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Joint DOD and DOA Estimation of Coherent Targets for Coprime MIMO Radar

IEEE TRANSACTIONS ON signal processing 2023年 71卷 1408-1420页

作者： Zhang, Xue Zheng, Zhi Wang, Wen-Qin So, Hing Cheung Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Inst Elect & Informat Engn Guangzhou 523808 Guangdong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Recently, coprime arrays have attracted lots of interest due to their ability of providing enhanced degrees-of-freedom and reduced mutual coupling effect compared to conventional uniform linear arrays. Benefitting from these excellent properties, coprime multiple-input multiple-output (MIMO) radar has been recently suggested for improving parameter identifiability and target detection. In this paper, we address the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation of coherent targets in coprime MIMO radar. First of all, we establish an extended virtual uniform rectangular array (URA) by performing array interpolation on the transmit and receive arrays of coprime MIMO radar. Subsequently, we derive a low-rank block Hankel matrix that is constructed using the correlation information of the virtual URA expected outputs, and utilize the matched filter outputs to recover the block Hankel matrix by solving a low-rank structured matrix completion problem. Finally, we estimate the DODs and DOAs of coherent targets by applying the modified matrix pencil method on the recovered low-rank matrix. We also derive the Cramer-Rao bounds with closed-form expressions for DOD and DOA estimation of coherent targets using coprime MIMO radar. The proposed algorithm can identify multiple coherent targets and achieve parameter automatic pairing. Numerical results demonstrate the superiority of the proposed algorithm over several existing approaches when handling coherent targets.

关键词： MIMO radar Direction-of-arrival estimation Estimation US Department of Defense signal processing algorithms MIMO communication Covariance matrices Coprime MIMO radar coherent target direction-of-departure (DOD) direction-of-arrival (DOA) array interpolation block Hankel matrix low-rank structured matrix completion

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A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural Network

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 7518-7528页

作者： Li, Han Zhong, Heping Tian, Zhen Zhang, Peng Tang, Jinsong Naval Univ Engn Naval Inst Underwater Acoust Technol Wuhan 430033 Peoples R China Natl Univ Def Technol Coll Meteorol & Oceanog Changsha 410073 Peoples R China

Two-dimensional phase unwrapping (2-D PU) is the process of converting the measured phase into the real phase in interferometric signal processing. Reliable unwrapping results are critical for digital elevation model generation using interferometric synthetic aperture radar (InSAR) and interferometric synthetic aperture sonar (InSAS). The majority of previous research has concentrated on accuracy, whereas the computational efficiency must be taken into account for the interferometric measurement system that requires real-time processing. This article proposes a low-time-consuming algorithm that can accomplish high-precision 2-D PU for this application scenario. The neural network and a new path-based 2-D PU algorithm make up this algorithm. First, the incorrect region in the gradient field is predicted and corrected using the neural network. The output channelwise variance is then calculated and used to generate the quality maps. Finally, to achieve phase reconstruction, the path-based algorithm performs path planning and flooding integral according to quality maps and compensated gradient. This article also provides a recommended data structure implementation to ensure the algorithm's high efficiency. Experimental results using InSAR and InSAS data show that the proposed algorithm is highly efficient and accurate.

关键词： Prediction algorithms Neural networks Convolutional neural networks signal processing algorithms Real-time systems Synthetic aperture radar Floods Convolutional neural network digital elevation model (DEM) interferometric synthetic aperture radar (InSAR) interferometric synthetic aperture sonar (InSAS) phase unwrapping real-time processing

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A signal Dependent Analysis of Orthogonal Least Squares Based on the Restricted Isometry Property

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1574-1585页

作者： Bosse, Jonathan Univ Paris Saclay DEMR ONERA F-91123 Palaiseau France

The Orthogonal Least Squares (OLS) is a widespread greedy algorithm for sparse signal approximation or reconstruction. We here address the problem of sufficient condition for exact signal support recovery via OLS. Based on the generalized Wielandt inequality, new bounds on the OLS selection rule are provided that enable a simple approach of the Restricted Isometry Property based analysis of OLS. Since these new bounds naturally take the signal magnitude distribution into account, they allow a derivation of a new relaxed exact support recovery condition in the noise-free case. Moreover, we study their influence on the design of such new conditions in bounded noise and Gaussian noise cases.

关键词： Matching pursuit algorithms signal processing algorithms Greedy algorithms Dictionaries Upper bound Gaussian noise Eigenvalues and eigenfunctions Compressed sensing greedy algorithms orthogonal least squares (OLS) Restricted Isometry Property (RIP) sparse reconstruction

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Joint Transceiving and Reflecting Design for Intelligent Reflecting Surface Aided Wireless Power Transfer

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第11期22卷 7478-7491页

作者： Yue, Qingdong Hu, Jie Yang, Kun Yu, Qin Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou 313001 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Huzhou 313001 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

In an intelligent reflecting surface (IRS) aided wireless power transfer (WPT) system, a practical architecture of an energy receiver (ER) is proposed, which includes multiple receive antennas, an analog energy combiner, a power splitter and multiple energy harvesters. In order to maximise the output direct-current (DC) power, the transmit beamformer of the transmitter, the passive beamformer of the IRS, the energy combiner, and the power splitter of the ER are jointly optimised. The optimisation problem is equivalently divided into two sub-problems, which independently maximises the input RF power and the output DC power of the energy harvesters, respectively. A successive linear approximation (SLA) based algorithm with a low complexity is proposed to maximise the input RF power to the energy harvesters, which converges to a Karush-Kuhn-Tucker (KKT) point. We also propose an improved greedy randomized adaptive search procedure (I-GRASP) based algorithm having better performance to maximise the input RF power. Furthermore, the optimal power splitter for maximising the output DC power of the energy harvesters is derived in closed-form. The numerical results are provided to verify the performance advantage of the IRS-aided WPT and to demonstrate that conceiving the optimised energy combiner achieves better WPT performance than the deterministic counterpart.

关键词： Radio frequency Wireless communication MIMO communication Complexity theory Transmitters RF signals signal processing algorithms Wireless power transfer (WPT) intelligent reflecting surface (IRS) power splitter MIMO energy harvester architecture multiple non-linear rectifiers

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Two-Dimensional DOA Estimation via Matrix Completion and Sparse Matrix Recovery for Coprime Planar Array

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第11期72卷 14127-14140页

作者： Liu, Donghe Zhao, Yongbo Xidian Univ Sch Elect Engn Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

The coprime planar array (CPPA) can obtain a larger virtual aperture using the sum-difference co-array (SDCA). Nonetheless, the holes in the SDCA always cause the virtual aperture to be not fully utilized. In order to solve this issue, a two-dimensional (2-D) directional of arrival (DOA) estimation algorithm with CPPA via matrix completion and sparse matrix recovery is proposed in this article. To accurately complete the missing elements in the SDCA, we construct an optimization problem based on the truncated nuclear norm regularization (TNNR) by constraining the conjugate flip symmetry property of the virtual array and the noise term. Moreover, we derive a complex-valued sparse matrix recovery algorithm based on the fast iterative shrinkage-thresholding (FISTA) method avoiding using Kronecker product operations between dictionary matrices, which aims to reduce the computational complexity of the conventional vector-form sparse recovery algorithms. Therefore, the proposed algorithm can achieve a larger virtual aperture and lower computational complexity, improving the angle estimation performance. Simulation results demonstrate the effectiveness of the proposed algorithm for CPPA.

关键词： Sparse matrices Estimation Direction-of-arrival estimation Transmission line matrix methods signal processing algorithms Apertures Computational complexity Coprime planar array (CPPA) two-dimensional directional of arrival (DOA) estimation matrix completion sparse matrix recovery

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Localization of Transmitters and Scatterers by Single Receiver

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 2267-2282页

作者： Zhang, Yang Ho, K. C. Univ Missouri Elect Engn & Comp Sci Dept Columbia MO 65211 USA

Localization by single receiver is attractive in practice. This article exploits the additional propagation paths from scatterers for the localization of transmitters, using the time differences of arrival between the direct and scattered signals and their angles of arrival observed at the receiver. Localization is possible only if there are at least two transmitters and at least two scatterers, which may not happen in a real scenario. We explore the utilization of intentional transmitter or scatterer at known position to assist in the localization, thereby enabling localization with arbitrary number of transmitters or scatterers. The challenge for this problem is that the positions of the unintentional scatterers are not known and they need to be estimated in conjunction with the transmitter positions. A non-iterative localization algorithm based on the integration of several minimal measurement solutions is proposed, with the analysis in supporting the Cramer-Rao Lower Bound performance for Gaussian noise over the small error region. The optimum placement of single intentional transmitter is derived for the 2-D and 3-D scenarios, as well as for single intentional scatterer. Simulation confirms the expected performance of the proposed positioning solution and validates the optimum placement.

关键词： Transmitters Location awareness Receivers Sensors Synchronization Stacking signal processing algorithms AOA localization optimum geometry TDOA

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An Improved Gray Wolf Algorithm for Radiation Near-Field Source Localization in Exact Model

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IEEE SENSORS JOURNAL 2023年第21期23卷 26311-26326页

作者： Zhang, Qi Li, Wenxing Li, Si Yang, Bin Mao, Yunlong Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Jiangsu Univ Sci & Technol Sch Elect & Informat Zhenjiang 212100 Jiangsu Peoples R China Hangzhou Dianzi Univ Hangzhou Sch Cyberspace Hangzhou 310018 Peoples R China

Spectral search algorithms are widely used in radiation near-field (RNF) source locations because of their high estimation accuracy, but they have disadvantages such as large computation and long running time. To solve this problem, the Fresnel model is mainly adopted in the existing works of literature to realize the source location estimation in the algorithm of spectral search. Although the operation time is effectively reduced, there is a mismatch between the Fresnel model and the exact model, resulting in the existing method being not suitable for the source location under the exact model. In order to shorten the running time of the RNF source spectrum search algorithm under the exact model, an improved gray wolf optimal (IGWO) algorithm based on the exact model is proposed to estimate the RNF source position, which guarantees the high precision and shorten the operation time. The algorithm uses the noise subspace fitting method as the cost function and the power function as the convergence factor, which shortens the time required for source location and improves the estimation accuracy of the algorithm. The simulation results show that the IGWO algorithm takes less time and has better estimation accuracy than the existing source location algorithms.

关键词： Position measurement Estimation Sensors Approximation algorithms signal processing algorithms Sensor arrays Location awareness Array signal processing improved gray wolf optimal (IGWO) algorithm near field source localization

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Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima's Replica Formula)

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IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第3期69卷 1824-1852页

作者： Gerbelot, Cedric Abbara, Alia Krzakala, Florent Univ PSL Sorbonne Univ Lab Phys Ecole normale Super CNRS F-75005 Ens Paris France NYU Courant Inst Math Sci New York NY 10012 USA Lab Phys Ecole normalesu perieure EPFL F-75005 Ens Paris France Lab Computat Biol & Theoret Biophys EPFL CH-1015 Lausanne Switzerland IdePh Lab EPFL CH-1015 Lausanne Switzerland

There has been a recent surge of interest in the study of asymptotic reconstruction performance in various cases of generalized linear estimation problems in the teacher-student setting, especially for the case of i.i.d standard normal matrices. Here, we go beyond these matrices, and prove an analytical formula for the reconstruction performance of convex generalized linear models with rotationally-invariant data matrices with arbitrary bounded spectrum, rigorously confirming, under suitable assumptions, a conjecture originally derived using the replica method from statistical physics. The proof is achieved by leveraging on message passing algorithms and the statistical properties of their iterates, allowing to characterize the asymptotic empirical distribution of the estimator. For sufficiently strongly convex problems, we show that the two-layer vector approximate message passing algorithm (2-MLVAMP) converges, where the convergence analysis is done by checking the stability of an equivalent dynamical system, which gives the result for such problems. We then show that, under a concentration assumption, an analytical continuation may be carried out to extend the result to convex (non-strongly) problems. We illustrate our claim with numerical examples on mainstream learning methods such as sparse logistic regression and linear support vector classifiers, showing excellent agreement between moderate size simulation and the asymptotic prediction.

关键词： Heuristic algorithms Approximation algorithms Estimation Convergence signal processing algorithms Mathematical models Machine learning Parametric statistics estimation error message passing expectation-maximization algorithms optimization convergence convex functions linear matrix inequalities

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Understanding How Pretraining Regularizes Deep Learning algorithms

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5828-5840页

作者： Yao, Yu Yu, Baosheng Gong, Chen Liu, Tongliang Univ Sydney Sch Comp Sci Camperdown NSW 2006 Australia Nanjing Univ Sci & Technol Sch Comp Sci & Engn Minist Educ PCA Lab Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Minist Educ Key Lab Intelligent Percept & Syst High Dimens In Nanjing 210094 Peoples R China

Deep learning algorithms have led to a series of breakthroughs in computer vision, acoustical signal processing, and others. However, they have only been popularized recently due to the groundbreaking techniques developed for training deep architectures. Understanding the training techniques is important if we want to further improve them. Through extensive experimentation, Erhan et al. (2010) empirically illustrated that unsupervised pretraining has an effect of regularization for deep learning algorithms. However, theoretical justifications for the observation remain elusive. In this article, we provide theoretical supports by analyzing how unsupervised pretraining regularizes deep learning algorithms. Specifically, we interpret deep learning algorithms as the traditional Tikhonov-regularized batch learning algorithms that simultaneously learn predictors in the input feature spaces and the parameters of the neural networks to produce the Tikhonov matrices. We prove that unsupervised pretraining helps in learning meaningful Tikhonov matrices, which will make the deep learning algorithms uniformly stable and the learned predictor will generalize fast w.r.t. the sample size. Unsupervised pretraining, therefore, can be interpreted as to have the function of regularization.

关键词： Deep learning Training Neural networks Prediction algorithms signal processing algorithms Decoding Approximation algorithms Classification deep learning regularization representation learning unsupervised pretrain

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People Flow Detection Algorithm Based on a Multiencoder-Classifier Cotraining Architecture for FMCW Radar

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

作者： Cao, Zhihui Wu, Zhijing Yu, Xuliang Song, Chunyi Xu, Zhiwei Zhejiang Univ Inst Marine Elect & Intelligent Syst Ocean Coll Zhoushan 316021 Peoples R China Zhejiang Univ Engn Res Ctr Ocean Sensing Technol & Equipment Minist Educ Zhoushan 316021 Peoples R China Donghai Lab Zhoushan 316021 Peoples R China

Deep learning (DL) frameworks are widely used in various applications due to their superiority over conventional handcrafted feature-based algorithms. However, applying DL to time-range feature map-based people flow detection (PFD) with frequency-modulated continuous wave (FMCW) radar still faces several challenges: 1) simultaneously achieving people counting and motion direction recognition requires a unified framework;2) existing mainstream network backbones designed for semantic information-rich optical images or natural language suffer performance loss in time-range feature maps with weak semantic information;and 3) the construction of labeled datasets in PFD scenes is usually costly, and limited data lead to performance loss due to overfitting. Therefore, this article proposes novel solutions from various aspects to efficiently apply DL to time-range feature map-based PFD. First, new preprocessing pipelines with Doppler spectrum analysis-based feature map truncation (DSAFMT) are proposed for the first time to simultaneously achieve people counting and direction recognition using a single radar in the radar PFD field. Second, a novel lightweight multiscale feature space fusion-based convolutional neural network backbone (MFSNet) is designed to efficiently extract multichannel differentiated representative features from time-range feature maps. Finally, a multiencoder-classifier cotraining architecture based on embedded features with two data synthesis methods and a newly designed loss function is proposed to improve the generalization ability of the algorithm. Using the test dataset collected from real scenes, the performance comparison results show that the proposed PFD algorithm outperforms the state-of-the-art algorithms and ablation studies demonstrate the effectiveness of each component of the proposed algorithm in PFD.

关键词： Radar Feature extraction Phase frequency detectors signal processing algorithms Classification algorithms Task analysis Radar detection Deep learning (DL) frequency-modulated continuous wave (FMCW) radar people flow detection (PFD) time-range feature map

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