Widely linear minimum dispersion beamforming for sub-Gaussian noncircular signals

Huang, Lei; Zhang, Jing; Zhang, Long; Ye, Zhongfu

doi:10.1016/j.sigpro.2015.10.012

Cited by 18 publications

(15 citation statements)

References 24 publications

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“…Note that although the couple (w f , B 1,0 ) satisfying w H f s = 1 and span(B 1,0 ) = span(s) ⊥ , is not unique, the SINR given by (44) and (45) does not depend on this choice. Moreover, denoting by SINR B , the SINR at the output of the MVDR beamformer B, the following inequalities can be deduced from the inclusion principle applied to the constrained minimization (29):…”

Section: Output Sinrmentioning

confidence: 99%

“…Under these assumptions, Fig.7 shows, for a non-filtered QPSK SOI and a non-filtered BPSK interference, the variations, as a function of K, of the estimated mean value of SINR(K), E(SINR(K)), computed over 1000 runs, at the output of several beamformers proposed in the paper or borrowed from the literature and corresponding to the Capon, MDDR [43], WL-MVDR [9], WL-MMSE [13], WL-MDDR [44], L-C(0), L-C(1,3) and WL-C(0) beamformers. Fig.7 shows that for the considered scenario, the MDDR beamformer is not faster than the Capon beamformer, while the WL MDDR beamformer is slightly faster than the WL MVDR beamformer but not faster than the WL MMSE beamformer.…”

Section: Adaptive Implementationmentioning

confidence: 99%

“…Note that the FO non-circularity of observations has been used by a WL MMSE beamformer in [42] to compensate I/Q imbalance effects at reception but not to improve the steady-state performance of WL beamformers. In addition, the non-Gaussiannity and both the sub-Gaussiannity and non-circularity of observations has already been used in [43] and [44], respectively, through the development of the linear minimum dispersion beamformer (MDB) and the WL MDB respectively, to boost the convergence speed of linear and WL beamformers respectively, but not to improve their steady-state performance. Finally note that some preliminary results of the paper have been presented in [45], but without any complexity and analytical performance analysis and with no comparison with nonlinear beamformers of the literature.…”

Section: Introductionmentioning

confidence: 99%

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Third-Order Volterra MVDR Beamforming for Non-Gaussian and Potentially Non-Circular Interference Cancellation

Chevalier

Delmas

Sadok

2018

IEEE Trans. Signal Process.

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Linear beamformers are optimal, in a mean square (MS) sense, when the signal of interest (SOI) and observations are jointly Gaussian and circular. Otherwise, linear beamformers become sub-optimal. When the SOI and observations are zero-mean, jointly Gaussian and non-circular, optimal beamformers become widely linear (WL). They become non-linear with a structure depending on the unknown joint probability distribution of the SOI and observations when the latter are jointly non-Gaussian, assumption which is very common in radiocommunications. In this context, the paper aims at introducing, for small-scale systems, third-order Volterra minimum variance distortionless response (MVDR) beamformers, for the reception of a SOI, whose waveform is unknown but whose steering vector is known, corrupted by non-Gaussian and potentially non-circular interference, omnipresent in practical situations. Properties, performance, complexity and adaptive implementation of these beamformers in the presence of non-Gaussian and potentially non-circular interference are analyzed in this paper. These new beamformers are shown to always improve, in the steady state, the performance of Capon beamformer for non-gaussian/circular interference, whereas some of them improve the performance of the WL MVDR beamformer for non-Gaussian/non-circular interference. These new beamformers open new perspectives for spectrum monitoring of non-Gaussian signals and for radiocommunication networks using such signals.

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Section: Output Sinrmentioning

confidence: 99%

Section: Adaptive Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Third-Order Volterra MVDR Beamforming for Non-Gaussian and Potentially Non-Circular Interference Cancellation

Chevalier

Delmas

Sadok

2018

IEEE Trans. Signal Process.

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“…Observing the sets LC and WL consisting of weight vectors constrained by (9) and (15), we can see that they both minimize the cost functionw HR xw while satisfying…”

Section: Output Performancementioning

confidence: 99%

Constrained Widely Linear Beamforming Antijammer Technique for Satellite-Borne Distributed Sensor Data Acquisition System

Wang

Zhao

et al. 2016

International Journal of Distributed Sensor Networks

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Widely linear (WL) adaptive beamforming algorithms are proposed based on constrained minimum variance (CMV) and constrained constant modulus (CCM) criteria, respectively, to fully exploit both the desired signal and interferences' noncircularity. Modified conjugate gradient (MCG) algorithm is employed to ensure convergence with one iteration per sample. To further facilitate the adaptive processing, CCM criterion based WL beamforming algorithm is modified by second-order local approximation. Global convergence of the proposed algorithms is analyzed and constraints of parameters are given. Results of numerical simulations demonstrate that the proposed WL CCM beamformer has superior performance over WL CMV beamformer and conventional beamformers in convergence rate, robustness against direction of arrival (DOA) mismatch, and the noncircularity coefficient estimation error, while the former two have comparable low complexities without numerical instability.

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“…However, in a practical application, the accurate SV and the precise noncircularity coefficient of the desired signal are unavailable. To address this problem, some robust WL beamforming algorithms have been proposed in [8], [10], [12]- [15]. In [13], Wang's method can deal with the uncertainties in the desired signal's SV and noncircularity coefficient, but it is sensitive to the large mismatch of noncircularity coefficient.…”

Section: Introductionmentioning

confidence: 99%

Robust Widely Linear Beamforming via an IAA Method for the Augmented IPNCM Reconstruction

Liu

Gui²,

Xie

et al. 2017

IEICE Trans. Fundamentals

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Based on the reconstruction of the augmented interferenceplus-noise (IPN) covariance matrix (CM) and the estimation of the desired signal's extended steering vector (SV), we propose a novel robust widely linear (WL) beamforming algorithm. Firstly, an extension of the iterative adaptive approach (IAA) algorithm is employed to acquire the spatial spectrum. Secondly, the IAA spatial spectrum is adopted to reconstruct the augmented signal-plus-noise (SPN) CM and the augmented IPNCM. Thirdly, the extended SV of the desired signal is estimated by using the iterative robust Capon beamformer with adaptive uncertainty level (AU-IRCB). Compared with several representative robust WL beamforming algorithms, simulation results are provided to confirm that the proposed method can achieve a better performance and has a much lower complexity.

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Widely linear minimum dispersion beamforming for sub-Gaussian noncircular signals

Cited by 18 publications

References 24 publications

Third-Order Volterra MVDR Beamforming for Non-Gaussian and Potentially Non-Circular Interference Cancellation

Third-Order Volterra MVDR Beamforming for Non-Gaussian and Potentially Non-Circular Interference Cancellation

Constrained Widely Linear Beamforming Antijammer Technique for Satellite-Borne Distributed Sensor Data Acquisition System

Robust Widely Linear Beamforming via an IAA Method for the Augmented IPNCM Reconstruction

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