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检索条件"任意字段=Sensor Array and Multichannel Signal Processing Workshop"

共 2593 条记录，以下是341-350 订阅

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Discrete-Phase Waveform Design to Quadratic Optimization via an ADPM Framework with Convergence Guarantee

Discrete-Phase Waveform Design to Quadratic Optimization via...

引用

sensor array and multichannel signal processing workshop

作者： Xianxiang Yu Guolong Cui Zhenghong Zhang Lin Zhou Jing Yang Lingjiang Kong School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Electronic Information Control Laboratory Chengdu China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper considers a quadratic optimization problem in radar discrete-phase waveform design under similarity and constant modulus constraints. A computationally efficient iterative algorithm based on the Alternating Direction Penalty Method (ADPM) framework is proposed. In each iteration, it converts the considered problem into two subproblems with closed-form solutions via an introduced auxiliary variable, while locally increasing the penalty factor involved in the ADPM framework. The proposed algorithm is ensured to converge for any initialization under some mild conditions and avoids the non-convergence problem of the Alternating Direction Method of Multipliers (ADMM) when handling the NP-hard problems. Finally, numerical simulations demonstrate that the proposed algorithm can outperform their counterparts by providing better objective values.

关键词： Phased arrays NP-hard problem signal processing algorithms Radar Numerical simulation Convex functions Iterative methods

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Enhanced DOA Estimation for MIMO radar in the Case of Limited Snapshots

Enhanced DOA Estimation for MIMO radar in the Case of Limite...

引用

sensor array and multichannel signal processing workshop

作者： Yanan Ma Xianbin Cao Xiangrong Wang School of Electronic and Information Engineering Beihang University Beijing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Multiple-input-multiple-output (MIMO) radar is well-known for providing high-resolution direction-of-arrival (DOA) estimation by forming a large-scaled sum coarray utilizing waveform diversity. However, the sacrifice is that a large number of snapshots are required to estimate the sample covariance matrix. When the number of training snapshots is limited, the performance of subspace-based DOA estimation method, such as multiple signal classification (MUSIC), deteriorates due to the distortion of noise subspace. In order to improve the accuracy of DOA estimation using MIMO radar in the case of few snapshots, we propose a method to refine the covariance matrix iteratively. The sampled covariance matrix is iteratively refined by subtracting cross-correlation terms using generalized inner product based on the previous DOA estimates. Finally, the MUSIC algorithm is implemented based on the refined sample covariance matrix to update the DOA estimates until achieving termination condition. Simulation results demonstrate that the additional covariance matrix refinement step enhances the accuracy of DOA estimation using MIMO radar in the case of limited snapshots significantly.

关键词： Training Direction-of-arrival estimation MIMO radar Simulation Estimation signal processing algorithms Multiple signal classification

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Enhanced Online IVA with Switched Source Prior for Speech Separation

Enhanced Online IVA with Switched Source Prior for Speech Se...

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sensor array and multichannel signal processing workshop

作者： Suleiman Erateb Jonathon Chambers Engineering and Digital Technologies CU Coventry UK School of Engineering University of Leicester UK

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Independent vector analysis (IVA) is a blind source separation (BSS) technique that has demonstrated efficiency in separating speech signals from their convolutive mixtures in the frequency domain. Particularly, it avoids the problematic permutation problem by using a multivariate source prior to model statistical inter dependency across the frequency bins of each source signal. The selection of the source prior is vital to the performance of the method. Practical real time systems require an online mode which is performed iteratively as signal data arrive. The performance of the online IVA is measured by the convergence time and steady state separation and accuracy. This paper proposes a novel switched source prior technique to improve the performance of the online IVA algorithm. The techniques switches between two source priors to acquire the better performance properties of both distributions at different stages of the learning algorithm. The switching process is controlled by an adaptive learning scheme as a function of proximity to the target solution. The experimental results demonstrate an enhanced separation performance using real room impulse responses and recorded speech signals.

关键词： Adaptive learning signal processing algorithms Process control Switches Speech enhancement Gaussian distribution Blind source separation

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Flying Radar: Altitude Optimization, Clutter Mitigation and Spectrum Sharing With MIMO Communication System

Flying Radar: Altitude Optimization, Clutter Mitigation and ...

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sensor array and multichannel signal processing workshop

作者： Mohamed Rihan Mahmoud M. Selim Chen Xu Lei Huang The Institute of Intelligent Computing Science Shenzhen University College of Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

According to recent reports, a flying radar (FR) onboard of UAV will provide high fidelity radar information on forward observation missions. However, such desired performance may be degraded when the FR shares the spectrum band with communication systems. In this paper, we study for the first time the coexistence between a FR and MIMO communication system. Specifically, we present an efficient framework to jointly deal with UAV’s altitude optimization, clutter mitigation, and spectrum sharing with MIMO communication system. The proposed framework is based on using transmit precoding at both systems to maximize the SINR of the FR under rate constraints for the communication system, and power budget constraints for both systems. Meanwhile, it optimizes the FR altitude to provide an optimum trade-off between the excreted interference from the communication system encountered at low altitude and high detection probability of the target amongst clutters encountered at high altitude due to line-of-sight. Simulation results show the effectiveness of the proposed framework for scenarios where both clutter and interference exist.

关键词： array signal processing Radar clutter Simulation Switches Receivers Clutter MIMO communication

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Spectral Algorithm for Shared Low-rank Matrix Regressions

Spectral Algorithm for Shared Low-rank Matrix Regressions

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sensor array and multichannel signal processing workshop

作者： Yotam Gigi Sella Nevo Gal Elidan Avinatan Hassidim Yossi Matias Ami Wiesel Google Research

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

We consider multiple matrix regression tasks that share common weights in order to reduce sample complexity. For this purpose, we introduce the common mechanism regression model which assumes a shared right low-rank component across all tasks, but allows an individual per-task left low-rank component. We provide a closed form spectral algorithm for recovering the common component and derive a bound on its error as a function of the number of related tasks and the number of samples available for each of them. Both the algorithm and its analysis are natural extensions of known results in the context of phase retrieval and low rank reconstruction. We demonstrate the efficacy of our approach for the challenging task of remote river discharge estimation across multiple river sites, where data for each task is naturally scarce. In this scenario sharing a low-rank component between the tasks translates to a shared spectral reflection of the water, which is a true underlying physical model. We also show the benefit of the approach in the setting of image classification where the common component can be interpreted as the shared convolution filters.

关键词： Phased arrays signal processing algorithms Estimation Filtering algorithms Reflection Rivers Task analysis

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Hyperspectral Image Clustering based on Variational Expectation Maximization

Hyperspectral Image Clustering based on Variational Expectat...

引用

sensor array and multichannel signal processing workshop

作者： Yuchen Jiao Yirong Ma Yuantao Gu Dept.of Electronic Engineering Tsinghua University Beijing China 69010 Unit PLA Urumqi China Dept. of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Hyperspectral image clustering is an important and challenging problem, which aims to group image pixels according to the land cover information extracted from the spectrum. The spectrum observed at adjacent pixels are often highly-correlated, and leveraging such spatial correlation can greatly improve the clustering accuracy. Markov Random Field (MRF) is a powerful model to characterize such correlation. However, in this model the spatial parameter β often needs to be manually tuned, which brings difficulty in finding an optimal value. In this paper, we propose a novel hyperspectral clustering algorithm, which is able to learn parameter β from the data and thus achieves better performance. Specifically, we model the spectral information with Gaussian mixture model, and use variational expectation maximization method to complete the parameter estimation and clustering task. Experiments on both synthetic and real-world data sets verify the effectiveness of the proposed algorithm.

关键词： Correlation Parameter estimation Graphical models signal processing algorithms Clustering algorithms Task analysis Gaussian mixture model

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Application of Dijkstra Algorithm in Path Planning for Geomagnetic Navigation

Application of Dijkstra Algorithm in Path Planning for Geoma...

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sensor array and multichannel signal processing workshop

作者： Qingya Liu Hanchen Xu Lihui Wang Jin Chen Yaoming Li Lizhang Xu School of Instrument Science and Engineering Southeast University Nanjing China School of Electric Power Engineering Nanjing Institute of Technology Nanjing China Key Laboratory or Modern Agricultural Equipment and Technology Jiangsu University Zhenjiang China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Path planning is one of the key technologies to realize the hidden navigation of underwater vehicles during long-haul. Path planning efficiency and accuracy are at the core of submarine track planning. Combining the navigation task with the geomagnetic map adaptability, the optimal path between the starting point and the target point is searched in the target space. The underwater geomagnetic navigation path planning model is established, and the principle and implementation method of Dijkstra algorithm are analyzed. An underwater geomagnetic navigation path planning model is established, and the Dijkstra algorithm is used for underwater geomagnetic navigation path planning. Combining different local windows in the adaptation area, the path planning calculation time and track cost are optimized. The simulation analyzes the influence of different local windows on the path planning in the adaptation area. The experiment results demonstrate that the Dijkstra algorithm can effectively find the optimal path that satisfies the constraints.

关键词： Geomagnetism Adaptation models Analytical models Target tracking Navigation signal processing algorithms Path planning

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LPI-based Optimal Radar Power Allocation for Target Time Delay Estimation in Joint Radar and Communications System

LPI-based Optimal Radar Power Allocation for Target Time Del...

引用

sensor array and multichannel signal processing workshop

作者： Yijie Wang Chenguang Shi Fei Wang Jianjiang Zhou Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing Univ. Aeronaut. Astronaut.) Ministry of Education Naijing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper explores the low probability of intercept (LPI)-based optimal radar power allocation for target time delay estimation in joint radar and communications system. The basis of LPI-based optimal radar power allocation is to minimize the total power consumption of radar system under the constraints of a specified target time delay estimation accuracy and a quality of service (QoS) of communications base station. The expression for Cramér-Rao lower bound (CRLB) is analytically derived and applied to gauge target time delay estimation accuracy. The resulting optimization problem is non-convex, which can be solved by the approach of linear programming. Several numerical results are provided to verify the superiority of the proposed radar power allocation in terms of the LPI performance of radar system. It is also shown that the LPI performance of radar benefits from cooperation with the communication system by reducing the total radiated energy of radar.

关键词： Delay effects Estimation Radar Quality of service Radar scattering Radar signal processing Resource management

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Low-cost Beamforming-based DOA Estimation with Model Order Determination

Low-cost Beamforming-based DOA Estimation with Model Order D...

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sensor array and multichannel signal processing workshop

作者： Elias Aboutanios Aboulnasr Hassanien School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Department of Electrical Engineering Wright State University Dayton OH USA

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Direction of Arrival (DOA) estimation algorithms generally assume knowledge of the number of sources. This crucial parameter is either determined by the problem or estimated from the available observations prior to the application of the DOA estimators. Model order estimation (MOE) strategies via information theoretic criteria such as the Akaike Information Criterion (AIC), Minimum Description Length (MDL), and Hannan-Quinn Criterion (HQC), are usually implemented using the singular value decomposition (SVD) which is computationally expensive. In this work, we incorporate the information theoretic criteria directly into the recently proposed Fast Iterative Interpolation Beamformer (FIIB), thus avoiding the SVD. We derive the expressions for the likelihood function as well as the penalty parameters of the three criteria in terms of the number of sources. The resulting FIIB with MOE algorithm is then able to at once determine the number of sources and estimate their parameters. Simulation results demonstrate that the FIIB-based MOE outperforms the SVD-based MOE. Furthermore the FIIB with MDL achieves a performance that is very close to the original FIIB algorithm.

关键词： Interpolation Direction-of-arrival estimation Simulation Computational modeling Estimation signal processing algorithms Frequency estimation

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Nonlinear Multiview Analysis: Identifiability and Neural Network-based Implementation

Nonlinear Multiview Analysis: Identifiability and Neural Net...

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sensor array and multichannel signal processing workshop

作者： Qi Lyu Xiao Fu School of EECS Oregon State University Corvallis OR USA

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Multiview analysis aims to extract common information from data entities across different domains (e.g., acoustic, visual, text). Canonical correlation analysis (CCA) is one of the classic tools for this problem, which estimates the shared latent information via linear transforming the different views of data. CCA has also been generalized to the nonlinear regime, where kernel methods and neural networks are introduced to replace the linear transforms. While the theoretical aspects of linear CCA are relatively well understood, nonlinear multiview analysis is still largely intuition-driven. In this work, our interest lies in the identifiability of shared latent information under a nonlinear multiview analysis framework. We propose a model identification criterion for learning latent information from multiview data, under a reasonable data generating model. We show that minimizing this criterion leads to identification of the latent shared information up to certain indeterminacy. We also propose a neural network based implementation and an efficient algorithm to realize the criterion. Our analysis is backed by experiments on both synthetic and real data.

关键词： Science - general Visualization Neural networks signal processing algorithms US Government Transforms Tools

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