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Robust Tracking of Multiple Targets Subspace Based on Distributed Array Networks

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 9758-9768页

作者： Chen, Ningkang Wei, Pine Gao, Lin Li, Wanchun Liu, Liang Zhang, Huaguo Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610054 Peoples R China

Robust subspace tracking is a real-time solution for suppressing harsh interference and estimating the target subspace in complex electromagnetic environments. In this article, a robust and decentralized subspace tracking method is proposed. The multisensor array is divided into a number of nodes, each attached with a processor and no central processing unit (CPU) is deployed. In order to reduce the impact of a burst pulse, a robust real-time update algorithm of the subspace is designed through the weighted least squares method. The optimization model established in this article is robust to synchronization error. Then a subspace real-time update algorithm is proposed based on the average consensus (AC) method that integrates the information of distributed arrays. The computational complexity and convergence properties of the proposed algorithm are also investigated. Simulation results show that the proposed approach converges to the subspace estimation error by an order of magnitude compared with the single-node algorithm.

关键词： signal processing algorithms Target tracking Peer-to-peer computing Arrays Array signal processing Real-time systems Eigenvalues and eigenfunctions Average consensus (AC) distributed PCA recursive least squares robust subspace tracking

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D²UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2023年第2期17卷 502-512页

作者： Jacome, Roman Bacca, Jorge Arguello, Henry Univ Ind Santander Dept Phys Santander 680002 Spain Univ Ind Santander Dept Comp Sci Santander 680002 Spain

Compressive spectral imaging (CSI) has attracted significant attention since it employs synthetic apertures to codify spatial and spectral information, sensing only 2D projections of the 3D spectral image. However, these optical architectures suffer from a trade-off between the spatial and spectral resolution of the reconstructed image due to technology limitations. To overcome this issue, compressive spectral image fusion (CSIF) employs the projected measurements of two CSI architectures with different resolutions to estimate a high-spatial high-spectral resolution. This work presents the fusion of the compressive measurements of a low-spatial high-spectral resolution coded aperture snapshot spectral imager (CASSI) architecture and a high-spatial low-spectral resolution multispectral color filter array (MCFA) system. Unlike previous CSIF works, this paper proposes joint optimization of the sensing architectures and a reconstruction network in an end-to-end (E2E) manner. The trainable optical parameters are the coded aperture (CA) in the CASSI and the colored coded aperture in the MCFA system, employing a sigmoid activation function and regularization function to encourage binary values on the trainable variables for an implementation purpose. Additionally, an unrolling-based network inspired by the alternating direction method of multipliers (ADMM) optimization is formulated to address the reconstruction step and the acquisition systems design jointly. Finally, a spatial-spectral inspired loss function is employed at the end of each unrolling layer to increase the convergence of the unrolling network. The proposed method outperforms previous CSIF methods, and experimental results validate the method with real measurements.

关键词： Spatial resolution Image reconstruction Apertures Image coding signal processing algorithms Optical sensors Optical imaging End-to-End Optimization Synthetic Aperture Design Compressive Spectral Image Fusion Unrolling algorithms

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Confederated Learning: Federated Learning With Decentralized Edge Servers

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 248-263页

作者： Wang, Bin Fang, Jun Li, Hongbin Yuan, Xiaojun Ling, Qing Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510006 Guangdong Peoples R China Pazhou Lab Guangzhou 510300 Guangdong Peoples R China

Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider a centralized framework, in which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner. Due to stringent communication and bandwidth constraints, such a centralized framework has limited scalability as the number of devices grows. To address this issue, in this paper, we propose a ConFederated Learning (CFL) framework. The proposed CFL consists of multiple servers, in which each server is connected with an individual set of devices as in the conventional FL framework, and decentralized collaboration is leveraged among servers to make full use of the data dispersed throughout the network. We develop a stochastic alternating direction method of multipliers (ADMM) algorithm for CFL. The proposed algorithm employs a random scheduling policy which randomly selects a subset of devices to access their respective servers at each iteration, thus alleviating the need of uploading a huge amount of information from devices to servers. Theoretical analysis is presented to justify the proposed method. Numerical results show that the proposed method can converge to a decent solution significantly faster than gradient-based FL algorithms, thus boasting a substantial advantage in terms of communication efficiency.

关键词： Servers Training Peer-to-peer computing signal processing algorithms Scalability Data models Computational modeling Confederated learning ADMM random scheduling

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Programmable Metasurface Hybrid MIMO Beamforming: Channel Estimation, Data Transmission, and System Implementations at 28 GHz

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IEEE SYSTEMS JOURNAL 2023年第1期17卷 1270-1281页

作者： Li, Yueheng Long, Xueyun de Oliveira, Lucas Giroto Eisenbeis, Joerg Alabd, Mohamad Basim Bettinga, Sven Wan, Xiang Cui, Tie Jun Zwick, Thomas Karlsruhe Inst Technol D-76131 Karlsruhe Germany Southeast Univ Nanjing 12579 Jiangsu Peoples R China

With the development of wireless communication generations, multiple-input and multiple-output (MIMO) architectures have proven to be a solution to the higher required data rates for the increasing number of mobile users. As a matter of fact, better earnings can be gathered from a low-cost system with effective functions, which results in the hybrid beamforming architecture combining digital chains with large-scale antenna arrays. The programmable metasurface (PM) is a promising antenna array concept that realizes reconfigurable beamforming. It is raised to be an attractive antenna array architecture due to its low price and power consumption. However, experimental investigations combining PMs with modern wireless communication systems have not been intensively studied. Its challenges and difficulties of signal processing and system implementation remain uncertainties to be discovered. In this article, a PM hybrid MIMO beamforming system including the aforementioned important topics is presented as follows. First of all, the hybrid beamforming channel estimation algorithm adapted for PM is created by merging analog beam training and digital interleaved orthogonal frequency division multiplexing. Afterward, data transmissions based on the estimated channel state information utilizes variant signal recovery methods to examine the channel estimation accuracy and system feasibilities. To practically analyze the proposed system, the aforementioned aspects are implemented into system-level experiments using three PMs operating at 28 GHz for downlink wireless communication in both single-user and multiuser scenarios. Proper results are delivered, which successfully prove the PM hybrid MIMO beamforming system functionalities.

关键词： Array signal processing MIMO communication Channel estimation signal processing algorithms Antenna arrays Training Symbols Antenna arrays communication systems signal processing

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Two-Dimensional Direction Finding Based on Cylindrical Nested Conformal Array

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 6853-6862页

作者： Fu, Mingcheng Zheng, Zhi Wang, Wen-Qin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Shenzhen Peoples R China UESTC Inst Elect & Informat Engn Dongguan 523808 Guangdong Peoples R China

Recently, nested conformal arrays have attracted considerable interest due to their ability to provide increased array aperture and enhanced degrees of freedom compared to uniform conformal arrays. In this article, we address the problem of 2-D direction finding using the cylindrical nested conformal array (CNCA). First of all, we derive the coarray signal model of the CNCA and utilize different coarray signal components to construct multiple Toeplitz matrices. Based on the constructed Toeplitz matrices, we further develop two direction-finding algorithms to achieve the decoupled estimation of the 2-D direction. Unlike the exiting approaches, the proposed two algorithms exploit more coarray output components, and hence, they have better direction-finding performance. Moreover, our algorithms can be applied to a larger number of nested subarrays in the CNCA. Numerical results demonstrate the superiority of the proposed algorithms in comparison to the exiting methods.

关键词： Sensors signal processing algorithms Manifolds Estimation Covariance matrices Azimuth Apertures Cylindrical nested conformal array (CNCA) difference coarray ESPRIT-like reduced-dimensional (R-D) MUSIC two-dimensional (2-D) direction finding

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Parameter Estimation Using a Triad of Electrically Long Dipole Despite Data Model Mismatch

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第2期59卷 1670-1682页

作者： Zhong, Wenye Song, Yang Cai, Guofa Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China

Most dipole-antenna signal processing algorithms have focused on "short dipole" whose physical length ($L$) is under ($1/10$) of a wavelength ($\lambda$). Recent works now handle "long dipoles" of an electrical length $L/\lambda \in [0.1, 1]$. This article advances an algorithmic paradigm that uses short-dipole algorithm on long-dipole data. By studying the mismatched Cramer-Rao bound, we find that applying short-dipole algorithm to a long-dipole data model may yield parameters' estimates that are not far from their true values especially when $L/\lambda$ is small. This phenomenon motivates a two-step algorithm where we apply short-dipole algorithm to get parameters' coarse estimates, which are then fine-tuned by an iterative algorithm. Our algorithm maximally retains the merits of the existing short-dipole algorithms while processing the signals received by a long dipole triad. Monte Carlo simulations verify the effectiveness of the proposed paradigm.

关键词： signal processing algorithms Direction-of-arrival estimation Estimation error Antenna measurements Dipole antennas Azimuth Data models Dipoles ESPRIT mismatched Cramer-Rao bound (MCRB) MUSIC mismatch model

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A Dual-Recursive-Least-Squares Algorithm for Automotive Radar Interference Suppression

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第10期24卷 10603-10617页

作者： Wang, Ping Yin, Xuefeng Rodriguez-Pineiro, Jose Chen, Zhuoyu Zhu, Pengqi Li, Gang Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China Zongmu Technol Shanghai 201210 Peoples R China

Automotive radar sensors are widely used in vehicles so as to allow the "advanced driver assistance systems (ADAS)" to work. As far as we are concerned, no mandatory regulations were established so far for interference control in the cases where multiple vehicles equipped with automotive radars exist in close proximity. Consequently, those inter-vehicle cross-interference easily result in the radar performance degradation, e.g. incapability of target detection and identification. In this paper, an interference suppression algorithm is proposed which utilizes two Recursive-Least-Square (RLS) adaptive filters to estimate, the interference signals caused by the "aggressor" radar and the echo signals from the "ego" radar. The algorithm operates in the iterative mode with its initial input being the originally received beat signals involving interference. In each iteration, the estimates of the interference signals and of the clean signals are calculated sequentially by the two RLS filters, and the beat signal is updated with parts of the fragments substituted with their interference-mitigated counterparts. Such iterative operations stop when the convergence criterion, e.g. none of the fragments in the beat signals are considered being interfered, is achieved or the iteration number reaches the limit predefined due to the practical constraints. Simulation and measurement results demonstrate that the proposed dual-RLS-based algorithm outperforms the existing methods in terms of lower interference-to-signal ratio resultant and superior capability of retrieving the original range-Doppler profile.

关键词： Radar Interference Chirp Radar antennas Automotive engineering signal processing algorithms Frequency-domain analysis Frequency-modulated continuous wave (FMCW) radar interference suppression adaptive noise canceller RLS adaptive algorithm

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A Parameter-Free Enhanced SS&E Algorithm Based on Deep Learning for Suppressing Azimuth Ambiguities

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Suo, Yuxi Fu, Kun Wu, Youming Diao, Wenhui Dai, Wei Sun, Xian Chinese Acad Sci Aerosp Informat Res Inst Beijing 100190 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Network Informat Syst Technol NIST Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100190 Peoples R China

Aliasing artifacts introduced by azimuth ambiguity seriously impact the interpretation of synthetic aperture radar images. To achieve parameter-free and fast azimuth ambiguity suppression, a novel deep learning model is designed to estimate the ambiguous signal intensity to total signal intensity ratio in the range-Doppler domain. This model does not depend on processing parameters and can be applied in any acquisition mode. The mean shift algorithm is applied to select less ambiguous subspectra according to the estimation result. The selected subspectra are restored to a full spectrum with an energy concentrated extrapolation method to preserve the resolution. The enhanced spectral selection and extrapolation algorithm overcomes the dependence on processing parameters, and experiments based on TerraSAR-X and Radarsat-2 images indicate that the proposed algorithm suppresses the azimuth ambiguity significantly.

关键词： Azimuth Doppler effect Estimation signal processing algorithms Deep learning Extrapolation Synthetic aperture radar Azimuth ambiguity suppression deep learning signal processing synthetic aperture radar (SAR)

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Squared Sine Adaptive Algorithm and Its Performance Analysis

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2023年 31卷 3617-3628页

作者： Huang, Xinqi Li, Yingsong Zakharov, Yuriy Miao, Yongchun Huang, Zhixiang Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Univ York Sch Phys Engn & Technol York YO10 5DD England

The squared sine adaptive (SSA) algorithm is presented for identification scenarios, such as acoustic-echo cancellation (AEC) applications, in non-Gaussian environments. To devise the SSA algorithm, a novel cost function is constructed by exerting a sliding window-type squared sine function on the estimation error vector, which provides robustness in impulsive-noise environments and speeds up convergence when the input is colored. Theoretical results are presented for predicting the mean-weight, convergence, transient excess-mean-square-error (EMSE), and tracking behaviour. Moreover, the minimum EMSE and the optimum step size for tracking are presented. The computational complexity of the SSA algorithm has also been investigated. Numerical experiments demonstrate that results of the theoretical analysis match the simulated results very well and the proposed SSA algorithm outperforms known algorithms in AEC applications.

关键词： Convergence signal processing algorithms Transient analysis Cost function Speech processing Prediction algorithms Behavioral sciences Adaptive filters AEC non-Gaussian environment performance analysis

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AI-Powered Noncontact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing

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IEEE INTERNET OF THINGS JOURNAL 2023年第11期10卷 9465-9481页

作者： Abedi, Hajar Ansariyan, Ahmad Morita, Plinio P. Wong, Alexander Boger, Jennifer Shaker, George Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Univ Waterloo Elect Engn Dept Waterloo ON N2L 3G1 Canada Univ Waterloo Sch Publ Hlth Sci Waterloo ON N2L 3G1 Canada

In this work, we present a cloud-based system for noncontact, real-time recognition, and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition, and gait analysis. To train deep learning models, we utilize range-Doppler maps generated from a data set of real-life in-home activities. The performance of several deep learning models is evaluated based on accuracy and prediction time, with the gated recurrent network [gated recurrent unit (GRU)] model selected for real-time deployment due to its balance of speed and accuracy compared to 2-D convolutional neural network long short-term memory (2D-CNNLSTM) and long short-term memory (LSTM) models. The overall accuracy of the GRU model for classifying in-home physical activities of trained subjects is 93%, with 86% accuracy for a new subject. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject's activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices.

关键词： Radar Legged locomotion Biomedical monitoring signal processing algorithms Monitoring Radar signal processing Activity recognition autonomous systems gait monitoring mm-wave radar sequential deep learning

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