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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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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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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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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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Statistical Analysis of Maximum Correntropy Criterion Subband Adaptive Filtering Algorithm

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IEEE signal processing LETTERS 2025年 32卷 2025-2029页

作者： Xu, Wenjing Zhao, Haiquan Lv, Shaohui Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Beijing 100816 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

The correntropy contains all the even-order information of data and is well suited for dealing with non-Gaussian noise. The subband adaptive filtering algorithm combined with the maximum correntropy criterion (MCC-SAF) has been proposed to efficiently handle colored input signals and impulsive noise. Compared with other similar algorithms, the MCC-SAF algorithm exhibits faster convergence speed and lower steady-state error in non-Gaussian noise environments. However, to the best of our knowledge, the transient and steady-state statistical performance of this algorithm is still missing. In this letter, the mean-square performance of the MCC-SAF is analyzed under Gaussian, binary, and uniform noises using vectorization method, in which the mathematical expectation of the exponential term is computed by means of the moment generating function (MGF), and finally the theoretical prediction models for mean square deviation (MSD) are derived and verified by computer simulations.

关键词： signal processing algorithms Adaptive filters Filtering algorithms Convergence Prediction algorithms Filtering theory Attenuation Transient analysis Steady-state Gaussian noise Maximum correntropy criterion non-Gaussian noise performance analysis moment generating function (MGF)

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A New Hybrid PSO-HHO Wrapper Based Optimization for Feature Selection

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IEEE ACCESS 2025年 13卷 87090-87099页

作者： Azeem, Sumbul Javed, Shazia Naseer, Iftikhar Ali, Oualid Ghazal, Taher M. Lahore Coll Women Univ Dept Math Lahore 54000 Pakistan Super Univ Fac Comp Sci & Informat Technol Lahore 54000 Pakistan Appl Sci Univ Coll Arts & Sci Manama 11937 Bahrain Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Dept Networks & Cybersecur Amman 19111 Jordan

Datasets used in data analysis often contain irrelevant or redundant attributes. These attributes hinder the performance of predictive models. Therefore, an effective preprocessing feature selection procedure is essential to identify the relevant features and eliminate unnecessary ones. Metaheuristic algorithms, mainly inspired by nature, are strong candidates for the feature selection process, as they can efficiently search large solution spaces. Metaheuristic algorithms provide flexible strategies for complex optimization problems such as traveling salesman problem, scheduling problems, nonlinear integer programming, and multi-objective optimization. Reasonably balancing exploration and exploitation will increase the search algorithm's performance. This research combines Particle Swarm Optimization (PSO) and Harris Hawk Optimization (HHO) to form a Hybrid PSO-HHO approach to enhance the feature selection process. The wrapper-based approach employs the K-nearest neighbors classifier using the Euclidean distance metric to identify the optimal solutions. The performance of the proposed binary algorithm is evaluated using four standard benchmark datasets obtained from the UCI repository. Classification accuracy and the number of selected features are used as performance indicators. Experimental results demonstrated that the hybrid model achieved mean accuracies of 95.23%, 97.61%, 95.20% and 92.24% across the four benchmark datasets, outperforming traditional PSO and HHO approaches.

关键词： Feature extraction Classification algorithms Optimization Metaheuristics signal processing algorithms Accuracy Prediction algorithms Particle swarm optimization Machine learning algorithms Filtering algorithms Feature selection wrapper based filter methods Harris Hawk optimization metaheuristic algorithms particle swarm optimization

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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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ReAdapt: A Reconfigurable Datapath for Runtime Energy-Quality Scalable Adaptive Filters

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第1期70卷 327-339页

作者： Lopes Pereira, Pedro Taua Paim, Guilherme Cesar da Costa, Eduardo Antonio Melo de Almeida, Sergio Jose Bampi, Sergio Univ Fed Rio Grande do Sul UFRGS Inst Informat INF Grad Program Microelect PGMICRO BR-91501970 Porto Alegre RS Brazil Univ Catolica Pelotas UCPel Grad Program Elect Engn & Comp BR-96015560 Pelotas RS Brazil

This paper proposes ReAdapt-a reconfigurable datapath architecture for scaling the energy-quality trade-off of adaptive filtering at runtime. The ReAdapt can dynamically select four adaptive filtering algorithms for gradating complexity levels during runtime by reconfiguring the processing flow in its datapath and by blocking the switching activity (e.g., reducing the CMOS dynamic power) of unused modules with data-gating. The ReAdapt proposal can scale the energy-quality trade-off by choosing the following four different levels of filter algorithms complexity: 1) least mean square (LMS);2) partial update normalized LMS (PU-NLMS);3) set-membership normalized LMS (SM-NLMS);4) normalized LMS (NLMS). The ReAdapt architecture reuses common modules of each adaptive filter, resulting in a compact VLSI hardware implementation. The ReAdapt architecture operation is implemented in a case-study for interference mitigation for electroencephalogram (EEG) signal processing. The hardware synthesis results show an increase of 6.80 times in throughput and at least a reduction of 2.84 times in energy per operation compared with the state-of-the-art adaptive filters. This paper also investigates the benefits of dynamically reconfiguring the four ReAdapt operating modes at runtime for different levels of signal-to-noise ratio (SNR) for the processed signals. We also demonstrate that dynamically reconfiguring the ReAdapt operating modes during runtime results in an optimal energy-quality trade-off which is advantageous over the conventional single static mode.

关键词： Runtime Computer architecture Complexity theory Very large scale integration signal processing algorithms Heuristic algorithms Finite impulse response filters VLSI design energy-quality scalable filtering reconfigurable architectures adaptive filters LMS PU-NLMS SM-NLMS NLMS digital signal processing

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