probability in unfavorable conditions and is more robust against nonuniformity of noise than state-of-the-art estimators.

关键词： Noise Covariance matrices signal processing algorithms Maximum likelihood estimation Sensor arrays Vectors Optimization Transmitters signal to noise ratio Light rail systems Array signal processing source number estimation nonuniform noise likelihood ratio test

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Diffusion Generalized Minimum Total Error Entropy Algorithm

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IEEE signal processing LETTERS 2025年 32卷 751-755页

作者： Cai, Peng Lin, Dongyuan Qian, Junhui Zheng, Yunfei Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China

Both the minimum error entropy (MEE) and mixture MEE (MMEE) are extensively employed in distributed adaptive filters, exhibiting their robustness against non-Gaussian noise by capturing high-order statistical information from network data. However, the fixed shape of the Gaussian kernel function existing in MEE and MMEE restricts their flexibility, leading to reduced robustness and deteriorated performance. To address this issue, a novel diffusion generalized minimum total error entropy (DGMTE) algorithm is first proposed in this letter, using a generalized MEE criterion to significantly improve the performance of error-in-variables models-based algorithms under non-Gaussian noise. Moreover, as a special case of DGMTE, a generalized minimum total error entropy (GMTE) algorithm is also proposed, and the local convergence analysis of DGMTE is given. Finally, simulations show the superiorities of DGMTE in comparison with other representative algorithms.

关键词： Noise signal processing algorithms Entropy Shape Adaptation models Kernel Estimation Distributed algorithms Cost function Convergence Adaptive filter distributed estimation errors-in-variables model generalized minimum error entropy total least squares

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