Trilinear decomposition-based transmit angle and receive angle estimation for multiple-input multiple-output radar

Zhang, X.; Zong-ze, XU; Xu, Liang; Xu, Dazhuan

doi:10.1049/iet-rsn.2010.0265

Cited by 136 publications

(149 citation statements)

References 23 publications

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“…The definition of PARAFAC quadrilinear decomposition model can be derivatively described from the trilinear decomposition model [19][20][21]. …”

Section: Parafac Quadrilinear Decomposition Model and Uniqueness Theoremmentioning

confidence: 99%

“…In recent years, PARAFAC has become a new research means in MIMO radar [19][20][21]. The PARAFAC analysis algorithms [19,20] and adaptive PARAFAC algorithm [21] have been developed for the estimation of DOAs and DODs of multiple targets.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the fact that several PARAFAC-based methods have been proposed for DOA and DOD estimation in bistatic MIMO radars, trilinear decomposition algorithms [17][18][19][20][21] with trilinear alternating least square (TALS) are commonly used to fit PARAFAC model. In this article, we investigate joint estimation of DOD, DOA, Doppler frequency and polarization.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Zhang

et al. 2013

EURASIP J. Adv. Signal Process.

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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.

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“…The definition of PARAFAC quadrilinear decomposition model can be derivatively described from the trilinear decomposition model [19][20][21]. …”

Section: Parafac Quadrilinear Decomposition Model and Uniqueness Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jiang

Zhang

et al. 2013

EURASIP J. Adv. Signal Process.

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“…Also, bistatic MIMO radar has the particular advantage of being able to obtain the target angles with respect to the transmit array (direction of departure) by processing the received data [3]. Several publications have studied direction of departure and direction of arrival estimation for bistatic MIMO radar [5][6][7][8][9]. Multiple target localization without range information can be achieved by using the estimated angles.…”

Section: Introductionmentioning

confidence: 99%

Multiple target three-dimensional coordinate estimation for bistatic MIMO radar with uniform linear receive array

Long

et al. 2013

EURASIP J. Adv. Signal Process.

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A novel scheme to achieve three-dimensional (3D) target location in bistatic radar systems is evaluated. The proposed scheme develops the additional information of the bistatic radar, that is the transmit angles, to estimate the 3D coordinates of the targets by using multiple-input multiple-output techniques with a uniform circular array on transmit and a uniform linear array on receive. The transmit azimuth, transmit elevation angles and receive cone angle of the targets are first extracted from the receive data and the 3D coordinates are then calculated on the basis of these angles. The geometric dilution of precision which is based on the root Cramer-Rao bound of the angles, is derived to evaluate the performance bound of the proposed scheme. Further, an ESPRIT based algorithm is developed to estimate the 3D coordinates of the targets. The advantages of this scheme are that the hardware of the receive array is reduced and the 3D coordinates of the targets can be estimated in the absence of the range information in bistatic radar. Simulations and analysis show that the proposed scheme has potential to achieve good performance with low-frequency radar.

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“…They applied PARAFAC model to estimate the azimuth and elevation angles from different sources in a uniform square array. Beamforming [14,15], polarization sensitive array processing [16][17][18] and MIMO radar location [19,20] have also been linked to trilinear analysis. The common characteristic of tensor modeling approach in these applications is that baseband signals and array response vectors, which are always involved in data model, are treated as pure 'double-precision' or 'complex' data during trilinear decomposition.…”

Section: Introductionmentioning

confidence: 99%

Constrained Trilinear Decomposition With Application to Array Signal Processing

Liu¹,

Jiang²,

Yang³

et al. 2012

PIER

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Abstract-This paper links the constrained trilinear tensor model into array signal processing. The structure properties of baseband signal, such as the Constant-Modulus (CM) and Finite Alphabet (FA) structures which are already known in the receiving array, are exploited in trilinear decomposition. Two novel algorithms for constrained trilinear decomposition are proposed and applied to array signal processing. The distinguishing features of the proposed model and algorithms compared to the traditional trilinear signal processing methods are: (i) the proposed model has a better performance and lower computation complexity. (ii) it can still work well even if degeneracy of factors are involved in the data model, which is not valid in traditional algorithms. Simulation results are presented to illustrate the application of the constrained trilinear decomposition to array signal processing and evaluate the performance of the proposed algorithms in DOAs estimation.

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Trilinear decomposition-based transmit angle and receive angle estimation for multiple-input multiple-output radar

Cited by 136 publications

References 23 publications

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

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

Multiple target three-dimensional coordinate estimation for bistatic MIMO radar with uniform linear receive array

Constrained Trilinear Decomposition With Application to Array Signal Processing

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