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A PARAFAC-based algorithm for multidimensional parameter estimation in polarimetric bistatic MIMO radar

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2013年第1期2013卷 1页

作者： Jiang, Hong Zhang, Yu Li, Jia Cui, Haijing Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China

In this article, we investigate the problem of applying the parallel factor quadrilinear decomposition technique to multidimensional target parameter estimation in a polarimetric bistatic multiple-input multiple-output (MIMO) radar system with a uniform rectangular array at the transmitter and a cross-dipole-based uniform rectangular array at the receiver. The signal model is developed, and a novel algorithm is proposed exploiting the quadrilinear alternating least squares to jointly estimate the two-dimensional direction of departure (2D-DOD), two-dimensional direction of arrival (2D-DOA), polarization parameters and Doppler frequency. multidimensional parameters can be automatically paired by this algorithm to avoid the performance degradation resulting from wrong pairing. The developed algorithm requires neither multidimensional spectral peak searching nor covariance matrix estimation and several eigen-value decompositions that may bring error accumulation. Furthermore, multiple targets having close 2D-DODs and close 2D-DOAs or even the same 2D-DOD or 2D-DOA are distinguishable by means of polarization diversity. The algorithm improves the performance of multi-target identification and three-dimensional localization. Numerical simulations demonstrate the effectiveness of the proposed algorithm.

关键词： multidimensional parameter estimation 2D-DOD 2D-DOA Polarization diversity PARAFAC Quadrilinear decomposition Bistatic MIMO radar

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Four-Dimensional parameter estimation for Mixed Far-Field and Near-Field Target Localization Using Bistatic MIMO Arrays and Higher-Order Singular Value Decomposition

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REMOTE SENSING 2024年第18期16卷 3366页

作者： Zhang, Qi Jiang, Hong Zheng, Huiming Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China

In this paper, we present a novel four-dimensional (4D) parameter estimation method to localize the mixed far-field (FF) and near-field (NF) targets using bistatic MIMO arrays and higher-order singular value decomposition (HOSVD). The estimated four parameters include the angle-of-departure (AOD), angle-of-arrival (AOA), range-of-departure (ROD), and range-of-arrival (ROA). In the method, we store array data in a tensor form to preserve the inherent multidimensional properties of the array data. First, the observation data are arranged into a third-order tensor and its covariance tensor is calculated. Then, the HOSVD of the covariance tensor is performed. From the left singular vector matrices of the corresponding module expansion of the covariance tensor, the subspaces with respect to transmit and receive arrays are obtained, respectively. The AOD and AOA of the mixed FF and NF targets are estimated with signal-subspace, and the ROD and ROA of the NF targets are achieved using noise-subspace. Finally, the estimated four parameters are matched via a pairing method. The Cram & eacute;r-Rao lower bound (CRLB) of the mixed target parameters is also derived. The numerical simulations demonstrate the superiority of the tensor-based method.

关键词： near-field and far-field target localization multidimensional parameter estimation higher-order singular value decomposition (HOSVD) tensor

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Phase-Calibration-Based 3-D Beamspace Matrix Pencil Algorithm for Indoor Passive Positioning and Tracking

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IEEE SENSORS JOURNAL 2023年第17期23卷 19670-19683页

作者： Yang, Xiaolong Li, Quanchen Zhou, Mu Wang, Jiacheng Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

In the context of integrated sensing and communication (ISAC), indoor device-free positioning and tracking technology based on channel state information (CSI) has become a key and significant branch. CSI extracted by high-bandwidth chips is not widely used for sensing due to problems of clock asynchrony and phase locking instability. This article implements passive positioning based on the CSI collected by the bcm4366 Wi-Fi chip. First, through extensive testing and observation, we describe the unstable phase difference phenomenon of the radio frequency (RF) channel of the bcm4366 chip. Calibrating phase ambiguity (PA) is implemented by a phase detection and compensation method. Based on this, a 3-D beamspace matrix pencil (BMP) algorithm is proposed to jointly estimate the angle of arrival (AoA), time of flight (ToF), and Doppler frequency shift (DFS) of CSI dynamic targets. Finally, passive localization is achieved based on joint multiparameters. Experiments verify that the 3-D BMP algorithm is superior to the existing algorithms in terms of tradeoff estimation accuracy and computational complexity. The positioning scheme based on 3-D BMP effectively reduces the positioning error after eliminating PA. In the single-link case, the median localization errors of the activity room and meeting room scenarios reach 0.42 and 0.55 m, respectively.

关键词： multidimensional parameter estimation passive positioning phase calibration Wi-Fi channel state information (CSI)

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Phase retrieval via Lagrange programming neural network

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DIGITAL SIGNAL PROCESSING 2023年 143卷

作者： Jing, Yang Fan, Xuhui Li, Jian So, Hing Cheung Leung, Chi-Sing Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China Xian Univ Sci & Technol Sch Commun & Informat Engn Xian 710054 Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

In this paper, the problem of phase retrieval is addressed. Its solution is based on the Lagrange programming neural network (LPNN), which is an analog neural computational technique for solving nonlinear constrained optimization problems according to the Lagrange multiplier theory. The local stability of the proposed algorithm is also investigated. Furthermore, we extend the LPNN based approach to more challenging array signal processing applications, namely, when multiple vector signals with full column rank or other constraints are required to be recovered from measured magnitudes. One of the key difficulties for these challenges is pairing the separately estimated parameters, while the parameters estimated by the extended method are automatically paired. The performance of the developed algorithms is demonstrated via computer simulations.

关键词： Phase retrieval Magnitude measurement Lagrange programming neural network Karush-Kuhn-Tucker conditions multidimensional parameter estimation

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Low complexity 3D-OMP algorithms for DOD DOA and Doppler frequency estimation in bistatic MIMO radar

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INTERNATIONAL JOURNAL OF ELECTRONICS 2019年第6期106卷 816-828页

作者： Tang, Wengen Jiang, Hong Pang, Shuaixuan Jilin Univ Coll Commun Engn Nanhu Rd 5372 Changchun 130012 Jilin Peoples R China

The biggest challenge of the traditional 3D orthogonal matching pursuit (OMP) method for direction-of-departure (DOD), direction-of-arrival (DOA) and Doppler frequency estimation in bistatic multiple-input multiple-output (MIMO) radar is the heavy computational burden due to a large number of atoms in the overcomplete dictionary. In this paper, low complexity 3D-OMP algorithms are investigated. First, the traditional 3D-OMP algorithm is given. Then, two-dimensionality reduced OMP-based algorithms are proposed exploiting the property of Khatri-Rao product and proper sparse representation. Also, the multiple measurement vectors (MMV) model is introduced to our OMP algorithms to guarantee the estimation performance. The simulation results show that the DOD, DOA and Doppler frequency can be effectively estimated with a small number of pulses and low computation cost. With similar accuracy compared with the traditional 3D-OMP method, much lower computational burden can be achieved by using the proposed methods.

关键词： Multiple-input multiple-output (MIMO) radar orthogonal matching pursuit (OMP) multidimensional parameter estimation low complexity array signal processing

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multidimensional Sparse Recovery for MIMO Channel parameter estimation 24

Multidimensional Sparse Recovery for MIMO Channel Parameter ...

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24th European Signal Processing Conference (EUSIPCO)

作者： Steffens, Christian Yang, Yang Pesavento, Marius Tech Univ Darmstadt Commun Syst Grp Darmstadt Germany Intel Deutschland GmbH Feldkirchen Germany

ISBN: (纸本)9780992862657

Multipath propagation is a common phenomenon in wireless communication. Knowledge of propagation path parameters such as complex channel gain, propagation delay or angle-of- arrival provides valuable information on the user position and facilitates channel response estimation. A major challenge in channel parameter estimation lies in its multidimensional nature, which leads to large-scale estimation problems which are difficult to solve. Current approaches of sparse recovery for multidimensional parameter estimation aim at simultaneously estimating all channel parameters by solving one large-scale estimation problem. In contrast to that we propose a sparse recovery method which relies on decomposing the multidimensional problem into successive one-dimensional parameter estimation problems, which are much easier to solve and less sensitive to off-grid effects, while providing proper parameter pairing. Our proposed decomposition relies on convex optimization in terms of nuclear norm minimization and we present an efficient implementation in terms of the recently developed STELA algorithm.

关键词： MIMO Channel parameters multidimensional parameter estimation Sparse Recovery Nuclear Norm STELA

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Extrapolation of MIMO Mobile-to-Mobile Wireless Channels Using Parametric-Model-Based Prediction

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2015年第10期64卷 4487-4498页

作者： Adeogun, Ramoni O. Teal, Paul D. Dmochowski, Pawel A. Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

This paper investigates the prediction of multiple-input-multiple-output (MIMO) narrow-band multipath fading channels for mobile-to-mobile (M-to-M) wireless communication systems. Using a statistical model for M-to-M communication in urban and suburban environments, we derive a parameterized double directional model and utilize a multidimensional extension of the ESPRIT algorithm to jointly estimate the angles of departure (AoD), angles of arrival (AoA), and effective Doppler frequencies. A simple method is also proposed for mobile velocity estimation. The parameter estimates are then used to forecast the M-to-M channel. The bound on the prediction error is derived using a vector formulation of the Cramer-Rao lower bound (CRLB). Simulations are used to evaluate the performance of the prediction scheme and to compare it to the derived error bound.

关键词： Channel estimation and prediction Cramer-Rao bound estimation of signal parameters via rotational invariance techniques (ESPRIT) mobile-to-mobile (M-to-M) channel multidimensional parameter estimation multiple-input-multiple-output (MIMO) multipath fading channels

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Computation of the exact Cramer-Rao lower bound for 2-D ARMA parameter estimation - I: The quarter-plane case

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2006年第1期53卷 23-27页

作者： Kizilkaya, A Kayran, AH Istanbul Tech Univ Dept Elect & Elect Engn TR-34469 Istanbul Turkey

A closed-form expression for computing the exact Cramer-Rao lower bound (CRLB) on unbiased estimates of the parameters of a two-dimensional (2-D) autoregressive moving average (ARMA) model is developed. The formulation is based on a matrix representation of 2-D homogeneous Gaussian random process that is generated uniformly from the related 2-D ARMA model. The formulas derived for the exact Fisher information matrix (FIM) are an explicit function of the 2-D ARMA parameters and are valid for real-valued homogeneous quarter-plane (QP) 2-D ARMA random fields, where data are propagated using only the past values. It is noteworthy that our approach is practical especially for quantifying the accuracy of 2-D ARMA parameter estimates realized with short data records. Computer simulations display the behavior of the derived CRLB expression for some QP causal 2-D ARMA processes, as a function of the number of data points. The extension of this algorithm for the nonsymmetric half-plane (NSHP) case will be presented in a subsequent paper.

关键词： Cramer-Rao lower bound (CRLB) Fisher information matrix (FIM) multidimensional parameter estimation quarter-plane (QP) two-dimensional (2-D) autoregressive moving average (ARMA) model real-valued homogeneous random fields

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A RECURSIVE ALGORITHM FOR COMPUTING CRAMER-RAO-TYPE BOUNDS ON ESTIMATOR COVARIANCE

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IEEE TRANSACTIONS ON INFORMATION THEORY 1994年第4期40卷 1205-1210页

作者： HERO, A FESSLER, JA UNIV MICHIGAN DIV NUCL MEDANN ARBORMI 48109

We give a recursive algorithm to calculate submatrices of the Cramer-Rao (CR) matrix bound on the covariance of any unbiased estimator of a vector parameter theta. Our algorithm computes a sequence of lower bounds that converges monotonically to the CR bound with exponential speed of convergence. The recursive algorithm uses an invertible ''splitting matrix'' to successively approximate the inverse Fisher information matrix. We present a statistical approach to selecting the splitting matrix based on a ''complete-data-incomplete-data'' formulation similar to that of the well-known EM parameter estimation algorithm. As a concrete illustration we consider image reconstruction from projections for emission computed tomography.

关键词： multidimensional parameter estimation ESTIMATOR COVARIANCE BOUNDS COMPLETE INCOMPLETE DATA PROBLEM IMAGE RECONSTRUCTION

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