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IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)

作者： Li Wei Huang, Chongwen Alexandropoulos, George C. Yuen, Chau Singapore Univ Technol & Design Engn Prod Dev Singapore Singapore Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens Greece

ISBN: (纸本)9781728119465

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low power configuration enabling massive connectivity and low latency communications. Channel estimation in RIS-based systems is one of the most critical challenges due to the large number of reflecting unit elements and their distinctive hardware constraints. In this paper, we focus on the downlink of a RIS-assisted multi-user Multiple Input Single Output (MISO) communication system and present a method based on the parallel factor (PARAFAC) decomposition to unfold the resulting cascaded channel model. The proposed method includes an alternating least squares algorithm to iteratively estimate the channel between the base station and RIS, as well as the channels between RIS and users. Our selective simulation results show that the proposed iterative channel estimation method outperforms a benchmark scheme using genie-aided information. We also provide insights on the impact of different RIS settings on the proposed algorithm.

关键词： Reconfigurable intelligent surfaces parallel factor decomposition iterative channel estimation metasurfaces

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Ambient modal identification using multi-rank parallel factor decomposition

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STRUCTURAL CONTROL & HEALTH MONITORING 2015年第4期22卷 595-614页

作者： Sadhu, A. Goldack, A. Narasimhan, S. Lakehead Univ Dept Civil Engn Thunder Bay ON P7B 5E1 Canada Tech Univ Berlin Dept Conceptual & Struct Design Berlin Germany

In this paper, the problem of underdetermined modal identification where the number of modes to be identified is larger than the available sensor measurements is addressed using parallel factor decomposition and blind source separation. Underdetermined situations not only arise when the number of sensors are limited but also when narrowband excitations are present in the measurements, for example, in pedestrian-induced vibration of footbridges. The basic premise of the proposed algorithm is based on multiple-rank parallel factor decomposition of covariance tensors constituted from vibration response measurements. Unlike conventional parallel factor decomposition using a single rank order, the proposed method utilizes multiple rank order decompositions. A stability chart constructed from identified sources through such multiple rank orders allows for the robust estimation of active modes. The statistical characteristics of the resulting modes are evaluated in order to delineate the sources corresponding to external disturbances versus inherent modes of the system. The proposed framework enables an automated selection of rank order, detection of external harmonics and an estimation of modal parameters that are relatively insensitive to the sensor configuration. The performance of the algorithm is illustrated using both numerical studies and an experimental study using pedestrian-induced vibration measurements of a stress-ribbon bridge located at the Technical UniversityBerlin, Germany. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： modal identification parallel factor decomposition blind source separation underdetermined problem narrowband excitation pedestrian bridge human-induced vibration

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Ambient modal identification of structures equipped with tuned mass dampers using parallel factor blind source separation

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SMART STRUCTURES AND SYSTEMS 2014年第2期13卷 257-280页

作者： Sadhu, A. Hazra, B. Narasimhan, S. Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada

In this paper, a novel parallel factor (PARAFAC) decomposition based Blind Source Separation (BSS) algorithm is proposed for modal identification of structures equipped with tuned mass dampers. Tuned mass dampers (TMDs) are extremely effective vibration absorbers in tall flexible structures, but prone to get de-tuned due to accidental changes in structural properties, alteration in operating conditions, and incorrect design forecasts. Presence of closely spaced modes in structures coupled with TMDs renders output-only modal identification difficult. Over the last decade, second-order BSS algorithms have shown significant promise in the area of ambient modal identification. These methods employ joint diagonalization of covariance matrices of measurements to estimate the mixing matrix (mode shape coefficients) and sources (modal responses). Recently, PARAFAC BSS model has evolved as a powerful multi-linear algebra tool for decomposing an nth order tensor into a number of rank-1 tensors. This method is utilized in the context of modal identification in the present study. Covariance matrices of measurements at several lags are used to form a 3rd order tensor and then PARAFAC decomposition is employed to obtain the desired number of components, comprising of modal responses and the mixing matrix. The strong uniqueness properties of PARAFAC models enable direct source separation with fine spectral resolution even in cases where the number of sensor observations is less compared to the number of target modes, i.e., the underdetermined case. This capability is exploited to separate closely spaced modes of the TMDs using partial measurements, and subsequently to estimate modal parameters. The proposed method is validated using extensive numerical studies comprising of multi-degree-of-freedom simulation models equipped with TMDs, as well as with an experimental set-up.

关键词： modal identification blind source separation parallel factor decomposition tuned-mass damper MTMD

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PARAFAC-Based Blind Identification of Underdetermined Mixtures Using Gaussian Mixture Model

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2014年第6期33卷 1841-1857页

作者： Gu, Fanglin Zhang, Hang Wang, Wenwu Zhu, Desheng PLA Univ Sci & Technol Inst Commun Engn Nanjing 210007 Jiangsu Peoples R China Univ Surrey Dept Elect Engn Guildford GU2 7XK Surrey England

This paper presents a novel algorithm, named GMM-PARAFAC, for blind identification of underdetermined instantaneous linear mixtures. The GMM-PARAFAC algorithm uses Gaussian mixture model (GMM) to model non-Gaussianity of the independent sources. We show that the distribution of the observations can also be modeled by a GMM, and derive a maximum-likelihood function with regard to the mixing matrix by estimating the GMM parameters of the observations via the expectation-maximization algorithm. In order to reduce the computation complexity, the mixing matrix is estimated by maximizing a tight upper bound of the likelihood instead of the log-likelihood itself. The maximum of the tight upper bound is obtained by decomposition of a three-way tensor which is obtained by stacking the covariance matrices of the GMM of the observations. Simulation results validate the superiority of the GMM-PARAFAC algorithm.

关键词： Blind identification Gaussian mixture model Expectation-maximization Underdetermined mixture parallel factor decomposition

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Tensor-Based Angle and Array Gain-Phase Error Estimation Scheme in Bistatic MIMO Rader

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IEEE ACCESS 2019年 7卷 47972-47981页

作者： Guo, Yuehao Wang, Xianpeng Wan, Liangtian Huang, Mengxing Shen, Chong Zhang, Kun Yang, Yongqin Hainan Univ Coll Informat Sci & Technol State Key Lab Marine Resource Utilizat South Chin Haikou 570228 Hainan Peoples R China Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China

This paper investigates the issue of angle and array gain-phase error estimation in multiple-input-multiple-output (MIMO) radar, and a tensor-based angle and gain-phase error estimation scheme is proposed. In our approach, the parallel factor (PARAFAC) decomposition is performed to estimate the transmit and receive direction matrices. Then the estimation of gain error can be obtained according to the relationship between the columns of direction matrices. After that, the linear feature of the phase in the additional well-calibrated array element is utilized to estimate the angles. Finally, by fully using the phase characteristics of all arrays, the phase error can be obtained. Our approach can remove the influence of error accumulation, and thus it has a superior angle and gain-phase error estimation performance, particularly under the condition of low signal-to-noise ratio (SNR). The numerical examples validate the superiority and effectiveness of the proposed scheme.

关键词： Bistatic MIMO radar angle estimation gain-phase error parallel factor decomposition

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A novel unitary PARAFAC method for DOD and DOA estimation in bistatic MIMO radar

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SIGNAL PROCESSING 2017年 138卷 273-279页

作者： Xu, Baoqing Zhao, Yongbo Cheng, Zengfei Li, Hui Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Xian 710071 Peoples R China

In this paper, a novel unitary parallel factor (U-PARAFAC) algorithm of estimating direction-of-departure (DOD) and direction-of-arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar is proposed. A real-valued tensor signal model is constructed by applying the traditional forward-backward averaging technique. Subsequently, the fact that the real-valued tensor follows a PARAFAC model is proved, thus the subspace-based high-order singular value decomposition (HOSVD) method can be avoided in the subsequent solving process. Furthermore, directly operating the real-valued loading factors instead of the signal subspace, traditional unitary ESPRIT (U-ESPRIT) method is firstly extended to the real-valued PARAFAC model. The new algorithm, which exploits the multidimensional structure and does not require the estimation of signal subspace, having good performance especially at low signal-to-noise ratio (SNR). More attractively, compared with classical tensor methods such as the PARAFAC algorithm and the unitary tensor-ESPRIT algorithm, the U-PARAFAC algorithm still performs well without sacrificing array aperture when targets are highly correlated or closely spaced. Additional angle pair-matching is not required. Simulation results verify the effectiveness of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Multiple-input multiple-output radar Angle estimation Real-valued tensor parallel factor decomposition

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Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第6期69卷 4144-4157页

作者： Wei, Li Huang, Chongwen Alexandropoulos, George C. Yuen, Chau Zhang, Zhaoyang Debbah, Merouane Singapore Univ Technol & Design Engn Prod Dev EPD Pillar Singapore 487372 Singapore Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310007 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310007 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Hangzhou 314400 Peoples R China Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 15784 Greece Univ Paris Saclay Cent Supelec F-91192 Gif Sur Yvette France Lagrange Math & Comp Res Ctr F-75007 Paris France

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramer-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

关键词： Channel estimation Estimation Matrix decomposition MISO communication Uplink Approximation algorithms Sparse matrices Channel estimation reconfigurable intelligent surfaces multi-user communication parallel factor decomposition approximate message passing Cramer-Rao bound sum rate performance

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Coarray Tensor-Based Angle Estimation for Bistatic MIMO Radar With a Dilated Moving Receive Array

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 8995-9009页

作者： Luo, Shuai Wang, Yuexian Li, Jianying Tellambura, Chintha Rodrigues, Joel J. P. C. Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada Lusofona Univ COPELABS P-1749024 Lisbon Portugal

Utilizing sparse arrays is a very effective and commonly used method to enhance the degrees of freedom (DOFs) of multiple-input multiple-output (MIMO) radar. Unfortunately, as research on sparse arrays has matured, it has become difficult to greatly improve the DOFs by relying on array structure design only. Moreover, the existing angle estimation methods for sparse MIMO radar would process data under a matrix-based framework rather than the entire coarray tensor, thus suffering some loss in angle estimation performance. In this article, we extend the DOFs of MIMO radar by exploiting sparse array motion and propose an angle estimation method exploiting coarray tensor. First, we not only use sparse arrays at the transmitter and receiver parts of MIMO radar but also dilate the interelement spacing of the receive array on a moving platform. We set the transmitted signal as periodic, and further expand the DOFs and virtual aperture of MIMO radar by using the aperture synthesis technique introduced by array motion. Second, we build a self-correlation tensor model and reshape it to produce an optimal tensor with the highest DOFs that can be obtained under the uniqueness condition of parallel factor decomposition. Third, we theoretically analyze the achievable DOFs of the proposed method and show that the maximum number of detectable targets of bistatic MIMO radar can be increased to about three times. Simulation results verify the correctness of the theoretical analysis and demonstrate the superior estimation performance of our proposed method.

关键词： Tensors MIMO radar Estimation Arrays Array signal processing Receivers Apertures Angle estimation array motion bistatic multiple-input multiple-output (MIMO) radar coarray tensor parallel factor decomposition

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PARAFAC-based Robust Localization for Bistatic MIMO Radar with partially available measurements 8

PARAFAC-based Robust Localization for Bistatic MIMO Radar wi...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Sun, Weize Lin, Xin Huang, Lei Yang, Xin Shenzhen Univ Coll Informat Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781538679463

This article tackles the problem of parameter estimation of bistatic multiple-input multiple output radar in the presence of impulsive noise or outlier. It is assumed that only part of data entries are recorded, and a new approach based on parallel factor decomposition (PARAFAC) is proposed for the joint estimation of directions-of-departures and directions-of arrivals. Since most of the state-of-the-art algorithms fail to give reasonable results under impulsive noise, we utilize the-norm with 0 < p <= 1 to against the outliers. By employing the data model of low-rank higher-order tensor with partly observed entries and outliers, an alternating iterative approach based on augmented Lagrangian multiplier is designed to recover the signal subspaces, and then a standard ESRPIT scheme is suggested for the final target localization. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms in terms of estimation accuracy under Gaussian mixture noise.

关键词： bistatic multiple-input multiple-output radar parallel factor decomposition Gaussian mixture tensor completion directions-of-departure and directions-of-arrival estimation

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PARAFAC Estimators for Coherent Targets in EMVS-MIMO Radar with Arbitrary Geometry

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REMOTE SENSING 2022年第12期14卷 2905-2905页

作者： Zhang, Lei Wang, Han Wen, Fang-Qing Shi, Jun-Peng China Three Gorges Univ Hubei Key Lab Intelligent Vision Based Monitoring Yichang 443002 Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China Yichun Univ Coll Phys Sci & Engn Yichun 336000 Peoples R China Natl Univ Def Technol Coll Elect Countermeasure Hefei 230037 Peoples R China

In the past few years, multiple-input multiple-output (MIMO) radar with electromagnetic vector sensor (EMVS) array, or called EMVS-MIMO radar, has attracted extensive attention in target detection. Unlike the traditional scalar sensor-based MIMO radar, an EMVS-MIMO radar can not only provides a two-dimensional (2D) direction finding of the targets but also offers 2D polarization parameter estimation, which may be important for detecting weak targets. In this paper, we investigate into multiple parameter estimations for a bistatic EMVS-MIMO radar in the presence of coherent targets, whose transmitting EMVS and receiving EMVS are placed in an arbitrary topology. Three tensor-aware spatial smoothing estimators are introduced. The core of the proposed estimators is to de-correlate the coherent targets via the spatial smoothing technique and then formulate the covariance matrix into a third-order parallel factor (PARAFAC) tensor. After the PARAFAC decomposition of the tensor, the factor matrices can be obtained. Thereafter, the 2D direction finding can be accomplished via the normalized vector cross-product technique. Finally, the 2D polarization parameter can be estimated via the least squares method. Unlike the state-of-the-art PARAFAC estimator, the proposed estimators are suitable for arbitrary sensor geometries, and they are robust to coherent targets as well as sensor position errors. In addition, they have better estimation performance than the current matrix-based estimators. Moreover, they are computationally efficient than the current subspace methods, especially in the presence of a large-scale sensor array. In addition, the proposed estimators are analyzed in detail. Numerical experiments coincide with our theoretical findings.

关键词： parallel factor decomposition estimation MIMO radar vector sensors coherent target

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