The Experts below are selected from a list of 267 Experts worldwide ranked by ideXlab platform
Albertjan Boonstra - One of the best experts on this subject based on the ideXlab platform.
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Spatial Filtering of rf interference in radio astronomy using a reference antenna array
IEEE Transactions on Signal Processing, 2016Co-Authors: Ahmad Mouri Sardarabadi, Allejan Van Der Veen, Albertjan BoonstraAbstract:Radio astronomical observations are increasingly contaminated by RF interference. Assuming an array of telescopes, a previous technique considered Spatial Filtering based on projecting out the interferer array signature vector. A disadvantage is that this effectively reduces the array by one (expensive) telescope. In this paper, we consider extending the astronomical array with a reference antenna array, and develop Spatial Filtering algorithms for this situation. The information from the reference antennas improves the quality of the interferer signature vector estimation, hence more of the interference can be projected out. Moreover, since only the covariance data of the astronomical array has to be reconstructed, the conditioning of the problem improves as well. The algorithms are tested both on simulated and experimental data.
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Spatial Filtering of rf interference in radio astronomy
IEEE Signal Processing Letters, 2002Co-Authors: J Raza, Albertjan Boonstra, A J Van Der VeenAbstract:We investigate Spatial Filtering techniques for interference removal in multichannel radio astronomical observations. The techniques are based on the estimation of the Spatial signature vector of the interferer from short-term Spatial covariance matrices followed by a subspace projection to remove that dimension from the covariance matrix, and by further averaging. The projections will also modify the astronomical data, and hence a correction has to be applied to the long-term average to compensate for this. As shown by experimental results, the proposed technique leads to significantly improved estimates of the interference-free covariance matrix.
Martin Bogdan - One of the best experts on this subject based on the ideXlab platform.
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Spatial Filtering based on canonical correlation analysis for classification of evoked or event related potentials in eeg data
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014Co-Authors: Martin Spuler, Armin Walter, Wolfgang Rosenstiel, Martin BogdanAbstract:Classification of evoked or event-related potentials is an important prerequisite for many types of brain-computer interfaces (BCIs). To increase classification accuracy, Spatial filters are used to improve the signal-to-noise ratio of the brain signals and thereby facilitate the detection and classification of evoked or event-related potentials. While canonical correlation analysis (CCA) has previously been used to construct Spatial filters that increase classification accuracy for BCIs based on visual evoked potentials, we show in this paper, how CCA can also be used for Spatial Filtering of event-related potentials like P300. We also evaluate the use of CCA for Spatial Filtering on other data with evoked and event-related potentials and show that CCA performs consistently better than other standard Spatial Filtering methods.
Barry Giesbrecht - One of the best experts on this subject based on the ideXlab platform.
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single trial classification of event related potentials in rapid serial visual presentation tasks using supervised Spatial Filtering
IEEE Transactions on Neural Networks, 2014Co-Authors: Hubert Cecotti, Miguel P Eckstein, Barry GiesbrechtAbstract:Accurate detection of single-trial event-related potentials (ERPs) in the electroencephalogram (EEG) is a difficult problem that requires efficient signal processing and machine learning techniques. Supervised Spatial Filtering methods that enhance the discriminative information in EEG data are commonly used to improve single-trial ERP detection. We propose a convolutional neural network (CNN) with a layer dedicated to Spatial Filtering for the detection of ERPs and with training based on the maximization of the area under the receiver operating characteristic curve (AUC). The CNN is compared with three common classifiers: 1) Bayesian linear discriminant analysis; 2) multilayer perceptron (MLP); and 3) support vector machines. Prior to classification, the data were Spatially filtered with xDAWN (for the maximization of the signal-to-signal-plus-noise ratio), common Spatial pattern, or not Spatially filtered. The 12 analytical techniques were tested on EEG data recorded in three rapid serial visual presentation experiments that required the observer to discriminate rare target stimuli from frequent nontarget stimuli. Classification performance discriminating targets from nontargets depended on both the Spatial Filtering method and the classifier. In addition, the nonlinear classifier MLP outperformed the linear methods. Finally, training based AUC maximization provided better performance than training based on the minimization of the mean square error. The results support the conclusion that the choice of the systems architecture is critical and both Spatial Filtering and classification must be considered together.
Tang Bin - One of the best experts on this subject based on the ideXlab platform.
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A Spatial Spectrum Estimation Method Based on the Spatial Filtering Approach
Signal Processing, 2010Co-Authors: Tang BinAbstract:The paper has proposed a Spatial spectrum estimation method based on the Spatial Filtering approach.After the overlap subarrays are set in the array,the Spatial jamming signals have been filtered and restrained using the adaptive beam formed by the subarray and the SINR is increased for the desired signal.Based on the secondary combination of the subarrays,the direction-of-arrivals are estimated with the outputs of the subarrays and the locations of the subarrays using the Spatial spectrum estimation method in the desired Spatial regions.Simulation results show the Spatial spectrum estimation method based on the Spatial Filtering approach has improved the electromagnetic environment for the desired signal and the estimate accuracy and the anti-jamming ability achieved are better than regular Spatial spectrum estimation method.
Ahmad Mouri Sardarabadi - One of the best experts on this subject based on the ideXlab platform.
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Spatial Filtering of rf interference in radio astronomy using a reference antenna array
IEEE Transactions on Signal Processing, 2016Co-Authors: Ahmad Mouri Sardarabadi, Allejan Van Der Veen, Albertjan BoonstraAbstract:Radio astronomical observations are increasingly contaminated by RF interference. Assuming an array of telescopes, a previous technique considered Spatial Filtering based on projecting out the interferer array signature vector. A disadvantage is that this effectively reduces the array by one (expensive) telescope. In this paper, we consider extending the astronomical array with a reference antenna array, and develop Spatial Filtering algorithms for this situation. The information from the reference antennas improves the quality of the interferer signature vector estimation, hence more of the interference can be projected out. Moreover, since only the covariance data of the astronomical array has to be reconstructed, the conditioning of the problem improves as well. The algorithms are tested both on simulated and experimental data.
