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Theoretical Insights and Practical algorithms for Transceiver Design of PMCW Radar

IEEE TRANSACTIONS ON signal processing 2024年 72卷 3091-3103页

作者： Lin, Ronghao Liu, Zhuoli Chen, Yutao So, Hing Cheung Li, Jian Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230026 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

Phase-modulated continuous-wave (PMCW) radar can fundamentally solve the growing mutual interference problems suffered by the widely used linear frequency-modulated continuous-wave automotive radar. To achieve this objective, long binary periodic probing sequences, characterized by low auto-correlation sidelobes, arbitrary period lengths, and substantial diversity, should be transmitted by the PMCW radar. To further decrease the sidelobe levels, at the slight cost of the signal-to-noise ratio (SNR), mismatched receive filters are viable options for PMCW radar. We focus on designing periodic binary probing sequences and mismatched receiver filter to achieve almost zero cross-correlation sidelobes levels at small SNR loss. We also introduce an algorithm to optimize mismatched receive filters subject to the constraint of a pre-determined maximum allowable SNR loss. We prove that if the cross-correlation sidelobes of a binary sequence can be completely eliminated by a mismatched filter at a small SNR loss, then it will also have low auto-correlation sidelobes. However, the reverse is not necessarily true. We investigate the properties of the widely used m-sequences under the mismatched receive filter framework as a specific example. Numerical examples are provided to validate our theoretical analysis and showcase the effectiveness of the proposed transceiver design algorithms for PMCW radar.

关键词： signal to noise ratio Radar Transceivers signal processing algorithms Correlation Automotive engineering Costs Binary sequence transceiver design low correlation zone m-sequence phase-modulated continuous-wave radar zoned integrated sidelobe level

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Supervised Feature Selection via Multi-Center and Local Structure Learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第9期36卷 4930-4942页

作者： Zhang, Canyu Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

Feature selection has achieved unprecedented success in obtaining sparse discriminative features. However, the existing methods almost use the & ell;(2,p)-norm constraint on transformation matrix to obtain sparse features, which introduces extra parameters and cannot obtain the features directly. In addition, existing algorithms only focused on the global structure and ignored the local structure, leading to poor performance when solving data with non-Gaussian distributions which a single center point cannot describe precisely. Based on above considerations, we propose a supervised feature selection via multi-center and local structure learning. We further introduce trace ratio criterion into our model in favor of improving the discriminant of features selected. In order to address the overlap problem, we use multiple center points to match the distribution of data and construct a k-Nearest Neighbor graph to explore the local structure of the data. In addition, we also propose an efficient method to optimize the transformation matrix with the & ell;(2,0)-norm constraint and can directly obtain the sparse features. We evaluate our method on Toy datasets and several real-world datasets, show improvement over state-of-the-art feature selection methods, and demonstrate the effectiveness of our model in dealing with non-Gaussian distributed data problems.

关键词： Feature extraction Sparse matrices Optical filters Information filters signal processing algorithms Reviews Filtering algorithms Supervised feature selection, sparse discriminant features multi-center local structure & ell (2,0 )-norm constraint

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Cubic Polishing Spline-Based algorithms for Industrial Image processing

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第1期20卷 314-326页

作者： Sun, Fangli Cai, Zhanchuan Macau Univ Sci & Technol Sch Comp Sci & Engn Macau 999078 Peoples R China

Industrial image processing is a major technology in the fourth industrial revolution. In recent years, the spline method has gained more and more attention in image processing. To explore an efficient industrial image processing algorithm, this article proposes novel cubic polishing spline algorithms. The cubic polishing spline is a high precision spline algorithm that brings more degrees of freedom by adding nodes to better balance the local and global aspects. First, the direct and indirect cubic polishing spline transforms are used for the image interpolation filter. Then, we construct the least squares cubic polishing spline and pyramid approximation method with application to the image reconstruction and image scaling (image upscaling or downscaling). Next, the frequency domain cubic polishing spline lowpass filter and highpass filter are proposed, and the proposed schemes are applied to image smoothing and image sharpening, respectively. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) and root mean square error (RMSE) evaluation metrics are used to illustrate the reconstruction effects, and experiments show that the algorithms proposed in this article can achieve better reconstruction results with better fidelity and competitiveness compared with other schemes.

关键词： Splines (mathematics) Image reconstruction Image processing Interpolation signal processing algorithms Approximation algorithms Filtering algorithms Approximation method image reconstruction image scaling interpolation polishing spline

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AoI-Oriented Resource Allocation for NOMA-Based Wireless Powered Cognitive Radio Networks Based on Multi-Agent Deep Reinforcement Learning

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IEEE ACCESS 2024年 12卷 69738-69752页

作者： He, Tao Peng, Yingsheng Liu, Yong Song, Hui South China Normal Univ Sch Elect & Informat Engn Foshan 528225 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 528406 Peoples R China

In this paper, we study a wireless powered cognitive internet of things (IoT) network, where cognitive radio (CR) and non-orthogonal multiple access (NOMA) technologies are exploited to improve spectral efficiency, and radio frequency based energy harvesting (RF-EH) technology is integrated to achieve the sustainable IoT network. To ensure the freshness of information delivery, we investigate the age of information (AoI) as a performance metric, and formulate a long-term average AoI minimization problem under energy sustainability constraint, in which the working mode and transmit power of the secondary devices (SDs) are jointly optimized. Then, we reformulate it as a decentralized Markov decision process (Dec-MDP) with continuous action space. Accordingly, a deep reinforcement learning (DRL) framework is exploited, and a multi-agent twin delayed deep deterministic policy gradient algorithm with dual action selection mechanism (MATD3-DAS) is proposed, which adopts the centralized training and decentralized execution (CTDE) framework and exploits both actor and critic networks to select actions for improving exploration ability. Simulation results show that the proposed algorithm can significantly reduce the long-term average AoI, where the decrements approach 9.58% and 52.34% compared with the MATD3 algorithm and TD3-DAS algorithm with centralized training and centralized execution (CTCE).

关键词： Resource management Internet of Things NOMA Throughput Heuristic algorithms signal processing algorithms Simulation Cognitive radio Energy harvesting Information age Deep reinforcement learning Multi-agent systems non-orthogonal multiple access radio frequency based energy harvesting age of information multi-agent deep reinforcement learning

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Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives

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IEEE ACCESS 2024年 12卷 119015-119038页

作者： Essaid, Billel Kheddar, Hamza Batel, Noureddine Chowdhury, Muhammad E. H. Lakas, Abderrahmane Univ Medea LSEA Lab Dept Elect Engn Medea 26000 Algeria Qatar Univ Dept Elect Engn Doha Qatar United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates

Automatic speech recognition (ASR) plays a pivotal role in our daily lives, offering utility not only for interacting with machines but also for facilitating communication for individuals with partial or profound hearing impairments. The process involves receiving the speech signal in analog form, followed by various signal processing algorithms to make it compatible with devices of limited capacities, such as cochlear implants (CIs). Unfortunately, these implants, equipped with a finite number of electrodes, often result in speech distortion during synthesis. Despite efforts by researchers to enhance received speech quality using various state-of-the-art signal processing techniques, challenges persist, especially in scenarios involving multiple sources of speech, environmental noise, and other adverse conditions. The advent of new artificial intelligence (AI) methods has ushered in cutting-edge strategies to address the limitations and difficulties associated with traditional signal processing techniques dedicated to CIs. This review aims to comprehensively cover advancements in CI-based ASR and speech enhancement, among other related aspects. The primary objective is to provide a thorough overview of metrics and datasets, exploring the capabilities of AI algorithms in this biomedical field, and summarizing and commenting on the best results obtained. Additionally, the review will delve into potential applications and suggest future directions to bridge existing research gaps in this domain.

关键词： Machine learning Auditory system Speech enhancement Speech recognition Medical services Cochlear implants signal processing algorithms Automatic speech recognition Deep learning Deafness cochlear implant deep learning profound hearing loss speech enhancement machine learning

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Experimental Validation of Single BS 5G mmWave Positioning and Mapping for Intelligent Transport

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第11期73卷 16744-16757页

作者： Ge, Yu Khosravi, Hedieh Jiang, Fan Chen, Hui Lindberg, Simon Hammarberg, Peter Kim, Hyowon Brunnegard, Oliver Eriksson, Olof Olsson, Bengt-Erik Tufvesson, Fredrik Svensson, Lennart Wymeersch, Henk Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Peng Cheng Lab PCL Shenzhen 518066 Peoples R China Qamcom S-41285 Gothenburg Sweden Ericsson Res S-16483 Gothenburg Sweden Chungnam Natl Univ Dept Elect Engn Daejeon 34134 South Korea Magna S-44737 Vargarda Sweden

Positioning with 5G signals generally requires connection to several base stations (BSs), which makes positioning more demanding in terms of infrastructure than communications. To address this issue, there have been several theoretical studies on single BS positioning, leveraging high-resolution angle and delay estimation and multipath exploitation possibilities at mmWave frequencies. This paper presents the first realistic experimental validation of such studies, involving a commercial 5G mmWave BS with a customized beam sweep procedure and a user equipment (UE) development kit mounted on a test vehicle. We present the relevant signal models, and signal processing methods, and validate these based on data collected in an outdoor science park environment. Our results indicate that positioning is possible, but the performance is limited by the knowledge of the position and orientation of the infrastructure and the multipath visibility and diversity.

关键词： 5G mobile communication Millimeter wave communication signal processing algorithms Receivers Downlink Channel estimation Accuracy Experimental validation 5G mmWave single BS positioning and mapping intelligent transportation

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ROCS: Robust One-Bit Compressed Sensing With Application to Direction of Arrival

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2407-2420页

作者： Li, Xiao-Peng Shi, Zhang-Lei Huang, Lei So, Anthony Man-Cho So, Hing Cheung Shenzhen Univ State Key Lab Radio Frequency Heterogeneous Integr Shenzhen 518060 Peoples R China China Univ Petr East China Coll Sci Qingdao 266580 Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

One-bit compressed sensing (1-bit CS) inherits the merits of traditional CS and further reduces the cost and burden on the hardware device via employing the 1-bit analog-to-digital converter. When the measurements do not involve sign flips caused by additive noise, most contemporary algorithms can attain excellent signal restoration. However, their recovery performance might significantly degrade if there is even a small portion of sign flips. In order to increase the estimation accuracy in noisy scenarios, we devise a new signal model for 1-bit CS to attain robustness against sign flips. Then, we give a double-sparsity optimization formulation of the restoration problem. Subsequently, we combine proximal alternating minimization and projected gradient descent to tackle the problem. Different from existing robust methodologies, our approach, referred to as robust one-bit CS (ROCS), does not require the number of sign flips. Furthermore, we analyze the convergence behavior of ROCS and show that the objective value and variable sequences converge. Numerical results using synthetic data demonstrate that ROCS is superior to the competing methods in terms of reconstruction error in noisy environments. ROCS is also applied to direction-of-arrival estimation and outperforms state-of-the-art approaches.

关键词： Noise Quantization (signal) Noise measurement Vectors signal processing algorithms Minimization Estimation Robust algorithm one-bit compressed sensing direction-of-arrival estimation l(0)-norm optimization

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GASF-ConvNeXt-TF Algorithm for Perimeter Security Disturbance Identification Based on Distributed Optical Fiber Sensing System

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IEEE INTERNET OF THINGS JOURNAL 2024年第10期11卷 17712-17726页

作者： Wang, Ya-Jun Zhuo, Wen Liu, Bin Liu, Juan Hu, Yingying Fu, Yue Xiao, Wenbo He, Xing-Dao Yuan, Jinhui Wu, Qiang Nanchang Hangkong Univ Key Lab Optoelect Informat Sci & Technol Jiangxi P Nanchang 330063 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Northumbria Univ Fac Engn & Environm Newcastle Upon Tyne NE1 8ST England

Phase-sensitive optical time-domain reflectometer technology can transform the fiber-optic cable into a large-scale sensor array for distributed acoustic sensing (DAS), which is an emerging infrastructure for the Internet of Things. However, it is limitated in event recognition capability, which is a major factor preventing its practical field application. This article proposes a perturbation recognition algorithm based on GASF-ConvNeXt-TF with fast process and high recognition accuracy. First, Gramian angular summation field (GASF) algorithm is used to encode external disturbance signal to transform the 1-D time-series signal into a more concentrated 2-D image feature. Then the convolutional neural network model ConvNeXt_tiny network is applied as the classifier. In order to prevent the weight gradient from oscillating back and forth during network training process, a cosine annealing algorithm is introduced to control the decay of the learning rate. Meanwhile, transfer learning is used to further optimize the network model, resulting in higher classification accuracy and faster convergence. Finally, two different experimental scenarios are arranged in a total length of 2.2 km of optical fiber cable, and six different disturbance events (shaking, kicking, knocking, trampling, wheel rolling, and impacting) are set. Different from previous perimeter security disturbance identification experiments, not only single-point disturbance recognition is performed but also two points disturbances are simultaneously recognized, and all have good overall identification accuracy. The overall recognition accuracy of the six disturbance events in single and multiple points experiments are 99.3% and 98.3%, respectively, with an average recognition time of 0.103 s. The proposed technique has potential application in infrastructures structure health monitoring, such as factories, airports, energy pipeline, and highway.

关键词： Sensors Feature extraction Optical fibers Internet of Things Classification algorithms signal processing algorithms Perturbation methods Convolutional neural network (CNN) distributed acoustic sensing (DAS) pattern recognition transfer learning (TL)

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Active and Passive Beamforming Designs for SER Minimization in RIS-Assisted MIMO Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 18838-18854页

作者： Chien, Trinh Van Duc, Bui Trong Luong, Ho Viet Duc Binh, Huynh Thi Thanh Ngo, Hien Quoc Chatzinotas, Symeon Hanoi Univ Sci & Technol HUST Sch Informat & Commun Technol SoICT Hanoi 100000 Vietnam Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN North Ireland Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg Luxembourg

This research exploits the applications of reconfigurable intelligent surface (RIS)-assisted multiple input multiple output (MIMO) systems, specifically addressing the enhancement of communication reliability with modulated signals. Specifically, we first derive the analytical downlink symbol error rate (SER) of each user as a multivariate function of both the phase-shift and beamforming vectors. The analytical SER enables us to obtain insights into the synergistic dynamics between the RIS and MIMO communication. We then introduce a novel average SER minimization problem subject to the practical constraints of the transmitted power budget and phase shift coefficients, which is NP-hard. By incorporating the differential evolution (DE) algorithm as a pivotal tool for optimizing the intricate active and passive beamforming variables in RIS-assisted communication systems, the non-convexity of the considered SER optimization problem can be effectively handled. Furthermore, an efficient local search is incorporated into the DE algorithm to overcome the local optimum, and hence offer low SER and high communication reliability. Monte Carlo simulations validate the analytical results and the proposed optimization framework, indicating that the joint active and passive beamforming design is superior to the other benchmarks.

关键词： Array signal processing Optimization Symbols MIMO communication Vectors Reconfigurable intelligent surfaces Minimization Downlink Wireless communication signal processing algorithms RIS-assisted MIMO beamforming design communication reliability differential evolution SER minimization

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A Delta Operator State Estimation Algorithm for Discrete-Time Systems With State Time-Delay

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IEEE signal processing LETTERS 2025年 32卷 391-395页

作者： Xu, Ling Zhang, Xiao Ding, Feng Zhu, Quanmin Changzhou Univ Sch Microelect & Control Engn Changzhou 213159 Peoples R China Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY England

This letter presents a state estimation algorithm for linear discrete-time systems with state-delay. In order to overcome the difficulty that the traditional Kalman filter cannot estimate the states of the systems with state-delay, a state estimation strategy is developed by combining the auxiliary model with the delta operator. Then, by constructing and minimizing the covariance matrix of the state reconstruction errors, a delta operator state estimation algorithm is derived and it can fulfill effective state estimation for the linear discrete-time system with state-delay. Moreover, the convergence proof is provided by means of stochastic stability theory. Finally, the experimental results demonstrate that the developed state estimation method is effective.

关键词： State estimation Vectors signal processing algorithms Mathematical models Delays Covariance matrices Convergence Stochastic processes Nonlinear systems Kalman filters Kalmam filtering state-delay state estimation signal processing

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