IET Signal Processing最新文献

The Effect of Antenna Place Codes for Reducing Sidelobes of SIAR and Frequency Diverse Array Sensors 天线位置编码对减少 SIAR 和频率多样化阵列传感器侧摆的影响

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-10-20 DOI: 10.1049/2024/9458494

Pourya Yaghoubi Aliabad, Hossein Soleimani, Mohammad Soleimani

{"title":"The Effect of Antenna Place Codes for Reducing Sidelobes of SIAR and Frequency Diverse Array Sensors","authors":"Pourya Yaghoubi Aliabad, Hossein Soleimani, Mohammad Soleimani","doi":"10.1049/2024/9458494","DOIUrl":"https://doi.org/10.1049/2024/9458494","url":null,"abstract":"<div>\u0000 <p>Synthetic impulse and aperture radar (SIAR) is a technique that frequency diverse array (FDA) radars can imply in practice, thus overcoming some of their challenges. SIAR radars, used in various fields like transportation and defense, can detect the range, azimuth angle, elevation angle, and Doppler of the target with their 4D-matched filter and a single receiver. However, the challenge of high-amplitude sidelobes is a significant concern for researchers. They have attempted to reduce it through various approaches, including frequency code, range–angle coupling, and range–Doppler coupling, to accurately identify target characteristics. This paper presents the antenna place code (AP code) parameter as a significant factor in minimizing sidelobe amplitudes. The parameter specifies that, rather than having all antennas active, a certain number of antennas are active in each pulse repetition interval (PRI) to achieve a lower sidelobe. Researchers have found that using AP codes can effectively lower the amplitude of the range–angle sidelobe, the range–Doppler sidelobe, error coupling, the repetition of sidelobe strands, and the output of angle error for different target angles. All studies are conducted on a linear array for simplicity. The output of various AP codes is compared to the previously common uniform array.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/9458494","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints 针对具有不等式约束的不确定系统的变式贝叶斯截断自适应滤波器

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-10-17 DOI: 10.1049/2024/3809689

Tianli Ma, Rong Zhang, Song Gao, Hong Li, Yang Zhang

{"title":"A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints","authors":"Tianli Ma, Rong Zhang, Song Gao, Hong Li, Yang Zhang","doi":"10.1049/2024/3809689","DOIUrl":"https://doi.org/10.1049/2024/3809689","url":null,"abstract":"<div>\u0000 <p>In this paper, a variational Bayesian (VB) truncated adaptive filter for uncertain systems with inequality constraints is proposed. By choosing the skew-<i>t</i> and inverse Wishart distributions as the prior information of the measurement noise and predicted error covariance matrix, the state vector, the predicted error covariance matrix, and noise parameters are inferred and approximated by using the VB method. To achieve the inequality-constrained estimation, the constrained state is computed by truncating the probability density function (PDF) of the estimated state after the variational update stage; the mean and covariance of the constrained state are the first and second moments of the truncated PDF. Considering the model uncertainties where the system dynamics are unpredictable, a multiple model VB truncated adaptive filter is proposed in the interacting multiple model framework. The performances of the proposed algorithms are evaluated via the target tracking simulations and the robot positioning experiments. Results show that the proposed algorithms improve estimation accuracy compared with the existing adaptive filters when the states suffer inequality constraints.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/3809689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Approach of Optimal Signal Streaming Analysis Implicated Supervised Feedforward Neural Networks 隐含监督前馈神经网络的优化信号流分析新方法

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-10-01 DOI: 10.1049/2024/2819057

Farhan Ali, He Yigang

{"title":"A Novel Approach of Optimal Signal Streaming Analysis Implicated Supervised Feedforward Neural Networks","authors":"Farhan Ali, He Yigang","doi":"10.1049/2024/2819057","DOIUrl":"https://doi.org/10.1049/2024/2819057","url":null,"abstract":"<div>\u0000 <p>The analysis and interpretation of enormous amounts of data generated by 5G networks present several challenges related to noise, precision, and feasibility validation. Therefore, this study aims to evaluate the effectiveness of channel equalisation in the network and enhance it by distributing signals over all subcarriers and symbols. The error-free signal received ensures the reliable transmission of signals in the network connection. These simulations were undertaken to fulfil the needs of and adapt the transmission properties according to the specific conditions of the channel. The dataset consists of artificially generated radio waves to train signals through neural networks (NNs) and machine learning algorithms to detect errors properly. The primary objective is to achieve optimal signal performance. In this regard, an artificial neural network (ANN) was initially employed, explicitly utilising the back-propagation technique and a feedforward multilayer perceptron (MLP). In addition, the signals were subjected to train using a real-time simulator, employing feedforward neural network and support vector machine (SVM) to validate the proposed methodology. Feedforward MLP achieved the highest performance in simulations compared to SVM. The scheme is promising to achieve optimal signal performance in real-time.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/2819057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142404436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Sharing and Performance Bounds in MIMO DFRC Systems: A Trade-Off Analysis MIMO DFRC 系统中的能量共享和性能界限：权衡分析

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-09-20 DOI: 10.1049/2024/8852387

Ziheng Zheng, Xiang Liu, Tianyao Huang, Yimin Liu, Yonina C. Eldar

引用次数: 0

A Labeled Multi-Bernoulli Filter Based on Maximum Likelihood Recursive Updating 基于最大似然递归更新的标记多贝努利过滤器

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-09-11 DOI: 10.1049/2024/1994552

Yuhan Song, Han Shen-Tu, Junhao Lin, Yizhen Wei, Yunfei Guo

引用次数: 0

Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment 针对无限方差过程环境的鲁棒分数低阶多窗 STFT

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-08-27 DOI: 10.1049/2024/7605121

Haibin Wang, Changshou Deng, Junbo Long, Youxue Zhou

{"title":"Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment","authors":"Haibin Wang, Changshou Deng, Junbo Long, Youxue Zhou","doi":"10.1049/2024/7605121","DOIUrl":"https://doi.org/10.1049/2024/7605121","url":null,"abstract":"<div>\u0000 <p>Mechanical fault vibration signal is a typical non-Gaussian process, they can be characterized by the infinite variance process, and the noise within these signals may also be the process in complex environments. The performance of the traditional cross-term reduction algorithm is compromised, sometimes yielding incorrect results under the infinite variance process environment. Several robust fractional lower order time–frequency representation methods are proposed including fractional low-order smoothed pseudo Wigner (FLOSPW), fractional low-order multi-windowed short-time Fourier transform (FLOMWSTFT), and improved fractional low-order multi-windowed short-time Fourier transform (IFLOMWSTFT) utilizing fractional low-order statistics and short-time Fourier transform (STFT) to mitigate cross-terms, enhance time–frequency resolution, and accommodate the infinite variance process environment. When compared to traditional methods, simulation results indicate that they effectively suppress the pulse noise and function effectively in lower mixed signal noise ratio (MSNR) in an infinite variance process environment. The efficacy of the proposed time–frequency algorithm is validated through its application to mechanical bearing outer ring fault vibration signals contaminated with Gaussian noise and subjected to an <i>α</i> infinite variance process.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/7605121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-Efficiency Maximization in Backscatter Communication-Based Non-Orthogonal Multiple Access System: Dinkelbach and Successive Convex Approximation Approaches 基于反向散射通信的非正交多址系统中的能效最大化：丁克巴赫法和连续凸近似法

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-08-22 DOI: 10.1049/2024/4107801

Dingjia Lin, Tianqi Wang, Kaidi Wang, Zhiguo Ding

引用次数: 0

Extended Infrared Target Filtering via Random Finite Set and Low-Rank Matrix Decomposition 通过随机有限集和低秩矩阵分解实现扩展红外目标过滤

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-08-21 DOI: 10.1049/2024/9914774

Jian Su, Haiyin Zhou, Qi Yu, Jubo Zhu, Jiying Liu

{"title":"Extended Infrared Target Filtering via Random Finite Set and Low-Rank Matrix Decomposition","authors":"Jian Su, Haiyin Zhou, Qi Yu, Jubo Zhu, Jiying Liu","doi":"10.1049/2024/9914774","DOIUrl":"https://doi.org/10.1049/2024/9914774","url":null,"abstract":"<div>\u0000 <p>Target detection in infrared remote sensing images has important practical applications. Among the current high-performance methods, the deep learning-based methods require training samples, and their generalization ability is also limited by the training set. The separation of low-rank and sparse matrix requires joint processing of multiple images with high computational complexity. The track-before-detect algorithms based on particle filtering also have high computational complexity. In this paper, the low-rank and sparse matrix of a single image are proposed for target detection, and a differentiable objective function is used in the separation. At the same time, an extended multitarget tracking algorithm based on random sets is used for target filtering between frames, and the design of the filters adopts the conjugate distribution under the Bayesian framework. Finally, the practical infrared sequence images containing multiple targets and complex backgrounds were employed to verify the performance of the proposed algorithms by comparing them with state-of-the-art algorithms.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/9914774","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Jaccard Coefficient-Based Clustering Approach with Application to Diagnosis and RUL Estimation 基于 Jaccard 系数的改进聚类方法在诊断和 RUL 估算中的应用

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-08-07 DOI: 10.1049/2024/6586622

Xiaoqing Li, Hao Tang, Hai Wang, Gangzhong Miao, Mingang Cheng

{"title":"An Improved Jaccard Coefficient-Based Clustering Approach with Application to Diagnosis and RUL Estimation","authors":"Xiaoqing Li, Hao Tang, Hai Wang, Gangzhong Miao, Mingang Cheng","doi":"10.1049/2024/6586622","DOIUrl":"https://doi.org/10.1049/2024/6586622","url":null,"abstract":"<div>\u0000 <p>Sample clustering techniques play a crucial role in the data-driven state evaluation of electromechanical equipment, and selecting an appropriate similarity measurement method for sample sets helps improve the clustering performance. The Jaccard coefficient is a commonly employed indicator of similarity for scalar set-type samples. In this paper, we propose an incremental clustering algorithm for matrix-type samples by defining an improved Jaccard coefficient. First, a new binary relation is formulated to derive a relationship matrix between samples. Second, an undirected graph is given by using the relationship matrix, and an improved pruning operation is provided to simplify the graph by eliminating redundant edges. Then, a new relationship matrix is generated according to the modified graph, which enables the calculation of the improved Jaccard coefficient. By using the improved Jaccard coefficient, the improved incremental clustering algorithm updates cluster centers by selecting a particular sample to maximize the sum of similarities between the selected sample and other samples within the same cluster. Finally, the effectiveness of the proposed incremental clustering algorithm is demonstrated in fault diagnosis and remaining useful life estimation application scenarios, respectively. The experimental results indicate that the improved algorithm outperforms traditional clustering methods.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/6586622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141967584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram 基于同相正交直方图的异步无线信号调制识别

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-08-01 DOI: 10.1049/2024/9589239

Xu Zhang, Xi Hui, Pengwu Wan, Tengfei Hui, Xiongfei Li

{"title":"Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram","authors":"Xu Zhang, Xi Hui, Pengwu Wan, Tengfei Hui, Xiongfei Li","doi":"10.1049/2024/9589239","DOIUrl":"https://doi.org/10.1049/2024/9589239","url":null,"abstract":"<div>\u0000 <p>Automatic modulation recognition is a key technology in the field of signal processing. Conventional recognition methods suffer from low recognition accuracy at low signal-to-noise ratios (SNR), and when the signal frequency is unstable or there is asynchronous sampling, the performance of conventional recognition methods will deteriorate or even fail. To address these challenges, deep learning-based modulation mode recognition technique is investigated in this paper for low-speed asynchronous sampled signals under channel conditions with varying SNR and delay. Firstly, the low-speed asynchronous sampled signals are modeled, and their in-phase quadrature components are used to generate a two-dimensional asynchronous in-phase quadrature histogram. Then, the feature parameters of this 2D image are extracted by radial basis function neural network (RBFNN) to complete the recognition of the modulation mode of the input signal. Finally, the accuracy of the method for seven modulation methods is verified by extensive simulations. The experimental results show that under the channel model of additive white Gaussian noise (AWGN), when the SNR of the input signal with low-speed asynchronous sampling is 6 dB, more than 95% of the average recognition accuracy can be achieved, and the effectiveness and robustness of the proposed scheme are verified by comparative experiments.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/9589239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141966674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0