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An Incremental Quantities Distance Protection Using Park's Transformation

IEEE TRANSACTIONS ON POWER DELIVERY 2022年第4期37卷 3200-3212页

作者： Chen, Yu Wen, Minghao Wang, Zhen Yin, Xianggen Chen, Deshu Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China

In this article, an incremental quantities distance protection using Park's transformation is proposed to increase the operation speed of distance protection. Based on the instantaneous voltage in abc frame approximate linear distribution along the AC transmission lines, this article proves that instantaneous voltage in dq frame still satisfies the approximate linear distribution along the AC transmission line. Then, the d-axis voltage components of fault point and setting point are used to determine the internal and external faults. In order to ensure that the presented distance protection can be applied to all fault types, a virtual three-phase system construction method is also presented. The fault point voltage construction method and low-pass filter design method are also discussed. Finally, a typical AC transmission system is developed in PSCAD/EMTDC to verify the performance of the proposed distance protection under different fault conditions. Simulation results indicate that the proposed incremental quantities distance protection has fast operation speed under internal metallic fault conditions and high security under external fault conditions.

关键词： Voltage Power transmission lines Low-pass filters signal processing algorithms Estimation Time-domain analysis Hardware Distance protection instantaneous value incremental quantities Park's transformation

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An Asymptotically MSE-Optimal Estimator Based on Gaussian Mixture Models

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4109-4123页

作者： Koller, Michael Fesl, Benedikt Turan, Nurettin Utschick, Wolfgang Tech Univ Munich Methoden Signalverarbeitung D-80333 Munich Germany

This paper investigates a channel estimator based on Gaussian mixture models (GMMs) in the context of linear inverse problems with additive Gaussian noise. We fit a GMM to given channel samples to obtain an analytic probability density function (PDF) which approximates the true channel PDF. Then, a conditional mean estimator (CME) corresponding to this approximating PDF is computed in closed form and used as an approximation of the optimal CME based on the true channel PDF. This optimal CME cannot be calculated analytically because the true channel PDF is generally unknown. We present mild conditions which allow us to prove the convergence of the GMM-based CME to the optimal CME as the number of GMM components is increased. Additionally, we investigate the estimator's computational complexity and present simplifications based on common model-based insights. Further, we study the estimator's behavior in numerical experiments including multiple-input multiple-output (MIMO) and wideband systems.

关键词： Channel estimation Probability density function Covariance matrices Statistics Numerical simulation Gaussian mixture model signal processing algorithms Asymptotic convergence conditional mean channel estimation Gaussian mixture models machine learning spatial channel model

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Multi-Source DoA Estimation of EM Waves Impinging Spherical Antenna Array With Unknown Mutual Coupling Using Relative signal Pressure Based Multiple signal Classification Approach

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IEEE ACCESS 2022年 10卷 103793-103805页

作者： Famoriji, Oluwole John Shongwe, Thokozani Univ Johannesburg Dept Elect & Elect Engn Technol ZA-2006 Johannesburg South Africa

Spherical antenna array (SAA) is a configuration that scans almost all the radiation sphere with constant directivity. It finds applications in spacecraft and satellite communication. Multiple signal classification (MUSIC) is a widely used multiple source direction-of-arrival (DoA) estimation method because of its low complexity implementation in practical applications. Conversely, it is susceptible to noise, which consequently affects its accuracy of localization. In this paper, MUSIC-based methods that operate at low signal-to-noise ratio (SNR) are developed via relative electromagnetic (EM) wave pressure measurements of a SAA. The proposed methods are the relative pressure MUSIC (RP-MUSIC), and in spherical domain (SH-RP-MUSIC). The developed SH-RP-MUSIC algorithm is in spherical domain thereby allows frequency-smoothing approach for the de-correlation of the coherent source signals towards an enhanced accuracy of localization. Both RP-MUSIC and SH-RP-MUSIC algorithms developed have the ability to estimate the number of active sources that is a priori knowledge of the conventional MUSIC algorithm. Numerical experiments were used to demonstrate the adequacy of the developed algorithms. In addition, measured data from experiment, which is the practically acceptable way to examine any procedure is employed to demonstrate the merits of the developed algorithms against the conventional MUSIC algorithm and other recent multiple source localization method in literature. Finally, in order to achieve DoA estimations with adequate localization accuracy at low SNR using SAA, SH-RP-MUSIC algorithm is a better choice.

关键词： Antenna arrays Location awareness Multiple signal classification Estimation Direction-of-arrival estimation signal processing algorithms signal to noise ratio DoA estimation SAA MUSIC multiple source localization relative signal pressure estimation of number of source

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Automatic Detection and Tracking of Random Frequency signals Using Magnitude and Phase Information

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IEEE ACCESS 2022年 10卷 67373-67382页

作者： Carevic, Dragana Def Sci & Technol Grp Maritime Div HMAS Stirling Rockingham WA 6168 Australia

This paper presents an approach to the problems of detection and frequency estimation of a frequency modulated narrow-band signal in additive complex Gaussian noise. The signal is assumed to have an unknown amplitude, initial phase and frequency trajectory over time, while a priori information regarding random frequency variability is taken to be available. The proposed approach operates in the frequency domain and uses the magnitude and phase of the discrete-time Fourier transforms computed over nonoverlapping signal segments. Robustness at low signal-to-noise ratios is achieved by suppressing the segment-related likelihoods for which the phase estimation error is large. The approach utilises unthresholded transform data and thus works in a track-before-detect manner, and the frequency trajectory is estimated by applying a search over the frequency-time bins. The results of a simulation study involving two signal types with different frequency variability are described.

关键词： Time-frequency analysis Frequency estimation Hidden Markov models Radar tracking Phase frequency detectors Estimation signal to noise ratio Detection algorithms frequency estimation Markov processes maximum a posteriori estimation phase frequency detectors phase estimation signal detection signal processing algorithms state estimation Viterbi algorithm

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Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2022年第4期8卷 1802-1815页

作者： Xu, Wangyang An, Jiancheng Xu, Yongjun Huang, Chongwen Gan, Lu Yuen, Chau Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China Singapore Univ Technol & Design Engn Prod Dev Pillar Singapore 487372 Singapore Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China Zhejiang Singapore Innovat & AI Joint Res Lab Hangzhou 310027 Zhejiang Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China UESTC Yibin Inst Yibin 643000 Sichuan Peoples R China

To mitigate the effects of shadow fading and obstacle blocking, reconfigurable intelligent surface (RIS) has become a promising technology to improve the signal transmission quality of wireless communications by controlling the reconfigurable passive elements with less hardware cost and lower power consumption. However, accurate, low-latency and low-pilot-overhead channel state information (CSI) acquisition remains a considerable challenge in RIS-assisted systems due to the large number of RIS passive elements. In this paper, we propose a three-stage joint channel decomposition and prediction framework to acquire CSI. The proposed framework exploits the two-timescale property that the base station (BS)-RIS channel is quasi-static and the RIS-user equipment (UE) channel is fast time-varying. Specifically, in the first stage, we use the full-duplex technique to estimate the channel between a BS's specific antenna and the RIS, addressing the critical scaling ambiguity problem in the channel decomposition. We then design a novel deep neural network, namely, the sparse-connected long short-term memory (SCLSTM), and propose a SCLSTM-based algorithm in the second and third stages, respectively. The algorithm can simultaneously decompose the BS-RIS channel and RIS-UE channel from the cascaded channel and capture the temporal relationship of the RIS-UE channel for prediction. Simulation results show that our proposed framework has lower pilot overhead than the traditional channel estimation algorithms, and the proposed SCLSTM-based algorithm can also achieve more accurate CSI acquisition robustly and effectively.

关键词： Channel estimation Prediction algorithms Wireless communication Coherence time Predictive models signal processing algorithms Deep learning Reconfigurable intelligent surface joint channel decomposition and prediction deep learning pilot overhead

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Efficient algorithms for Constant-Modulus Analog Beamforming

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 756-771页

作者： Arora, Aakash Tsinos, Christos G. Shankar, M. R. Bhavani Chatzinotas, Symeon Ottersten, Bjorn Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg Luxembourg

The use of a large-scale antenna array (LSAA) has become an important characteristic of multi-antenna communication systems to achieve beamforming gains such as in designing millimeter-wave (mmWave) systems to combat severe propagation losses. In such applications, each antenna element has to be driven by a radio frequency (RF) chain for the implementation of fully-digital beamformers, significantly increasing the hardware cost, complexity, and power consumption. Therefore, constant-modulus analog beamforming (CMAB) becomes a viable solution. In this paper, we consider the scaled analog beamforming (SAB) or constant-modulus analog beamforming (CMAB) architecture and design the system parameters by solving two variants of beampattern matching problem. In the first case, both the magnitude and phase of the beampattern are matched to the given desired beampattern whereas in the second case, only the magnitude of the beampattern is matched. Both the beampattern matching problems are cast as a variant of the constant-modulus least-squares (CLS) problem. We provide efficient algorithms based on the alternating majorization-minimization (AMM) framework that combines the alternating minimization and the MM frameworks and the conventional-cyclic coordinate descent (C-CCD) algorithms to solve the problem in each case. We also propose algorithms based on a new modified-CCD (M-CCD) based approach. For all the developed algorithms we prove convergence to a Karush-Kuhn-Tucker (KKT) point (or a stationary point). Numerical results demonstrate that the proposed algorithms converge faster than the state-of-the-art solutions. Among all the algorithms, the M-CCD-based algorithms have faster convergence when evaluated in terms of the number of iterations and the AMM-based algorithms offer lower complexity.

关键词： Array signal processing signal processing algorithms Optimization Antenna arrays Convergence Charge coupled devices Minimization Analog beamforming majorization-minimization MM alternating MM AMM cyclic coordinate descent CCD large-scale antenna arrays unit-modulus constraints nonconvex optimization block cyclic coordinate descent BCCD

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Federated Matrix Factorization: Algorithm Design and Application to Data Clustering

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 1625-1640页

作者： Wang, Shuai Chang, Tsung-Hui Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks. Although many FL algorithms have been proposed, few of them have considered the matrix factorization (MF) model, which is known to have a vast number of signal processing and machine learning applications. Since the MF problem involves two blocks of variables and the variables are usually subject to constraints related to specific solution structure, it requires new FL algorithm designs to achieve communication-efficient MF in heterogeneous data networks. In this paper, we address the challenge by proposing two new federated MF (FedMF) algorithms, namely, FedMAvg and FedMGS, based on the model averaging and gradient sharing principles, respectively. Both FedMAvg and FedMGS adopt multiple steps of local updates per communication round to speed up convergence, and allow only a randomly sampled subset of clients to communicate with the server for reducing the communication cost. Convergence properties for the two algorithms are thoroughly analyzed, which delineate the impacts of heterogeneous data distribution, local update number, and partial client communication on the algorithm performance, and guide the design of proposed algorithms. By focusing on a data clustering task, extensive experiment results are presented to examine the practical performance of proposed algorithms, as well as demonstrating their efficacy over the existing distributed clustering algorithms.

关键词： Clustering algorithms signal processing algorithms Servers Distributed databases Convergence Partitioning algorithms Data models Federated learning matrix factorization model averaging gradient sharing clustering

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Privacy-Preserving in-Bed Human Pose Estimation: Highlights from the IEEE Video and Image processing Cup 2021 Student Competition

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IEEE signal processing MAGAZINE 2022年第3期39卷 121-129页

作者： Liu, Shuangjun Huang, Xiaofei Marcenaro, Lucio Ostadabbas, Sarah Northeastern Univ Dept Elect & Comp Engn Augmented Cognit Lab Boston MA 02115 USA Univ Genoa Telecommun I-16126 Genoa Italy Northeastern Univ Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Augmented Cognit Lab Boston MA 02115 USA

Every person spends around one third of his/her life in bed. For an infant or a young toddler, this percentage can be much higher, and for bed-bound patients it can go up to 100% of their time. In-bed pose estimation is a critical step in many human behavior monitoring systems that are focused on prevention, prediction, and management of at-rest or sleep-related conditions in both adults and children. The topic of automatic noncontact human pose estimation has received a lot of attention/success, especially in the last few years in the computer vision community, thanks to the introduction of deep learning and its power in artificial intelligence (AI) modeling. However, the state-of-the-art (SOTA) vision-based AI algorithms in this field can hardly work under the challenges associated with in-bed human behavior monitoring, such as significant illumination changes (e.g., full darkness at night), heavy occlusion (e.g., covering by a sheet or blanket), as well as the privacy concerns that mitigate large-scale data collection, necessary for any deep learning-based model training. The data quality challenges and privacy concerns have hindered the use of advanced vision-based in-bed behavior monitoring systems at home, which during the recent COVID-19 pandemic could have been an effective way to control the spread of the virus by avoiding in-person visits to clinics.

关键词： Training Pediatrics Data privacy Pandemics Image processing Pose estimation signal processing algorithms

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Optimal Intermittent Particle Filter

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2814-2825页

作者： Aspeel, Antoine Gouverneur, Amaury Jungers, Raphael M. Macq, Benoit UCLouvain ICTEAM B-1348 Louvain La Neuve Belgium

The problem of the optimal allocation (in the expected mean square error sense) of a measurement budget for particle filtering is addressed. We propose three different optimal intermittent filters, whose optimality criteria depend on the information available at the time of decision making. For the first, the stochastic program filter, the measurement times are given by a policy that determines whether a measurement should be taken based on the measurements already acquired. The second, called the offline filter, determines all measurement times at once by solving a combinatorial optimization program before any measurement acquisition. For the third one, which we call online filter, each time a new measurement is received, the next measurement time is recomputed to take all the information that is then available into account. We prove that in terms of expected mean square error, the stochastic program filter outperforms the online filter, which itself outperforms the offline filter. However, these filters are generally intractable. For this reason, the filter estimate is approximated by a particle filter. Moreover, the mean square error is approximated using a Monte-Carlo approach, and different optimization algorithms are compared to approximately solve the combinatorial programs (a random trial algorithm, greedy forward and backward algorithms, a simulated annealing algorithm, and a genetic algorithm). Finally, the performance of the proposed methods is illustrated on two examples: a tumor motion model and a common benchmark for particle filtering.

关键词： Atmospheric measurements Stochastic processes signal processing algorithms Mean square error methods Filtering algorithms Information filters Particle measurements Optimal measurement times particle filtering sequential Monte Carlo methods sparse measurements genetic algorithm

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signal Tracking Algorithm With Adaptive Multipath Mitigation and Experimental Results for LTE Positioning Receivers in Urban Environments

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第4期58卷 2779-2795页

作者： Wang, Pai Wang, Yang Morton, Y. Jade Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab BeiDou Nav & Locat Serv Shanghai Peoples R China Univ Colorado Colorado Ctr Astrodynam Res Smead Dept Aerosp Engn Sci Boulder CO 80309 USA

Positioning with cellular signals has been gaining attention in urban and indoor environments, where global navigation satellite system signals have limited availability due to interference, blockage, or multipath. However, accurate and reliable tracking of cellular signals under highly dynamic urban channel conditions remains a challenging task. This article presents a cellular long-term evolution (LTE) signal tracking algorithm implemented by an adaptive multipath estimating delay lock loop (AMEDLL) to achieve carrier phase synchronization and time-of-arrival (TOA) tracking under severe multipath propagation conditions. The analytical expression of the coherently integrated correlation result over multiple slots with the LTE cell-specific reference signal is derived. A multipath estimator along with a simple yet efficient multipath estimation monitoring approach is developed to estimate the parameters of all detected multipath signals. Several heuristic monitoring criteria based on historical multipath parameter estimations are established to enable adaptive adjustment of the estimated path number. Real LTE signals are collected in an urban environment for the signal tracking performance evaluation. This article presents two case studies with varying levels of multipath effects and signal power to illustrate the effectiveness of the developed signal tracking algorithm. Instead of a TOA truth reference, open-loop carrier phase estimations are used to analyze the TOA tracking error. Our analyses demonstrate that the AMEDLL-based tracking algorithm provides improved TOA estimation accuracy over the existing super-resolution-algorithm-based and delay-lock-loop-based tracking schemes.

关键词： Long Term Evolution Receivers Synchronization OFDM signal processing algorithms Tracking loops Time of arrival estimation Cellular signal long-term evolution (LTE) multipath estimator multipath propagation positioning time of arrival (TOA)

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