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检索条件"任意字段=2008 IEEE Sensor Array and Multichannel Signal Processing Workshop"

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The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking

The LOCATA Challenge Data Corpus for Acoustic Source Localiz...

引用

sensor array and multichannel signal processing workshop

作者： Heinrich W. Löllmann Christine Evers Alexander Schmidt Heinrich Mellmann Hendrik Barfuss Patrick A. Naylor Walter Kellermann Friedrich-Alexander University Erlangen-Nürnberg Imperial College London Humboldt-Universität zu Berlin

Algorithms for acoustic source localization and tracking are essential for a wide range of applications such as personal assistants, smart homes, tele-conferencing systems, hearing aids, or autonomous systems. Numerous algorithms have been proposed for this purpose which, however, are not evaluated and compared against each other by using a common database so far. The ieee-AASP Challenge on sound source localization and tracking (LOCATA) provides a novel, comprehensive data corpus for the objective benchmarking of state-of-the-art algorithms on sound source localization and tracking. The data corpus comprises six tasks ranging from the localization of a single static sound source with a static microphone array to the tracking of multiple moving speakers with a moving microphone array. It contains real-world multichannel audio recordings, obtained by hearing aids, microphones integrated in a robot head, a planar and a spherical microphone array in an enclosed acoustic environment, as well as positional information about the involved arrays and sound sources represented by moving human talkers or static loudspeakers.

关键词： arrays Microphone arrays Task analysis array signal processing Acoustics Databases

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Multi-Efficiency Based Resource Allocation for Cognitive Radio Networks with Deep Learning

Multi-Efficiency Based Resource Allocation for Cognitive Rad...

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sensor array and multichannel signal processing workshop

作者： Miao Liu Tiecheng Song Lei Zhang Hikmet Sari Guan Gui National Mobile Communications Research Laboratory Southeast University Nanjing China School of Electrical and Information engineering Jiangsu University of Technology Changzhou China College of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

In this paper, a multi-efficiency based scheme is considered, for not only the energy efficiency (EE) and spectrum efficiency (SE) of the primary users (PUs) and the secondary users (SUs), but also the computing efficiency (CE) of the deep learning based RA algorithm in the cognitive radio networks (CRN). Considering the OFDMA based resource allocation (RA) problem for the underlaying SUs, a weighted sum of the secondary interference power is introduced as the objective to minimize. To solve the problem, a deep learning based reweighted message-passing algorithm (ReMPA) is proposed. Compared to the previous scheme and the traditional scheme, the simulation results show that the proposed scheme has effective improvement on both the SE and EE for PUs and SUs.

关键词： array signal processing sensor arrays Conferences Communications technology Resource management Machine learning

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Listener-Position Adaptive Crosstalk Cancelation Using a Parameterized Superdirective Beamformer

Listener-Position Adaptive Crosstalk Cancelation Using a Par...

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sensor array and multichannel signal processing workshop

作者： Xiaohui Ma Christoph Hohnerlein Jens Ahrens Department of Engineering Aarhus University Aarhus N Denmark Berlin Institute of Technology Berlin Germany Chalmers University of Technology Gothenburg Sweden

We present a method for cancelation of the acoustic crosstalk between the ears of a listener using a linear loudspeaker array. This allows for controlling the signals that arise at listener's ear independently of each other so that binaural cues can be imposed. We employ superdirective near-field beamforming (SDB) over the vast part of the frequency range. Allowing the listener to move in front of the array requires the computation of a new SDB solution for each possible position of the listener, which is a computationally expensive procedure. We show that the beamformer weights exhibit a very smooth evolution for listening positions along a line parallel to the array. This makes it straightforward to parameterize pre-computed beamformer weights with a few parameters for each frequency. Upon realtime execution, the beamformer weights can be determined efficiently for any arbitrary position along the listener contour from the parameters with negligible error. Simulation results show that the proposed beamformer provides a higher channel separation than previously published solutions while maintaining robustness.

关键词： Loudspeakers Ear Crosstalk array signal processing Robustness sensor arrays

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Hybrid Particle Filtering Based on an Elitist Resampling Scheme

Hybrid Particle Filtering Based on an Elitist Resampling Sch...

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sensor array and multichannel signal processing workshop

作者： Mhd Modar Halimeh Christian Huemmer Andreas Brendel Walter Kellermann Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

In this paper, the elitist resampling particle filter (ERPF) is introduced. The ERPF is an approach to combine generic particle filters, i.e., the SIS and SIR particle filter, based on an evolutionary selection of particles, which introduces a longterm memory to the selected group of the particles. Thereby, the ERPF aims at fusing the advantages, e.g., robustness and computational efficiency, and mitigating the drawbacks of each filter, e.g., degeneracy and sample impoverishment. Two variants of the ERPF are presented in this paper: the discrete ERPF (DERPF) and the continuous ERPF (CERPF), which to the authors' knowledge represent new forms of particle filters. The two proposed hybrids are compared to the corresponding original filters using two well-established benchmark models to illustrate that the evolutionary combinations provide a better estimation accuracy and exhibit an increased robustness against outliers.

关键词： Estimation Particle filters Robustness Sociology Statistics array signal processing

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Linear-Time Joint Probabilistic Data Association for Multiple Extended Object Tracking

Linear-Time Joint Probabilistic Data Association for Multipl...

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sensor array and multichannel signal processing workshop

作者： Shishan Yang Kolja Thormann Marcus Baum Chair for Sensor Technology University of Passau Germany

The Joint Probabilistic Data Association (JPDA) filter for multiple object tracking is based on the assumption that at most one measurement originates from a target object. However, with the development of high-resolution sensors, it is often the case that multiple spatially distributed detections are obtained from a single object. To tackle this emerging data association challenge, this paper presents a JPDA method based on the Poisson spatial measurement model for extended objects. As the constraint that one target gets at most one measurement is relaxed, the marginal association probabilities can be obtained with linear complexity in the number of measurements and targets. The proposed method is compared to a partition-based multiple extended object tracking algorithm.

关键词： Shape Object tracking Clutter Target tracking Probabilistic logic Shape measurement Time measurement

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MIMO Radar and Communications Spectrum Sharing: A Multiple-Access Perspective

MIMO Radar and Communications Spectrum Sharing: A Multiple-A...

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sensor array and multichannel signal processing workshop

作者： Yu Rong Alex R. Chiriyath Daniel W. Bliss Bliss Laboratory of Information Signals and Systems Arizona State University Tempe AZ

In this paper, we investigate the joint multiple-input multiple-output (MIMO) radar and communications system convergence problem and develop a joint radar-communications performance bound using the concept of estimation rate for radar system and data information rate for communications system. At the radar receiver, we propose a Successive Interference Cancellation (SIC) strategy to estimate radar parameters and decode communications signal simultaneously. While, at the MIMO communications transmitter, we propose a spectrum sharing scheme and show that the optimal communications performance can be achieved through a spectral and spatial waterfilling algorithm. In the end, we show that the Multiple-Access (MAC) performance bound outperforms the result from the spectrally isolated MIMO radar-communications system.

关键词： Radar Silicon carbide Estimation MIMO communication Radar signal processing

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Tracking Multiple Evolving Threats with Cluttered Surveillance Observations

Tracking Multiple Evolving Threats with Cluttered Surveillan...

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sensor array and multichannel signal processing workshop

作者： Zachariah Sutton Peter Willett Yaakov Bar-Shalom Department of ECE University of Connecticut Storrs Connecticut

Many threats in the form of human actions (terrorist attacks, military actions, etc.) can be stochastically modeled by someone with relevant expert knowledge. In this work, a threat is taken to be a sequence of actions that evolve over time and culminate at some ultimate goal. A model would be a hypothesis as to how a threat would develop, and what kind of observable evidence it would produce along the way. This modeling method allows us to attempt detection using the preliminary evidence of a threat. This would theoretically allow the user to take preemptive action; i.e. the user can interfere with the threat before its culmination. This work presents a method of stochastically modeling these types of processes using Hidden Markov Models (HMMs). We then present a detection scheme based on random finite set (RFS) filters (Bernoulli filters) that allows for detection of multiple threat processes using a single cluttered stream of observed data.

关键词： Hidden Markov models Clutter Radio frequency Markov processes array signal processing sensor arrays

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On Transmitter Polarization Optimization for Target Scattering Matrix Estimation

On Transmitter Polarization Optimization for Target Scatteri...

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sensor array and multichannel signal processing workshop

作者： Kexiao Hu Shuli Shi Zhiwen Liu Yougen Xu School of Information and Electronics Beijing Institute of Technology Beijing China

The knowledge about target scattering matrix is essential for adapted and matched polarimetric target illumination. The Cramer-Rao bound (CRB) for target scattering matrix estimation in the presence of compound Gaussian clutter is known to be the function of transmitter polarization. The optimized probing waveform polarimetry design procedure thus can be performed to minimize the CRB for the “best” target scattering matrix estimation. However, to find the optimal transmitter polarization for target scattering matrix estimation, a suboptimal lattice search has to be used in the current method. In this contribution, we demonstrate that the mentioned CRB minimization for target scattering matrix estimation against compound Gaussian clutter can be achieved by simply transmitting several couples of probing pulses with orthogonal polarizations, for example, by transmitting alternately a number of horizontally and vertically polarized pulses just like the traditional scheme for directly measuring the target scattering matrix in the absence of clutter.

关键词： array signal processing sensor arrays Conferences

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Constrained Least Squares for Extended Complex Factor Analysis

Constrained Least Squares for Extended Complex Factor Analys...

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sensor array and multichannel signal processing workshop

作者： Ahmad Mouri Sardarabadi Alle-Jan van der Veen L. V.E. Koopmans Kapteyn Astronomical Institute University of Groningen The Netherlands Delft University of Technology Delft The Netherlands

For subspace estimation with an unknown colored noise, Factor Analysis (FA) and its extensions, denoted as Extended FA (EFA), are good candidates for replacing the popular eigenvalue decomposition (EVD). Finding the unknowns in (E)FA can be done by solving a non-linear least square problem. For this type of optimization problems, the Gauss-Newton (GN) algorithm is a powerful and simple method. The most expensive part of the GN algorithm is finding the direction of descent by solving a system of equations at each iteration. In this paper we show that for (E)FA, the matrices involved in solving these systems of equations can be diagonalized in a closed form fashion and the solution can be found in a computationally efficient way. We show how the unknown parameters can be updated without actually constructing these matrices. The convergence performance of the algorithm is studied via numerical simulations.

关键词： array signal processing sensor arrays Conferences Ions Indexes Integrated circuits

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Learning Overcomplete Dictionaries from Markovian Data

Learning Overcomplete Dictionaries from Markovian Data

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sensor array and multichannel signal processing workshop

作者： S. Akhavan S. Esmaeili M. Babaie-Zadeh H. Soltanian-Zadeh School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering Sharif University of Technology Tehran Iran

We explore the dictionary learning problem for sparse representation when the signals are dependent. In this paper, a first-order Markovian model is considered for dependency of the signals, that has many applications especially in medical signals. It is shown that the considered dependency among the signals can degrade the performance of the existing dictionary learning algorithms. Hence, we propose a method using the Maximum Log-likelihood Estimator (MLE) and the Expectation Minimization (EM) algorithm to learn the dictionary from the signals generated under the first-order Markovian model. Simulation results show the efficiency of the proposed method in comparison with the state-of-the-art algorithms.

关键词： Dictionaries Sparse matrices Machine learning Markov processes Minimization Optimization signal processing algorithms

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