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Dimension-Reduced Radio Astronomical Imaging Based on Sparse Reconstruction

Dimension-Reduced Radio Astronomical Imaging Based on Sparse...

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sensor array and multichannel signal processing workshop

作者： Shuimei Zhang Yujie Gu Chang-Hee Won Yimin D. Zhang Department of Electrical and Computer Engineering Temple University Philadelphia PA USA

Modern radio telescopes commonly use antenna arrays to achieve high-resolution imaging, and various beamforming techniques have been developed in radio astronomy to generate dirty images. Because the manifold of a radio telescope array varies over time due to Earth rotation, beamformers are separately designed and implemented at each time epoch, and the resulting images are averaged over multiple epochs to form enhanced dirty images. Because astronomical scenes are typically sparse, we propose a new method through sparse reconstruction to obtain clean astronomical images. To reduce the computational complexity, a singular value decomposition based compressive sensing scheme is applied. The proposed method offers reduced computational complexity while maintaining the high quality of the sparse reconstruction. Unlike traditional beamforming techniques which require an additional deconvolution procedure for clean image formation, the proposed technique provides clean astronomical images directly with accurate estimation of the source position and intensity.

关键词： Imaging Image reconstruction Antenna arrays Extraterrestrial measurements array signal processing Radio astronomy Antenna measurements

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A Neyman-Pearson Type sensor Censoring Scheme for Compressive Distributed Sparse signal Recovery

A Neyman-Pearson Type Sensor Censoring Scheme for Compressiv...

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sensor array and multichannel signal processing workshop

作者： Jwo-Yuh Wu Ming-Hsun Yang Tsang-Yi Wang Department of Electrical and Computer Engineering National Chiao Tung University Taiwan Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

To strike a balance between energy efficiency and data quality control, this paper proposes a Neyman-Pearson type sensor censoring scheme for distributed sparse signal recovery via compressive-sensing based on wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor (i) directly transmits the real-valued compressed data if the sensing vector support is detected to be overlapped with the signal support, (ii) sends a one-bit hard decision if empty support overlap is inferred, (iii) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. Computer simulations are used to illustrate the performance of the proposed scheme.

关键词： sensors Wireless sensor networks signal reconstruction Pollution measurement Matching pursuit algorithms array signal processing

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Online Localization of Multiple Moving Speakers in Reverberant Environments

Online Localization of Multiple Moving Speakers in Reverbera...

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sensor array and multichannel signal processing workshop

作者： Xiaofei Li Bastien Mourgue Laurent Girin Sharon Gannot Radu Horaud INRIA Grenoble Rhône-Alpes Faculty of Engineering Bar-Ilan University Israel

This paper addresses the problem of online multiple moving speakers localization in reverberant environments. The direct-path relative transfer function (DP-RTF), as defined by the ratio between the first taps of the convolutive transfer function (CTF) of two microphones, encodes the inter-channel direct-path information and is thus used as a localization feature being robust against reverberation. The CTF estimation is based on the cross-relation method. In this work, the recursive least-square method is proposed to solve the cross-relation problem, due to its relatively low computational cost and its good convergence rate. The DP-RTF feature estimated at each time-frequency bin is assumed to correspond to a single speaker. A complex Gaussian mixture model is used to assign each observed feature to one among several speakers. The recursive expectation-maximization algorithm is adopted to update online the model parameters. The method is evaluated with a new dataset containing multiple moving speakers, where the ground-truth speaker trajectories are recorded with a motion capture system.

关键词： Microphones Estimation Reverberation signal processing algorithms Indexes Speech processing array signal processing

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Performance Analysis of Multi-Hop Relaying Cooperation for Internet-of-Things System

Performance Analysis of Multi-Hop Relaying Cooperation for I...

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sensor array and multichannel signal processing workshop

作者： Baofeng Ji Bingbing Xing Huahong Ma Chunguo Li Hong Wen Luxi Yang Institute of Atmospheric Physics of Chinese Academy of Sciences Beijing China School of Information Engineering Henan University of Science and Technology Luoyang China University of Electronic Science and Technology of China Chengdu China School of Information Science and Engineering Southeast University Nanjing China

Performance analysis is studied in this paper for the wireless transmissions in Internet-of-things (IoT) system, where both the direct link and the multi-hop relaying caching wireless transmission from the source node to the destination node are taken into the consideration. The key feature is the Nakagami channels of the wireless channel from the source node to the destination node, which results in the difficulty of the theoretical analysis over the system performance. To tackle this difficulty, the probability distribution function (PDF) of the received signal to noise ratio (SNR) at the destination node is derived by exploiting the function and integral properties. Then, the outage probability and bit error rate(BER) of the whole wireless IoT system are derived in the analytical expression without any approximation. Numerical simulations demonstrate the accuracy of the derived theoretical analysis for this system.

关键词： Relays Wireless communication Wireless sensor networks Manganese Probability density function signal to noise ratio Power system reliability

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Compressive Sensing Based Direction-of-Arrival Estimation in MIMO Radars in Presence of Strong Jamming via Blocking Matrix

Compressive Sensing Based Direction-of-Arrival Estimation in...

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sensor array and multichannel signal processing workshop

作者： Beiyi Liu Guan Gui Shin-Ya Matsushita Li Xu Department of Electronics and Information Systems Akita Prefectural University Akita Japan College of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

The performances of the traditional anti-jamming DOA methods for the colocated MIMO radar system will be seriously deteriorated when the number of the snapshots is small. In this paper, a compressive sensing (CS)-based anti-jamming DOA estimation with only a few snapshots is proposed. In the new DOA estimation, the information on the direction of strong jamming is used to construct a blocking matrix that can block the jamming component effectively. Hence, only the desired signal component is left after preprocessing with the blocking matrix. Then, the DOA estimation problem with a blocking matrix is formulated as a sparse recovery problem, and it is solved by the orthogonal matching pursuit (OMP) algorithm. We compare the proposed method with the traditional CS-based method and the traditional anti-jamming DOA methods. Numerical results demonstrate that the proposed method outperforms the traditional methods.

关键词： Jamming Direction-of-arrival estimation Estimation MIMO radar Sparse matrices sensor arrays Matching pursuit algorithms

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EEG-Based Classification of Emotional State Using an Autonomous Vehicle Simulator

EEG-Based Classification of Emotional State Using an Autonom...

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sensor array and multichannel signal processing workshop

作者： Corey Park Shervin Shahrdar Mehrdad Nojoumian Department of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca Raton FL

Societal acceptance of self-driving cars (SDC) is predicated on a level of trust between humans and the autonomous vehicle. Although the performance of SDCs has improved dramatically, the question of mainstream acceptance and requisite trust is still open. We are exploring this question through integration of virtual reality SDC simulator and an electroencephalographic (EEG) recorder. In order for a passenger to build and maintain trust, the SDC will need to operate in a manner that elicits positive emotional response and avoids negative emotional response. In our experiment, a test subject was exposed to scenarios designed to induce positive and negative emotional responses, quantified by the EEG beta wave to alpha wave power ratio. As predicted, an increase in the beta to alpha power ratio was observed when the test subject was exposed to stress inducing situations inside the SDC simulator. Our results are expected to inform the design and operation of an EEG-based supervisory feedback control module or artificial intelligence (AI) that monitors the emotional state of passengers and adjusts the AI control parameters accordingly.

关键词： Electroencephalography Brain modeling Psychology signal processing Monitoring Conferences Electrodes

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Reduced-Order Zero-Forcing Beamforming vs Optimal Beamforming and Dirty Paper Coding and Massive MIMO Analysis

Reduced-Order Zero-Forcing Beamforming vs Optimal Beamformin...

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sensor array and multichannel signal processing workshop

作者： Christo KurisummoottiL Thomas Dirk Slock EURECOM Sophia-Antipolis France

Optimal linear transmitter beamformers in multi-antenna multi-user systems are of the Minimum Mean Squared Error (MMSE) type (dual uplink MMSE receivers). MMSE designs make an optimal compromise between noise enhancement and interference suppression and reduce to matched filters at low SNR and zero-forcing at high SNR. We consider a realistic scenario of user channels of varying attenuation and constrain the beamformers to either zero-force or ignore each interference term. This leads to a reduced-order zero-forcing (RO-ZF) design in which the number of interference sources being zero-forced increases with SNR. We apply a simple large systems analysis (applicable to Massive MIMO) to determine the asymptotic performance of RO-ZF designs, determine the optimal ZF orders, and compare to optimal and ZF linear and Dirty Paper Coding (DPC) designs. RO-ZF designs lead to variable reductions of computational complexity and channel state information (CSI) requirements (esp. in future multi-cell extensions), both important considerations in Massive MIMO systems.

关键词： Interference System analysis and design signal to noise ratio MIMO communication Covariance matrices array signal processing MISO communication

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Performance of the Asynchronous Consensus Based Bundle Algorithm in Lossy Network Environments

Performance of the Asynchronous Consensus Based Bundle Algor...

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sensor array and multichannel signal processing workshop

作者： Matthew Rantanen Jalil Modares Nicholas Mastronarde Farshad Ghanei Karthik Dantu Department of Electrical Engineering University at Buffalo Buffalo New York Department of Computer Science and Engineering University at Buffalo Buffalo New York

We study multi-agent task allocation where multiple tasks must be divided among multiple autonomous robots. Algorithms for solving such problems are typically developed under the assumption of perfect communication, without considering the lossy nature of the underlying wireless network. In this paper, leveraging a sophisticated unmanned aerial vehicle (UAV) network simulation platform, we investigate the sensitivity of a well-known decentralized task allocation framework to realistic communication constraints. In particular, we use the University at Buffalo's Airborne Networking and Communications (UB-ANC) Emulator to demonstrate that the Asynchronous Consensus Based Bundle Algorithm (ACBBA) deviates from its desired theoretical behavior when it is deployed in a realistic (lossy) network setting, especially as the number of agents (UAVs) and number of tasks increase. This may manifest in the form of the same task being assigned to multiple agents and/or some tasks not being assigned at all.

关键词： Task analysis Resource management Drones signal processing algorithms Planning Robot sensing systems

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Robust Sparse Phase Retrieval from Differential Measurements Using Reweighted $L_{1}$ Minimization

Robust Sparse Phase Retrieval from Differential Measurements...

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sensor array and multichannel signal processing workshop

作者： Pulak Sarangi Piya Pal Department of Electrical and Computer Engineering University of California San Diego

We consider the problem of sparse Fourier phase retrieval where the goal is to recover a sparse signal from the magnitudes of its Fourier transform. In our earlier work, it was shown that using a certain differential measurement model, it is possible to perform sparse Fourier phase retrieval with near-minimal sample complexity. Such measurements are realizable using interferometric imaging techniques and can remove the ambiguities inherent in Fourier phase retrieval in the noiseless setting. However, in presence of noise, such a differential measurement model can potentially lead to noise amplification and degrade the performance as sparsity increases. To address this issue, we propose to use a reweighted l 1 minimization algorithm in conjunction with our differential measurement model. Reweighted l 1 minimization has been successfully adopted in compressed sensing where it shows good performance in presence of large sparsity. We demonstrate a sparse phase retrieval algorithm based on reweighted l 1 minimization which shows superior performance in low signal to Noise ratio (SNR) and high sparsity regime.

关键词： Minimization signal processing algorithms Phase measurement signal to noise ratio Extraterrestrial measurements Robustness Noise measurement

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Finite-Alphabet Beamformed NOMA for Multiuser MISO Broadcast Visible Light Communications

Finite-Alphabet Beamformed NOMA for Multiuser MISO Broadcast...

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sensor array and multichannel signal processing workshop

作者： Yan-Yu Khang Jian-Kang Zhang Hong-Yi Yu National Digital Switching System Engineering & Technological Research Center Zhengzhou China McMaster University Hamilton Canada

In this paper, we consider an optimal finite-alphabet non-orthogonal multiple access (NOMA) design for a multiuser multi-input single-output (MISO) visible light communication (VLC) broadcast system when channel state information is available at the transmitter. By utilizing the cooperation of multiuser interference, an optimal multidimensional additively uniquely decomposable constellation group (AUDCG) is designed to maximize the received worst-case minimum Euclidean distance of all users subject to a normalized average optical power within the orthants of a real-valued space. This optimal AUDCG is proved to be commonly used pulse amplitude (PAM) with an optimal beamformer, which is the optimal solution to a linear max-min programming problem. In addition, our optimal AUDCG admits fast demodulation of the sum signal from a noisy received signal as well as fast decoding individual signal from the estimated sum signal. Computer simulations indicate that our proposed finite-alphabet NOMA design has significantly better error performance than currently available zero-forcing (ZF), minimum mean square error (MMSE) and time-division orthogonal access (TDMA) transmission schemes for multiuser MISO VLC broadcast systems.

关键词： MISO communication Optical transmitters Receivers Demodulation Decoding array signal processing Interference

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