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Robust Adaptive Beamforming signal Techniques for Drone Surveillance

Robust Adaptive Beamforming Signal Techniques for Drone Surv...

引用

sensor array and multichannel signal processing workshop

作者： Shangang Fan Jiangbo Liu Mengqian Tian Hao Huang Fei Dai Lie Yang Guan Gui NJUPT College of Telecommunication and Information Engineering Nanjing China UESTC School of Electronic Engineering Chengdu China

Drone surveillance poses a big technical challenge in the signal beamforming due to the drones' small size and low flying speed at low altitude collisions and etc. In a drone surveillance system, robust adaptive beamforming is required to identify suspicious targets. However, interference motion and array steering vector (ASV) mismatch problems often occurs in the situation of the antenna platform motion or propagation channel variability. To solve these problems, we propose a robust adaptive beamforming algorithms, which can broaden interference nulls. Specifically, by introducing a norm constraint, the proposed algorithm produces a broad trough at the direction of interference and adopts an idea of worst case performance optimization to robustly against the ASV mismatch. Simulation results demonstrate the validity of the proposed algorithm in terms of both output beampattern and signal-to-interference-plus-noise ratio (SINR).

关键词： Interference Covariance matrices array signal processing Robustness signal processing algorithms signal to noise ratio Antenna arrays

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Towards a model-based development approach for Wireless sensor-Actuator Network protocols (position paper) 4

Towards a model-based development approach for Wireless Sens...

引用

4th ACM workshop on Design, Modeling and Evaluation of Cyber Physical Systems, CyPhy 2014

作者： Kumar S., A. Ajith Simonsen, Kent I.F. Bergen University College Norway University of Agder Norway Bergen University College Norway Danish Technical University Norway

ISBN: (纸本)9781450328715

Model-Driven Software Engineering (MDSE) is a promising approach for the development of applications, and has been well adopted in the embedded applications domain in recent years. Wireless sensor Actuator Networks consisting of resource constrained hardware and platform-specific operating system is one application area where the advantages of MDSE can be exploited. Code-generation is an integral part of MDSE, and using a multi-platform code generator as a part of the approach has several advantages. Due to the automated code-generation, it is possible to obtain time reduction and prevent errors induced due to manual translations. With the use of formal semantics in the modeling approach, we can further ensure the correctness of the source model by means of verification. Also, with the use of network simulators and formal modeling tools, we obtain a verified and validated model to be used as a basis for code-generation. the aim is to build protocols with shorter design to implementation time and efforts, along with higher confidence in the protocol designed.

关键词： Network protocols

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Covariance estimation in time varying ARMA processes

Covariance estimation in time varying ARMA processes

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sensor array and multichannel signal processing workshop

作者： Ami Wiesel Amir Globerson Hebrew University of Jerusalem Israel

We consider large scale covariance estimation using a small number of samples in applications where there is a natural ordering between the random variables. the two classical approaches to this problem rely on banded covariance and banded inverse covariance structures, corresponding to time varying moving average (MA) and autoregressive (AR) models, respectively. Motivated by this analogy to spectral estimation and the well known modeling power of autoregressive moving average (ARMA) processes, we propose a novel time varying ARMA covariance structure. Similarly to known results in the context of AR and MA, we address the completion of an ARMA covariance matrix from its main band, and its estimation based on random samples. Finally, we examine the advantages of our proposed methods using numerical experiments.

关键词： Covariance matrix Estimation Autoregressive processes Vectors Mathematical model signal processing Equations

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Distributed detection of correlated random processes under energy and bandwidth constraints

Distributed detection of correlated random processes under e...

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sensor array and multichannel signal processing workshop

作者： Juan Augusto Maya Leonardo Rey Vega Cecilia G. Galarza Andrés Altieri University of Buenos Aires CSC-CONICET Buenos Aires Argentina

We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN). Using Large Deviation theory (LDT), we compute the probability error exponents of a distributed scheme for detecting a correlated circularly-symmetric complex Gaussian process under the Neyman-Pearson framework. Using an analog scheme, the sensors transmit scaled versions of their measurements several times through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. In the analysis, we consider the energy constraint on each node transmission. We show that the proposed distributed scheme requires relatively few MAC channel uses to achieve the centralized error exponents when detecting correlated Gaussian processes.

关键词： Detectors Bandwidth Wireless sensor networks Covariance matrices Gaussian processes Vectors Communication channels

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An L1-norm linearly constrained LMS algorithm applied to adaptive beamforming

An L1-norm linearly constrained LMS algorithm applied to ada...

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sensor array and multichannel signal processing workshop

作者： José F. de Andrade Marcello L. R. de Campos José A. Apolinário Program of Electrical Engineering (PEE)-COPPE Federal University of Rio de Janeiro Rio de Janeiro Brazil Department of Electrical Engineering (SE/3) Military Institute of Engineering Rio de Janeiro Brazil

We propose in this work an L 1 -norm Linearly-Constrained Least-Mean-Square (L 1 -CLMS) algorithm. In addition to the linear constraints present in the CLMS algorithm, the L 1 -CLMS algorithm takes into account an L 1 -norm penalty on the filter coefficients. the performance of the L 1 -CLMS algorithm is evaluated for a time-varying system identification under Gaussian noise and for an adaptive beamforming scenario. the effectiveness of the L 1 -CLMS algorithm is demonstrated by comparing, via computer simulations, its results with the CLMS algorithm. When employed in a sensor array, the L 1 -norm constraint increases the convergence rate making the proposed algorithm a good candidate for adaptive beamforming applications.

关键词： signal processing algorithms arrays Convergence array signal processing Heuristic algorithms Azimuth Vectors

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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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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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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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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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A novel approach to rank determination of multichannel data covariance matrices

A novel approach to rank determination of multichannel data ...

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ieee workshop on Statistical signal and array processing

作者： P.M. Djuric Department of Electrical Engineering State University of New York Stony Brook Stony Brook NY USA

the rank selection problem of a multichannel data covariance matrix is addressed by Bayesian methodology. A maximum a posteriori solution is derived, and a bootstrap technique for its implementation proposed. Our rule is tested on simulated sensor array data that represent random signals embedded in white Gaussian noise. the tests include comparisons with the popular AIC and MDL criteria. the results show that the Bayesian rule outperforms them, particularly for low signal-to-noise ratios and small direction-of-arrival separations.

关键词： Covariance matrix sensor arrays Eigenvalues and eigenfunctions Testing Bayesian methods Gaussian noise array signal processing Parameter estimation signal to noise ratio signal processing

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