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

Jiang, Hong; Zhang, Yu; Li, Jia; Cui, Haijing

doi:10.1186/1687-6180-2013-133

Cited by 16 publications

(17 citation statements)

References 28 publications

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“…In addition, it has low complexity because of the absence of angle searching [13]. Reference [14] introduces parallel factor analysis to polarimetric MIMO radar, which can improve spatial resolution and achieve the goal of three-dimensional positioning. Note that the cross-dipole antennas are used in the above two papers.…”

Section: International Journal Of Antennas and Propagationmentioning

confidence: 99%

Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

Tang

2016

International Journal of Antennas and Propagation

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We study two-dimensional direction of arrival (2D-DOA) estimation problem of monostatic MIMO radar with the receiving array which consists of electromagnetic vector sensors (EMVSs). The proposed angle estimation algorithm can be applied to the arbitrary and unknown array configuration, which can be summarized as follows. Firstly, EMVSs in the receiver of a monostatic MIMO radar are used to measure all six electromagnetic-field components of an incident wavefield. The vector sensor array with the six unknown electromagnetic-field components is divided into six spatially identical subarrays. Secondly, ESPRIT is utilized to estimate the rotational invariant factors (RIFs). Parts of the RIFs are picked up to restore the source’s electromagnetic-field vector. Last, a vector cross product operation is performed between electric field and magnetic field to obtain the Pointing vector, which can offer the 2D-DOA estimation. Prior knowledge of array elements’ positions and angle searching procedure are not necessary for the proposed 2D-DOA estimation method. Simulation results prove the validity of the proposed method.

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Section: International Journal Of Antennas and Propagationmentioning

confidence: 99%

Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

Tang

2016

International Journal of Antennas and Propagation

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“…Though earlier practices motivated the use of vector/matrix-based techniques to characterise higherorder data, more recently tensor decomposition methods have found to be well equipped to capture the internal associations across different modes [23]. This is why tensor decomposition methods found a number of applications in various signal processing areas including signal separation, parameter estimation [26][27][28], dimensionality reduction [29], feature extraction and classification [30].…”

Section: Introductionmentioning

confidence: 99%

Rectangular array of electromagnetic vector sensors: tensor modelling/decomposition and DOA‐polarisation estimation

Ahmed

Zhang

Wang

et al. 2019

IET signal process.

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In this study, the authors propose a fast quadrilinear decomposition algorithm for estimation of the directions-of-arrival and polarisations of the incident sources via a uniform rectangular array of electromagnetic vector sensors (EMVSs). Conventional quadrilinear alternating least squares (QALS), involves computationally intensive Khatri-Rao products in each iteration, to update the parameter matrices (factors). Moreover, QALS is more likely to fall in a local minimum and tends to take more steps before an acceptable solution, which further slows down the convergence and often mis-converges, thereby yielding meaningless results. To preserve the quadrilinearity, they arrange the measurements as a four-dimensional (4D) data (fourthorder tensor), from which a third-order sub-tensor (3D slice) can be obtained by fixing one index along any dimension. These slices are used to create new cost functions that are alternately minimised while updating the factors until convergence. They show that the rows of parameter matrices form the diagonal elements of a tensor, which capture the internal quadrilinearity of data and significantly reduce the cost function in few iterations only. Simulation results verify that the authors' algorithm holds faster convergence, does not mis-converge, provides parameter estimation accuracy similarly to the QALS and superior of the Estimation of Signal Parameters via Rotational Invariance Technique and propagator method.

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“…Zheng et al [5] show the joint DOA and polarisation estimations for monostatic polarimetric MIMO radar based on ESPRIT algorithm. Jiang et al [6] show parallel factor analysis method for a polarimetric MIMO radar system. The authors in [7,8] extend crossed-dipole vector to the six components electromagnetic vector sensor, and then use traditional vector cross-product and polarisation smoothing to estimate the DOA and (or) polarisation of a polarimetric MIMO radar system.…”

Section: Introductionmentioning

confidence: 99%

BOMP‐based angle estimation with polarimetric MIMO radar with spatially spread crossed‐dipole

Zhang

2018

IET signal process.

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For polarimetric multi-input multi-output (MIMO) radar with spatially spread crossed-dipole, this article studies the problem of joint direction of arrival (DOA) and polarisation parameter estimation based on block-orthogonal matching pursuit (BOMP) algorithm. First, the signal model of polarimetric MIMO radar with spatially spread crossed-dipole is established, and then the covariance matrix of the received data is calculated. Using the relationship between polarisation parameter and DOA in the crossed-dipole, sparse dictionary matrix is constructed within only DOA parameter and it will be translated into a block sparse problem. Then, fast BOMP algorithm is used to estimate their support positions and their amplitudes. Last, DOA estimation is calculated by support positions and polarisation parameter is estimated by the amplitudes of the support positions. The proposed algorithm has three advantages. One is that overcomplete dictionary is constructed within only the DOA, which has a small computational complexity. Another one is that the problem of strong mutual coupling among collocated crosseddipole is solved by using the spatially spread crossed-dipole. The last one is that the DOA and polarisation estimations can pair automatically without any additional processing. Computer simulation results demonstrate the effectiveness of the proposed algorithm. 2 Signal model of polarimetric MIMO radar with spatially spread crossed-dipole A single spatially spread crossed-dipole is shown in Fig. 1, which electric dipole Ex is placed along with the x axis and electric dipole

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

Cited by 16 publications

References 28 publications

Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

Rectangular array of electromagnetic vector sensors: tensor modelling/decomposition and DOA‐polarisation estimation

BOMP‐based angle estimation with polarimetric MIMO radar with spatially spread crossed‐dipole

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