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Tensor approximation under existence constraint. Application to antenna array processing

TRAITEMENT DU SIGNAL 2016年第1期33卷 11-33页

作者： Sahnoun, Souleymen Comon, Pierre Gipsa Lab 11 Rue MathDomaine UnivBP 46 F-38402 St Martin Dheres France

The subject is localization and estimation of sources in difficult conditions, namely when the sources are correlated and closely located in space, and samples are short. The proposed algorithm is based on a low-rank tensor approximation under original constraints ensuring its existence. It requires an antenna array formed of identical subarrays shifted in space. Performance bounds are computed in the presence of additive complex noncircular Gaussian noise.

关键词： source localization tensors diversity coherence antenna array processing low-rank approximation complex Cramer-Rao bounds non circularity

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Deep neural network based anti-jamming processing in the GNSS array receiver

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GPS SOLUTIONS 2025年第2期29卷 1-15页

作者： Xie, Yuchen Chen, Lei Zhang, Ke Huang, Xinming Peng, Jing Ni, Shaojie Natl Univ Def Technol Coll Elect Sci Changsha 410073 Peoples R China Key Lab Satellite Nav Technol Changsha 410073 Peoples R China

Signal anti-jamming has always been a difficult problem in GNSS (global navigation satellite system) signal processing. There are many GNSS anti-jamming techniques in the existing research, which can achieve good results if the interferences are sparsely distinguishable in some signal feature domains. Specifically, the single antenna based anti-jamming techniques cannot deal with wideband Gaussian noise interference because it is not sparse in time or frequency domain, while the only effective method currently is using multiple antennas to apply the space array processing (SAP) technique since the wideband Gaussian noise interference is sparse in the spatial domain. However, when the incoming directions of the different interferences are not less than that of antennas, the interferences are not sparse to the array anymore, and the SAP anti-jamming performance would decrease drastically. In this paper, a LSTM (long short-term memory) deep neural network (DNN) based algorithm is proposed to enhance the array anti-jamming performance in this situation. The proposed network estimates the interferences as an integrity by exploring the non-linear relationship of the array data received by antennas. Especially, a new loss function is designed exclusively for GNSS anti-jamming problem. The proposed DNN method is verified in the simulation that two wideband Gaussian interferences with JSR (jamming to signal ratio) 50 dB can be eliminated by using two antennas' data, and the interference cancellation ratio improvement is about 24 dB compared to some other widely used classical SAP algorithms.

关键词： GNSS Deep neural network antenna array processing Anti-jamming

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Low-Rank EM-Based Imaging for Large-Scale Switched Interferometric arrays

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IEEE SIGNAL processing LETTERS 2025年 32卷 41-45页

作者： Wang, Jianhua El Korso, Mohammed Nabil Bacharach, Lucien Larzabal, Pascal Univ Paris Saclay SATIE F-91190 Gif Sur Yvette France Univ Paris Saclay L2S F-91190 Gif Sur Yvette France

Interferences and computational cost pose significant challenges in large-scale interferometric sensing, impacting the accuracy and numerical efficiency of imaging algorithms. In this letter, we introduce an imaging algorithm using antenna switching based on expectation-maximization (EM) to address both challenges. By leveraging the low-rank noise model, our approach effectively captures interferences in interferometric data. Additionally, the proposed switching strategy between different sub-arrays reduces significantly the computational complexity during image restoration. Through extensive experiments on simulated datasets, we demonstrate the superiority of the low-rank noise model over the Gaussian noise model in the presence of interferences. Furthermore, we show that the proposed switching approach yields similar imaging performance with fewer antennas compared to the full array configuration, thereby reducing computational complexity, while outperforming non-switching configurations with the same number of antennas.

关键词： Imaging Switches antennas Computational modeling antenna arrays Vectors Signal processing algorithms Radio interferometry Correlation Computational complexity antenna array processing Barankin bound Cramer-Rao bound EM algorithm interferometric array

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RFI-Aware and Low-Cost Maximum Likelihood Imaging for High-Sensitivity Radio Telescopes

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IEEE SIGNAL processing LETTERS 2024年 31卷 2960-2964页

作者： Wang, J. El Korso, M. N. Bacharach, L. Larzabal, P. Univ Paris Saclay SATIE F-91190 Gif Sur Yvette France Univ Paris Saclay L2S F-91190 Gif Sur Yvette France

This paper addresses the challenge of interference mitigation and reduction of computational cost in the context of radio interferometric imaging. We propose a novel maximum-likelihood-based methodology based on the antenna sub-array switching technique, which strikes a refined balance between imaging accuracy and computational efficiency. In addition, we tackle robustness regarding radio interference by modeling the additive noise as t-distributed. Through simulation results, we demonstrate the superiority of the t-distributed noise model over the conventional Gaussian noise model in scenarios involving interferences. We evidence that our proposed switching approach yields similar imaging performances with far fewer visibilities compared to the full array configuration, thus, diminishing the computational complexity.

关键词： Noise Imaging Vectors antennas Computational modeling antenna measurements antenna arrays Switches Signal processing algorithms Sensors antenna array processing expectation-maximization algorithm interferometry radio-astronomical imaging

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Adaptive Approximate Kernel Orthogonalization for antenna array processing

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IEEE TRANSACTIONS ON antennaS AND PROPAGATION 2013年第8期61卷 4091-4100页

作者： Navia-Vazquez, Angel Martinez-Ramon, Manel Garcia-Munoz, Luis E. Christodoulou, Christos G. Univ Carlos III Madrid Dept Signal Theory & Commun Madrid 28911 Spain Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA

In a previous paper we have presented a kernel method for antenna array processing using gaussian kernels as basis functions (Approximated Kernel Orthogonalization, AKO) and shown that it presents near optimal performance with an affordable computational burden and small size of the resulting model. However, AKO was only able to operate in batch mode, i.e., it had to process chunks of data (between 10 and 30 frames), which implies a delay in the system response and inability to rapidly respond to changes in the environment. We present here an adaptive extension, the so called Adaptive Approximate Kernel Orthogonalization (AAKO) Method, that is able to on-line process the received frames without loosing performance with respect to the batch version and without any delay in the output, which opens up the possibility of real time implementations.

关键词： Adaptivity antenna array processing kernel method Mercer's kernels non-stationary support vector machines

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A Novel Blind Adaptive Beamformer with Robustness against Mutual Coupling and Miscalibration Effects 17

A Novel Blind Adaptive Beamformer with Robustness against Mu...

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17th European Conference on antennas and Propagation (EuCAP)

作者： Yagan, M. Yaser Coskun, Ahmet F. Pusane, Ali E. TUBITAK Informat & Informat Secur Res Ctr BILGE HISAR Lab Kocaeli Turkiye Bogazici Univ Dept Elect & Elect Engn Istanbul Turkiye

ISBN: (纸本)9788831299077

Beamforming techniques utilized either at the transmitter or the receiver terminals have achieved superior quality-of-service performances from both the multi-antenna wireless communications systems, communications intelligence and radar target detection perspectives. Despite the overwhelming advantages in ideal operating conditions, beamforming approaches have been shown to face substantial performance degradations due to unknown mutual coupling effects and miscalibrated array elements. As a promising solution, blind beamformers have been proposed as a class of receiver beamformers that do not require a reference signal to operate. In this paper, a novel gradient-based blind beamformer is introduced with the aim of mitigating the deteriorating effects of unknown mutual coupling or miscalibration effects. The proposed approach is shown to find the optimal weights in different antenna array configurations in the presence of several unknown imperfections (e.g., mutual coupling effects, miscalibration effects due to gain and phase variations, inaccurate antenna positions). By providing numerical results related to the proposed algorithm for different array configurations, and bench-marking with the other existing approaches, the proposed scheme has been shown to achieve superior performance in many aspects. Additionally, a measurement-based analysis has been included with validation purposes.

关键词： antenna array processing receive beamforming mutual coupling array calibration imperfections

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ON LEAST SQUARES PROBLEMS WITH CERTAIN VANDERMONDE-KHATRI-RAO STRUCTURE WITH APPLICATIONS TO DMD

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2020年第5期42卷 A3250-A3284页

作者： Drmac, Zlatko Mezic, Igor Mohr, Ryan Univ Zagreb Fac Sci Dept Math Zagreb 10000 Croatia Univ Calif Santa Barbara Dept Mech Engn & Math Santa Barbara CA 93106 USA AIMdyn Inc Santa Barbara CA 93101 USA

This paper proposes a new computational method for solving the structured least squares problem that arises in the process of identification of coherent structures in dynamic processes, such as, e.g., fluid flows. It is deployed in combination with dynamic mode decomposition (DMD), which provides a nonorthogonal set of modes corresponding to particular temporal frequencies. A selection of these is used to represent time snapshots of the underlying dynamics. The coefficients of the representation are determined from a solution of a structured linear least squares problems with the matrix that involves the Khatri-Rao product of a triangular and a Vandermonde matrix. Such a structure allows for a very efficient normal equation based least squares solution, which is used in state-of-the-art computational fluid dynamics (CFD) tools, such as the sparsity promoting DMD (DMDSP). A new numerical analysis of the normal equations approach provides insights about its applicability and its limitations. Relevant condition numbers that determine numerical robustness are identified and discussed. Further, the paper offers a corrected seminormal solution and the QR factorization based algorithms. It shows how to use the Vandermonde-Khatri-Rao structure to efficiently compute the QR factorization of the least squares coefficient matrix, thus providing a new computational tool for the ill-conditioned cases where the normal equations may fail to compute a sufficiently accurate solution. Altogether, the presented material provides a firm numerical linear algebra framework for a class of structured least squares problems arising in a variety of applications besides the DMD, such as, e.g., multistatic antenna array processing.

关键词： antenna array processing coherent structure dynamic mode decomposition Khatri-Rao product Koopman operator Krylov subspaces proper orthogonal decomposition Rayleigh-Ritz approximation scattering coefficients structured least squares Vandermonde matrix

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A WHITENESS TEST BASED ON THE SPECTRAL MEASURE OF LARGE NON-HERMITIAN RANDOM MATRICES

A WHITENESS TEST BASED ON THE SPECTRAL MEASURE OF LARGE NON-...

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IEEE International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Bose, A. Hachem, W. Indian Stat Inst Kolkata India Univ Gustave Eiffel ESIEE Paris LIGM CNRS F-77454 Marne La Vallee France

ISBN: (纸本)9781509066315

In the context of multivariate time series, a whiteness test against an MA(1) correlation model is proposed. This test is built on the eigenvalue distribution (spectral measure) of the non-Hermitian one-lag sample autocovariance matrix, instead of its singular value distribution. The large dimensional limit spectral measure of this matrix is derived. To obtain this result, a control over the smallest singular value of a related random matrix is provided. Numerical simulations show the excellent performance of this test.

关键词： antenna array processing Large non-Hermitian matrix theory Limit spectral distribution Smallest singular value Whiteness test in multivariate time series

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Generalized cablibration of model errors for uniform linear arrays

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MEASUREMENT SCIENCE AND TECHNOLOGY 2020年第11期31卷 115101-115101页

作者： Tian, Ye Rong, Xiaoliu Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Hebei Key Lab Informat Transmiss & Signal Proc Qinhuangdao 066004 Hebei Peoples R China

A generalizedcalibration technique for model errors is proposed for uniform linear arrays. Being different from the existing calibration methods, this technique constructs covariance matrices for two adjacent sampling periods, from a reference source with a known location, and then estimates the model errors from the difference between these matrices and an equation formed by using the MUSIC null space. The proposed technique can not only calibrate different types of model errors (such as gain-phase errors, geometry errors and mutual coupling) in different impinging source scenarios, but also has great robustness to different types of background noise. Meanwhile, it is computationally efficient and does not require an accurate clock synchronization between the reference source and the array receiving system. However, it requires that the statistical properties of both signal and noise must be constant in the two adjacent sampling periods. Numerical examples validate the superiority and effectiveness of the proposed technique.

关键词： array model errors calibration gain-phase errors mutual coupling array geometry errors antenna array processing

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Wideband Multiple Diversity Tensor array processing

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IEEE TRANSACTIONS ON SIGNAL processing 2017年第20期65卷 5334-5346页

作者： Raimondi, Francesca E. D. Farias, Rodrigo Cabral Michel, Olivier J. Comon, Pierre Univ Grenoble Alpes GIPSA Lab CNRS F-38000 Grenoble France Univ Cote Azur CNRS I3S F-06900 Sophia Antipolis France

This paper establishes a tensor model for wideband coherent array processing including multiple physical diversities. A separable coherent focusing operation is proposed as a preprocessing step in order to ensure the multilinearity of the interpolated data. We propose an alternating least squares algorithm to process tensor data, taking into account the noise correlation structure introduced by the focusing operation. We show through computer simulations that the estimation of direction of arrival and polarization parameters improves compared to existing narrowband tensor processing and wideband MUltiple SIgnal Classification. The performance is also compared to the Cramer-Rao bounds of the wideband tensor model.

关键词： Localization DoA estimation interpolation coherent wideband antenna array processing tensor MUSIC high resolution polarization CP decomposition

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