Quadrilinear Decomposition-Based Blind Signal Detection for Polarization Sensitive Uniform Square Array

Shi, Ying; Zhang, Xiaofei

doi:10.2528/pier08102005

Cited by 15 publications

(24 citation statements)

References 37 publications

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“…In this system, each antenna is weighted by a properly designed gain and phase shift before transceiving. Polarization sensitive antenna arrays have some inherent advantages over traditional arrays [1]- [3], because they can separate signals based on their polarization characteristics [4], [5]. It is extensively used to improve the performance in a variety of areas such as radio, navigation, and wireless communications [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

Joint polarized and spatial domain anti-jamming method for GNSS

Zeng

Wang

et al. 2013

2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013)

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Because of the vulnerability of navigation signal, adaptive array processing plays a significant role in global navigation satellite system (GNSS) receivers. However, when the direction of arrival (DOA) of the desired signal coincides with that of the interference, the desired signal is mitigated which leads to a degradation of system performance. This paper utilizes antenna array's polarization information and proposes a novel joint polarized and spatial anti-jamming (JPSA) method based on linearly constrained minimum variance (LCMV) criterion. Unlike traditional LCMV criterion , we propose a constraint matrix consisting of the steering vector of desired signal and interference, which can suppress the interference and enhance the desired signal effectively. Simulations in single and multiple interference scenarios show that the proposed method suppresses interference successfully while the DOA of desired signal is the same with that of interference.

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Section: Introductionmentioning

confidence: 99%

Joint polarized and spatial domain anti-jamming method for GNSS

Zeng

Wang

et al. 2013

2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013)

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“…Therefore, (17) is the constrained least square solution of (16), and the following inequality is always satisfied…”

Section: Convergencementioning

confidence: 99%

“…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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“…The polarization can be considered as the fourth dimension and hence a four-way description happens to fit better as long as the targets have diverse polarizations. Thus, PARAFAC quadrilinear decomposition [22,23] with quadrilinear alternating least square (QALS) is applied to multidimensional parameter estimation in polarimetric bistatic MIMO radar.…”

Section: Introductionmentioning

confidence: 99%

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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Quadrilinear Decomposition-Based Blind Signal Detection for Polarization Sensitive Uniform Square Array

Cited by 15 publications

References 37 publications

Joint polarized and spatial domain anti-jamming method for GNSS

Joint polarized and spatial domain anti-jamming method for GNSS

Constrained Trilinear Decomposition With Application to Array Signal Processing

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

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