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Robust Adaptive Beamforming of LFM signals Based on Interference-plus-Noise Covariance Matrix Reconstruction in Fractional Fourier Domain

Robust Adaptive Beamforming of LFM Signals Based on Interfer...

引用

sensor array and multichannel signal processing workshop

作者： Jian Lu Jian Yang Xinxin Liu Rocket Force University of Engineering Xi’an China School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

The linear frequency modulation (LFM) signal can accumulate as an impulse by the fractional Fourier transform (FRFT), and the phase differences among the peaks on the sensors mainly depend on the direction of arrival of the LFM signal and the antenna structure. A novel robust adaptive beamforming is proposed by the interference-plus-noise covariance matrix (INCM) reconstruction in fractional Fourier domain. The FRFT is applied to the radar echoes and then the peaks can be extracted to reconstruct the INCM with the Capon spatial spectrum. The optimal weight vector can be obtained by solving the optimization problem. Simulation results demonstrate that the proposed method can efficiently suppress the deception interferences of false targets, and outperform other tested beamformers across a wide range of signal-to-noise ratios.

关键词： array signal processing Simulation Radar Radar antennas sensors Covariance matrices Time-domain analysis

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Sparse array and Precoding Design for Integrated Sensing and Communication Systems

Sparse Array and Precoding Design for Integrated Sensing and...

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sensor array and multichannel signal processing workshop

作者： R.S. Prasobh Sankar Sundeep Prabhakar Chepuri Indian Institute of Science Bengaluru Karnataka India

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper, we perform joint antenna selection and transmit pre-coder design for integrated sensing and communication (ISAC) systems to meet signal-to-interference-plus-noise-ratio (SINR) requirements at the users while being capable of identifying, i.e., estimating the direction of arrivals (DoAs), of certain number of sources. We first present a sufficient condition to ensure certain identifiability. Next, through a series of relaxations, we obtain a convex approximation to the combinatorial antenna selection and precoding problem, which we solve using off-the-shelf solvers. The proposed method offers comparable performance to ISAC systems with optimal antenna selection obtained through exhaustive search while significantly out-performing ISAC systems with arbitrarily selected active antennas.

关键词： Sufficient conditions Direction-of-arrival estimation array signal processing Transmitting antennas Interference Integrated sensing and communication Radar antennas

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Exploiting order statistic filter in the processing of ultrasonic sensor array signals

Exploiting order statistic filter in the processing of ultra...

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ieee Winter workshop on Nonlinear Digital signal processing

作者： R. Suoranta Machine Automation Laboratory Technical Research Centre of Finland (VTT) Tampere Finland

A novel method to apply Order Statistic Filtering to process ultrasonic sensor array signals is presented. Proposed approach performs a beamforming operation by combining signals from different channels and at the same time performing a detection of signal. Proposed beamforming method makes it possible to realize beamforming systems also with airborne ultrasonics. More over, the new robust method will enhance the detection accuracy and resolution in time-of-flight based sonar systems. The method has already been applied to a prototype of new ultrasonic ranging system.

关键词： sensor arrays Statistics signal processing array signal processing signal detection Information filtering Information filters Sonar detection signal resolution Mobile robots

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Optimization Inspired Learning Network for Multiuser Robust Beamforming

Optimization Inspired Learning Network for Multiuser Robust ...

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sensor array and multichannel signal processing workshop

作者： Minghe Zhu Tsung-Hui Chang School of Science & Engineering The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

For real-time wireless networks with strict latency and energy constraints, deep neural networks have been used to approximate the resource allocation solutions that are previously obtained by advanced but computationally expensive optimization algorithms. In this paper, we consider the multi-user beamforming design problem for sum rate maximization in multi-antenna interference channels. Specifically, we propose a gradient projection inspired recurrent neural network for efficient beamforming optimization. The key ingredient is to explore the structure of the gradient vector of the sum rate so that the network learns only a set of dimension reduced coefficients. Furthermore, we extend it to the robust beamforming design for worst-case sum rate maximization in the presence of bounded channel errors. Numerical results show that the proposed learning networks can achieve high accuracy of the sum rates while with significantly reduced runtime.

关键词： Runtime Recurrent neural networks array signal processing Wireless networks signal processing algorithms Real-time systems Interference channels

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An Efficient Optimization Framework for Learning General Signed Graphs From Smooth signals

An Efficient Optimization Framework for Learning General Sig...

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sensor array and multichannel signal processing workshop

作者： Shi-Yuk Fong Anthony Man-Cho So Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Graph learning has been widely used in many fields to study the relationships between different entities in a dataset. We present an optimization framework based on the proximal alternating direction method of multipliers (pADMM) for learning general signed graphs from smooth signals. We show that our proposed pADMM enjoys global convergence and a local linear convergence rate. Then, we demonstrate the effectiveness of the proposed framework through numerical experiments on signed graphs. Our proposed framework provides a promising approach for learning general signed graphs from smooth signals and can be a valuable tool for data analysis and decision-making in various fields.

关键词： Data analysis array signal processing Conferences Decision making Optimization Convergence sensor arrays

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POPLAR: Parafac2 decOmPosition using auxiLiAry infoRmation

POPLAR: Parafac2 decOmPosition using auxiLiAry infoRmation

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sensor array and multichannel signal processing workshop

作者： Ekta Gujral Georgios Theocharous Evangelos E. Papalexakis UC Riverside Adobe Inc

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

PARAFAC2 is a powerful method for analyzing multi-modal data consisting of irregular frontal slices. In this work, we propose POPLAR method that imposes graph Laplacians constraints induced by the similarity symmetric tensor as auxiliary information to force decomposition factors to behave similarly and the method is developed using AO-ADMM for 3-way PARAFAC2 tensor decomposition. To the best of our knowledge, POPLAR is the first approach to incorporate graph Laplacians constraints using auxiliary information. We extensively evaluate POPLAR’s performance in comparison to state-of-the-art approaches across synthetic and real dataset, and POPLAR clearly exhibits better performance with respect to the Fitness (better 3−8%), and F1 score (better 5−20%) among the state-of-the-art factorization method. Furthermore, the running time for the method is comparable to the state-of-art method.

关键词： Tensors Laplace equations array signal processing Conferences Force sensor arrays

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Extreme Beam Broadening using Phase Only Pattern Synthesis

Extreme Beam Broadening using Phase Only Pattern Synthesis

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sensor array and multichannel signal processing workshop

作者： G.C. Brown J.C. Kerce M.A. Mitchell Sensors and Electromagnetic Applications Laboratory Georgia Tech Research Institute Georgia Institute of Technology Atlanta GA USA

This paper describes methods for broadening the beam of a phased array antenna using phase-only element weights. This type of broadening can be valuable for improving the search occupancy and/or reducing the search frame time for large phased arrays, when transmit beam broadening is combined with multiple simultaneous receive beams. Phase-only broadening is required for typical solid-state (active phased) arrays because control of the transmit amplitude at each element is not practical. A notable result is that there is a relationship between the amount of beam broadening and the efficiency (directivity) of the resulting beam. In particular it is shown that broadening less than ~2.5:1 results in reduced beam efficiency for both linear and circular arrays, while a broadening of greater than ~2.5:1 results in improved beam efficiency, and that, therefore, certain broadening configurations will be preferable to the radar designer (e.g. 4:1 in one plane rather than 2:1 in two planes, due to a large difference in efficiency). The algorithms used to achieve the broadening patterns presented here are based on a combination of homotopy from known optimality with a stochastic gradient descent approach using a carefully constructed one parameter family of penalty functions

关键词： sensor arrays Apertures Laboratories Radar Robustness

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Blind source separation methods based on output nonlinear correlation for bilinear mixtures of an arbitrary number of possibly correlated signals

Blind source separation methods based on output nonlinear co...

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sensor array and multichannel signal processing workshop

作者： Yannick Deville Shahram Hosseini IRAP (Institut de Recherche en Astrophysique et Planétologie) Université de Toulouse UPS CNRS CNES OMP Toulouse France

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Traditional Blind Source Separation (BSS) methods require quite restrictive properties from the source signals (typically, statistically independent or at least uncorrelated sources) and mixing transform (typically, linear instantaneous mixtures). In this paper, we address a much more complex case, where the considered deterministic source signals may be correlated (we only request the source vectors and some associated vectors to be linearly independent) and where the mixing transform is nonlinear (more precisely, bilinear, which is e.g. of high interest for Earth observation applications). We propose a separation principle leading to a new set of BSS algorithms applicable to an arbitrary number of sources and based on nonlinear correlation parameters. Moreover, we analyze the separability properties of this approach and thus show that it is guaranteed to separate the sources up to the trivial scale and permutation indeterminacies.

关键词： Earth Correlation array signal processing Conferences signal processing algorithms Transforms Blind source separation

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A highly parallel architecture for adaptive multichannel algorithms

A highly parallel architecture for adaptive multichannel alg...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： D. Mansour Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel

In this paper an adaptive lattice structured filter for processing wideband spatial data received by an array of sensors is discribed. Using a special architecture that implements a modified version of the Gram-Schmidt orthogonalization procedure a very simple derivation of number of algorithms involving multichannel inputs are presented. A wavefront array processing architecture is described that solve the following algorithms: multichannel spatio-temporal exact least square adaptive lattice filter, matrix-inversion algorithm, linear equations solution and the Fadeev problem. The same architecture and processor elements are shared by all four algorithms.

关键词： Parallel architectures sensor arrays Lattices Adaptive filters Wideband Adaptive arrays array signal processing Least squares methods Nonlinear filters Equations

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On Detecting Low-Pass Graph signals Under Partial Observations

On Detecting Low-Pass Graph Signals Under Partial Observatio...

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sensor array and multichannel signal processing workshop

作者： Hoang-Son Nguyen Hoi-To Wai Dept. of Computer Science & Engineering The Chinese University of Hong Kong Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.

关键词： array signal processing Conferences Low-pass filters signal processing algorithms Detectors Filtering algorithms Complexity theory

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