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Sampling size in Monte Carlo Bayesian compressive sensing

Sampling size in Monte Carlo Bayesian compressive sensing

引用

sensor array and multichannel signal processing workshop

作者： Ioannis Kyriakides Radmila Pribić Department of Electrical Engineering University of Nicosia Cyprus Sensors Advanced Developments Thales Nederland Delft The Netherlands

Bayesian compressive sensing using Monte Carlo methods is able to handle non-linear, non-Gaussian signal models. the computational expense associated with Monte Carlo methods is, however, a concern especially in scenarios requiring real-time processing. In this work, a theoretical model is derived that provides insight on the relationship between performance and computational expense for a Monte Carlo Bayesian compressive sensing algorithm. the theoretical model is shown to accurately describe the practical performance of the algorithm. Additionally, the theoretical model is able to inexpensively project the algorithm's performance characteristics for various SNRs and computational complexity levels. the model is then useful in assessing the method's performance under different operational requirements.

关键词： Bayes methods signal processing algorithms Monte Carlo methods Compressed sensing Noise measurement Computational modeling Indexes

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Single-Snapshot Adaptive Beamforming

Single-Snapshot Adaptive Beamforming

引用

sensor array and multichannel signal processing workshop

作者： Yujie Gu Yimin D. Zhang Department of Electrical and Computer Engineering Temple University Philadelphia PA USA

Adaptive beamformers are sensitive to model mismatch, especially when the number of training samples is small or the training samples are contaminated by the signal component. In this paper, we consider an extreme scenario where only a single signal-contaminated snapshot is available for adaptive beamformer design. In such a case, we cannot perform direct inversion or eigen-decomposition of the rank-one sample covariance matrix required in adaptive beamformer design. To address this issue, we formulate a sparsity-constrained covariance matrix fitting problem to estimate the spatial spectrum distribution over the observed spatial domain, which is then used for adaptive beamformer design via the sparse reconstruction of the interference-plus-noise covariance matrix. Simulation results demonstrate the performance advantage of the proposed adaptive beamforming algorithm over other beamforming algorithms suitable for the single-snapshot scenario.

关键词： Covariance matrices array signal processing Interference sensor arrays signal to noise ratio Adaptive arrays Optimization

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NON-MYOPIC sensor SCHEDULING FOR A CENTRALIZED sensor NETWORK

NON-MYOPIC SENSOR SCHEDULING FOR A CENTRALIZED SENSOR NETWOR...

引用

2008 ieee sensor array and multichannel signal processing workshop

作者： Himanshu Shah Darryl Morrell Department of Electrical Engineering Arizona State University USA Department of Engineering Arizona State University USA

When tracking a target in a sensor network with constrained resources, the target state estimate error can be significantly reduced using non-myopic sensor scheduling strategies. Integer non-linear programming has been used to obtain myopic sensor schedules [1]. In this paper, we apply it to a non-myopic sensor scheduling scenario consisting of a network of acoustic sensors in a centralized sensor network;there is one fusion center that combines measurements to update target belief. We cast this problem, which we call the Central Node Scheduling problem, as an integer non-linear programming problem with the objective of minimizing the total predicted tracking error over an M step planning horizon subject to sensor usage and start-up cost constraints. Using Monte Carlo simulations, we show the benefits of this approach for the centralized sensor network.

关键词： Planning Target tracking Acoustics Acoustic measurements Approximation methods Schedules Scheduling

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Selective focusing with numerical technique of time reversal operator decomposition

Selective focusing with numerical technique of time reversal...

引用

sensor array and multichannel signal processing workshop

作者： Dinh-Quy Nguyen Xuexin Yap Woon-Seng Gan Yong-Kim Chong Digital Signal Processing Laboratory Nanyang Technological University Singapore

Recently, a time reversal operator method that is applied to ultrasonic detection and focusing with arrays of transducers has been proposed. this method is known as the DORT (Decomposition de lpsilaOperateur de Retournement Temporel). the DORT method requires the measurement of the inter-element impulse responses of the propagating medium for all pairs of elements in the array. Time reversal operator (TRO) is deduced from these responses and diagonalized. the eigenvector and eigenvalue of TRO is analyzed to perform selective focusing on different scatterers. We develop the numerical technique of DORT method by using the Greenpsilas function estimation in homogeneous and multiple scattering medium. this technique can set up the feasibility study of DORT method to perform selective focusing on multiple scatterers.

关键词： Focusing Eigenvalues and eigenfunctions Scattering Transducers Numerical models Media Acoustics

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Frequency Domain Trinicon-Based Blind Source Separation Method with Multi-Source Activity Detection for Sparsely Mixed signals

Frequency Domain Trinicon-Based Blind Source Separation Meth...

引用

sensor array and multichannel signal processing workshop

作者： Zelin Wang Jing Lu Kai Chen Key Laboratory of Modern Acoustics and Institute of Acoustics Nanjing University Nanjing People's Republic of China

the TRINICON (`Triple-N ICA for convolutive mixtures') framework is an effective blind signal separation (BSS) method for separating sound sources from convolutive mixtures. It makes full use of the non-whiteness, non-stationarity and non-Gaussianity properties of the source signals and can be implemented either in time domain or in frequency domain, avoiding the notorious internal permutation problem. It usually has best performance when the sources are continuously mixed. In this paper, the offline dual-channel frequency domain TRINICON implementation for sparsely mixed signals is investigated, and a multi-source activity detection is proposed to locate the active period of each source, based on which the filter updating strategy is regularized to improve the separation performance. the objective metric provided by the BSSEVAL toolkit is utilized to evaluate the performance of the proposed scheme.

关键词： Acoustics Conferences Speech processing array signal processing sensor arrays Blind source separation

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A cognitive approach for ambiguity function shaping

A cognitive approach for ambiguity function shaping

引用

sensor array and multichannel signal processing workshop

作者： A. Aubry A. De Maio B. Jiang S. Zhang Università degli Studi di Napoli Federico II Napoli Italy Industrial and Systems Engineering University of Minnesota Minneapolis Minneapolis MN USA

In this paper, we propose a cognitive approach to design phase-only modulated waveforms sharing a desired ambiguity function. We formulate the problem as a quartic order polynomial optimization problem with a constant modulus constraint on each variable. thus, we devise an optimization procedure based on the Maximum Block Improvement (MBI) approach. At the analysis stage, we assess the performance of the proposed technique showing its capability to properly shape the ambiguity function of the transmitted waveform.

关键词： Optimization Radar Doppler effect Algorithm design and analysis Polynomials Vectors signal processing algorithms

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Time Difference of Arrival (TDOA)-Based Acoustic Source Localization and signal Extraction for Intelligent Audio Classification

Time Difference of Arrival (TDOA)-Based Acoustic Source Loca...

引用

sensor array and multichannel signal processing workshop

作者： Mingsian R. Bai Shih-Syuan Lan Jong-Yi Huang Department of Power Mechanical Engineering National Tsing Hua University Hsinchu Taiwan

An intelligent system is proposed to locate and classify audio source signals in large spaces. the system is composed of a sparsely distributed microphone array and an artificial intelligence (AI) system. A sparse array aimed for acoustic source localization and signal extraction is configured. the localization method is based on time difference of arrival (TDOA). this method begins with estimation of the TDOAs among at least 4 sensors, with the aid of a subspace-based time delay estimation algorithm. Next, a constrained least squares (CLS) algorithm is applied to locate the source in accord with the estimated TDOAs. Once the source is located, the source signal is extracted by using the minimum variance distortionless response (MVDR) beamformer and a postfilter. the extracted audio signals are further classified in light of machine learning. Convolutional Long Short-Term Memory (ConvLSTM) plays a central role in the AI-based classifier. Mel-Frequency Spectral Coefficients (MFSC) serves as the input layer in the ConvLSTM. the performance of the proposed system is quantified by using localization error, audio quality, and F 1 scores. Simulations are undertaken to validate the proposed TDOA-based localization and separation technique.

关键词： Microphones array signal processing sensor arrays signal processing algorithms Conferences Time difference of arrival

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Interferometric array Design Under Regularized Antenna Placements for Interference suppression

Interferometric Array Design Under Regularized Antenna Place...

引用

sensor array and multichannel signal processing workshop

作者： Xiangrong Wang Moeness Amin Elias Aboutanios School of Electronic and Information Engineering Beihang University Beijing China Center for Advanced Communications Villanova University PA USA School of Electrical Engineering University of New South Wales Sydney Australia

Interference suppression is an important task in extensive sensor array applications, including large interferometric arrays in radio astronomy. the interferometric array design in terms of maximizing output signal-to-interference-plus-noise ratio (MaxSINR) calls for freely placing antennas on grid points without imposing any regularity conditions on the array structure. the configured sparse array exhibits multiple drawbacks, most notably, uncontrollable beampatterns with high sidelobes. To overcome these shortcomings, we add conventional beampattern sidelobe constraints in the design formulation. the resulting array is referred to as restricted beampattern array configuration (RBAC). Alternatively, we consider a new regularized placement array configuration (RPAC) strategy, where antenna position constraints are imposed as means for sidelobe control. In this case, the antenna selection is performed such that each segment of grid points must include one antenna, preventing very large inter-element spacing. We formulate the new RPAC problem and compare its solution to that of the RBAC.

关键词： Antenna arrays Interference sensor arrays array signal processing signal to noise ratio Radio frequency

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Patterned complex-valued matrix derivatives

Patterned complex-valued matrix derivatives

引用

sensor array and multichannel signal processing workshop

作者： Are Hjorungnes Daniel P. Palomar UniK - University Graduate Center University of Oslo Kjeller Norway Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong China

A systematic and simple method is proposed for how to find the derivative of complex-valued matrix functions which depend on matrix arguments that contain patterns. the proposed method is developed by means of the chain rule and it is able to handle both linear and nonlinear patterns. One main issue of the proposed method is to identify a matrix function which depends on independent variables and its domain must have the same dimension as the dimension of the set of patterned matrices. In addition, this function must produce all the matrices within the pattern of interest. Some illustrative examples which are relevant for problems in the area of signal processing for communications are presented.

关键词： Matrices Jacobian matrices Symmetric matrices signal processing Matrix decomposition Presses Vectors

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Beamformer design for nonstationary signals by means of interfrequency correlations

Beamformer design for nonstationary signals by means of inte...

引用

sensor array and multichannel signal processing workshop

作者： Tofigh Naghibi Beat Pfister Speech Processing Group Computer Engineering and Networks Laboratory ETH Zurich Switzerland

the wide-sense stationary assumption has been frequently employed in array processing, since it results in uncorrelated frequency bins and consequently major simplifications arise. However, unlike stationary signals, significant interfrequency correlations are observable in nonstationary signals like speech. Here, we drop the stationarity assumption and will show that taking interfrequency correlations into account leads to higher noise reduction. We develop a framework to design nonstationary beamformers similar to the stationary Frost beamformers. Based on the noise model, it can be used to design both fixed and adaptive beamformers. the nonstationary beamformer derived here is a set of time-varying filters and hence can be seen as a set of time-frequency or wavelet transform filters.

关键词： arrays Noise Covariance matrix Correlation Microphones Vectors Power generation

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