The Experts below are selected from a list of 1038 Experts worldwide ranked by ideXlab platform

P Andreani - One of the best experts on this subject based on the ideXlab platform.

  • the correct estimate of the probability of false detection of the matched filter in weak signal detection problems ii further results with application to a set of alma and atca data
    Astronomy and Astrophysics, 2017
    Co-Authors: R Vio, C Verges, P Andreani
    Abstract:

    The matched filter (MF) is one of the most popular and reliable techniques to the detect signals of known structure and amplitude smaller than the level of the Contaminating Noise. Under the assumption of stationary Gaussian Noise, MF maximizes the probability of detection subject to a constant probability of false detection or false alarm (PFA). This property relies upon a priori knowledge of the position of the searched signals, which is usually not available. Recently, it has been shown that when applied in its standard form, MF may severely underestimate the PFA. As a consequence the statistical significance of features that belong to Noise is overestimated and the resulting detections are actually spurious. For this reason, an alternative method of computing the PFA has been proposed that is based on the probability density function (PDF) of the peaks of an isotropic Gaussian random field. In this paper we further develop this method. In particular, we discuss the statistical meaning of the PFA and show that, although useful as a preliminary step in a detection procedure, it is not able to quantify the actual reliability of a specific detection. For this reason, a new quantity is introduced called the specific probability of false alarm (SPFA), which is able to carry out this computation. We show how this method works in targeted simulations and apply it to a few interferometric maps taken with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact Array (ATCA). We select a few potential new point sources and assign an accurate detection reliability to these sources.

  • the correct estimate of the probability of false detection of the matched filter in the detection of weak signals ii further results with application to a set of alma and atca data
    arXiv: Instrumentation and Methods for Astrophysics, 2017
    Co-Authors: R Vio, C Verges, P Andreani
    Abstract:

    The matched filter (MF) is one of the most popular and reliable techniques to the detect signals of known structure and amplitude smaller than the level of the Contaminating Noise. Under the assumption of stationary Gaussian Noise, MF maximizes the probability of detection subject to a constant probability of false detection or false alarm (PFA). This property relies upon a priori knowledge of the position of the searched signals, which is usually not available. Recently, it has been shown that when applied in its standard form, MF may severely underestimate the PFA. As a consequence the statistical significance of features that belong to Noise is overestimated and the resulting detections are actually spurious. For this reason, an alternative method of computing the PFA has been proposed that is based on the probability density function (PDF) of the peaks of an isotropic Gaussian random field. In this paper we further develop this method. In particular, we discuss the statistical meaning of the PFA and show that, although useful as a preliminary step in a detection procedure, it is not able to quantify the actual reliability of a specific detection. For this reason, a new quantity is introduced called the specific probability of false alarm (SPFA), which is able to carry out this computation. We show how this method works in targeted simulations and apply it to a few interferometric maps taken with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact Array (ATCA). We select a few potential new point sources and assign an accurate detection reliability to these sources.

  • The correct estimate of the probability of false detection of the matched filter in weak-signal detection problems
    'EDP Sciences', 2017
    Co-Authors: R Vio, C Verges, P Andreani
    Abstract:

    The matched filter (MF) is one of the most popular and reliable techniques to the detect signals of known structure and amplitude smaller than the level of the Contaminating Noise. Under the assumption of stationary Gaussian Noise, MF maximizes the probability of detection subject to a constant probability of false detection or false alarm (PFA). This property relies upon a priori knowledge of the position of the searched signals, which is usually not available. Recently, it has been shown that when applied in its standard form, MF may severely underestimate the PFA. As a consequence the statistical significance of features that belong to Noise is overestimated and the resulting detections are actually spurious. For this reason, an alternative method of computing the PFA has been proposed that is based on the probability density function (PDF) of the peaks of an isotropic Gaussian random field. In this paper we further develop this method. In particular, we discuss the statistical meaning of the PFA and show that, although useful as a preliminary step in a detection procedure, it is not able to quantify the actual reliability of a specific detection. For this reason, a new quantity is introduced called the specific probability of false alarm (SPFA), which is able to carry out this computation. We show how this method works in targeted simulations and apply it to a few interferometric maps taken with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact Array (ATCA). We select a few potential new point sources and assign an accurate detection reliability to these sources

David J Anderson - One of the best experts on this subject based on the ideXlab platform.

  • doi:10.1155/2007/37485 Research Article Tracking Signal Subspace Invariance for Blind Separation and Classification of Nonorthogonal Sources in Correlated Noise
    2013
    Co-Authors: Karim Oweiss, David J Anderson
    Abstract:

    We investigate a new approach for the problem of source separation in correlated multichannel signal and Noise environments. The framework targets the specific case when nonstationary correlated signal sources contaminated by additive correlated Noise impinge on an array of sensors. Existing techniques targeting this problem usually assume signal sources to be independent, and the Contaminating Noise to be spatially and temporally white, thus enabling orthogonal signal and Noise subspaces to be separated using conventional eigendecomposition. In our context, we propose a solution to the problem when the sources are nonorthogonal, and the Noise is correlated with an unknown temporal and spatial covariance. The approach is based on projecting the observations onto a nested set of multiresolution spaces prior to eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the degree of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the signal sources in the context of “best basis ” selection. The technique shows robustness to source nonstationarities as well as anisotropic properties of the unknown signal propagation medium under no constraints on the array design, and with minimal assumptions about the underlying signal and Noise processes. We illustrate the high performance of the technique on simulated and experimental multichannel neurophysiological data measurements. Copyright © 2007 K. G. Oweiss and D. J. Anderson. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work i

  • tracking signal subspace invariance for blind separation and classification of nonorthogonal sources in correlated Noise
    EURASIP Journal on Advances in Signal Processing, 2007
    Co-Authors: Karim Oweiss, David J Anderson
    Abstract:

    We investigate a new approach for the problem of source separation in correlated multichannel signal and Noise environments. The framework targets the specific case when nonstationary correlated signal sources contaminated by additive correlated Noise impinge on an array of sensors. Existing techniques targeting this problem usually assume signal sources to be independent, and the Contaminating Noise to be spatially and temporally white, thus enabling orthogonal signal and Noise subspaces to be separated using conventional eigendecomposition. In our context, we propose a solution to the problem when the sources are nonorthogonal, and the Noise is correlated with an unknown temporal and spatial covariance. The approach is based on projecting the observations onto a nested set of multiresolution spaces prior to eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the degree of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the signal sources in the context of "best basis" selection. The technique shows robustness to source nonstationarities as well as anisotropic properties of the unknown signal propagation medium under no constraints on the array design, and with minimal assumptions about the underlying signal and Noise processes. We illustrate the high performance of the technique on simulated and experimental multi-channel neurophysiological data measurements.

  • exploiting signal subspace invariance to resolve non stationary non orthogonal sensor array signals in correlated Noise fields
    International Conference on Acoustics Speech and Signal Processing, 2006
    Co-Authors: Karim Oweiss, David J Anderson
    Abstract:

    We investigate a new approach for the problem of source separation/classification of non-orthogonal, non-stationary noisy signals impinging on an array of sensors. We propose a solution to the problem when the Contaminating Noise is temporally and spatially correlated. The observations are projected onto a nested set of multiresolution spaces prior to classical eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the level of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the correlated signal sources in the context of `best basis' selection. The technique shows robustness to source non-stationarity as well as anisotropic properties of the channel characteristics under no constraints on the array design. We illustrate the high performance of the technique on simulated and experimental multichannel neurophysiological data measurements.

R Vio - One of the best experts on this subject based on the ideXlab platform.

  • the correct estimate of the probability of false detection of the matched filter in weak signal detection problems ii further results with application to a set of alma and atca data
    Astronomy and Astrophysics, 2017
    Co-Authors: R Vio, C Verges, P Andreani
    Abstract:

    The matched filter (MF) is one of the most popular and reliable techniques to the detect signals of known structure and amplitude smaller than the level of the Contaminating Noise. Under the assumption of stationary Gaussian Noise, MF maximizes the probability of detection subject to a constant probability of false detection or false alarm (PFA). This property relies upon a priori knowledge of the position of the searched signals, which is usually not available. Recently, it has been shown that when applied in its standard form, MF may severely underestimate the PFA. As a consequence the statistical significance of features that belong to Noise is overestimated and the resulting detections are actually spurious. For this reason, an alternative method of computing the PFA has been proposed that is based on the probability density function (PDF) of the peaks of an isotropic Gaussian random field. In this paper we further develop this method. In particular, we discuss the statistical meaning of the PFA and show that, although useful as a preliminary step in a detection procedure, it is not able to quantify the actual reliability of a specific detection. For this reason, a new quantity is introduced called the specific probability of false alarm (SPFA), which is able to carry out this computation. We show how this method works in targeted simulations and apply it to a few interferometric maps taken with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact Array (ATCA). We select a few potential new point sources and assign an accurate detection reliability to these sources.

  • the correct estimate of the probability of false detection of the matched filter in the detection of weak signals ii further results with application to a set of alma and atca data
    arXiv: Instrumentation and Methods for Astrophysics, 2017
    Co-Authors: R Vio, C Verges, P Andreani
    Abstract:

    The matched filter (MF) is one of the most popular and reliable techniques to the detect signals of known structure and amplitude smaller than the level of the Contaminating Noise. Under the assumption of stationary Gaussian Noise, MF maximizes the probability of detection subject to a constant probability of false detection or false alarm (PFA). This property relies upon a priori knowledge of the position of the searched signals, which is usually not available. Recently, it has been shown that when applied in its standard form, MF may severely underestimate the PFA. As a consequence the statistical significance of features that belong to Noise is overestimated and the resulting detections are actually spurious. For this reason, an alternative method of computing the PFA has been proposed that is based on the probability density function (PDF) of the peaks of an isotropic Gaussian random field. In this paper we further develop this method. In particular, we discuss the statistical meaning of the PFA and show that, although useful as a preliminary step in a detection procedure, it is not able to quantify the actual reliability of a specific detection. For this reason, a new quantity is introduced called the specific probability of false alarm (SPFA), which is able to carry out this computation. We show how this method works in targeted simulations and apply it to a few interferometric maps taken with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact Array (ATCA). We select a few potential new point sources and assign an accurate detection reliability to these sources.

  • The correct estimate of the probability of false detection of the matched filter in weak-signal detection problems
    'EDP Sciences', 2017
    Co-Authors: R Vio, C Verges, P Andreani
    Abstract:

    The matched filter (MF) is one of the most popular and reliable techniques to the detect signals of known structure and amplitude smaller than the level of the Contaminating Noise. Under the assumption of stationary Gaussian Noise, MF maximizes the probability of detection subject to a constant probability of false detection or false alarm (PFA). This property relies upon a priori knowledge of the position of the searched signals, which is usually not available. Recently, it has been shown that when applied in its standard form, MF may severely underestimate the PFA. As a consequence the statistical significance of features that belong to Noise is overestimated and the resulting detections are actually spurious. For this reason, an alternative method of computing the PFA has been proposed that is based on the probability density function (PDF) of the peaks of an isotropic Gaussian random field. In this paper we further develop this method. In particular, we discuss the statistical meaning of the PFA and show that, although useful as a preliminary step in a detection procedure, it is not able to quantify the actual reliability of a specific detection. For this reason, a new quantity is introduced called the specific probability of false alarm (SPFA), which is able to carry out this computation. We show how this method works in targeted simulations and apply it to a few interferometric maps taken with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact Array (ATCA). We select a few potential new point sources and assign an accurate detection reliability to these sources

Karim Oweiss - One of the best experts on this subject based on the ideXlab platform.

  • doi:10.1155/2007/37485 Research Article Tracking Signal Subspace Invariance for Blind Separation and Classification of Nonorthogonal Sources in Correlated Noise
    2013
    Co-Authors: Karim Oweiss, David J Anderson
    Abstract:

    We investigate a new approach for the problem of source separation in correlated multichannel signal and Noise environments. The framework targets the specific case when nonstationary correlated signal sources contaminated by additive correlated Noise impinge on an array of sensors. Existing techniques targeting this problem usually assume signal sources to be independent, and the Contaminating Noise to be spatially and temporally white, thus enabling orthogonal signal and Noise subspaces to be separated using conventional eigendecomposition. In our context, we propose a solution to the problem when the sources are nonorthogonal, and the Noise is correlated with an unknown temporal and spatial covariance. The approach is based on projecting the observations onto a nested set of multiresolution spaces prior to eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the degree of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the signal sources in the context of “best basis ” selection. The technique shows robustness to source nonstationarities as well as anisotropic properties of the unknown signal propagation medium under no constraints on the array design, and with minimal assumptions about the underlying signal and Noise processes. We illustrate the high performance of the technique on simulated and experimental multichannel neurophysiological data measurements. Copyright © 2007 K. G. Oweiss and D. J. Anderson. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work i

  • tracking signal subspace invariance for blind separation and classification of nonorthogonal sources in correlated Noise
    EURASIP Journal on Advances in Signal Processing, 2007
    Co-Authors: Karim Oweiss, David J Anderson
    Abstract:

    We investigate a new approach for the problem of source separation in correlated multichannel signal and Noise environments. The framework targets the specific case when nonstationary correlated signal sources contaminated by additive correlated Noise impinge on an array of sensors. Existing techniques targeting this problem usually assume signal sources to be independent, and the Contaminating Noise to be spatially and temporally white, thus enabling orthogonal signal and Noise subspaces to be separated using conventional eigendecomposition. In our context, we propose a solution to the problem when the sources are nonorthogonal, and the Noise is correlated with an unknown temporal and spatial covariance. The approach is based on projecting the observations onto a nested set of multiresolution spaces prior to eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the degree of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the signal sources in the context of "best basis" selection. The technique shows robustness to source nonstationarities as well as anisotropic properties of the unknown signal propagation medium under no constraints on the array design, and with minimal assumptions about the underlying signal and Noise processes. We illustrate the high performance of the technique on simulated and experimental multi-channel neurophysiological data measurements.

  • exploiting signal subspace invariance to resolve non stationary non orthogonal sensor array signals in correlated Noise fields
    International Conference on Acoustics Speech and Signal Processing, 2006
    Co-Authors: Karim Oweiss, David J Anderson
    Abstract:

    We investigate a new approach for the problem of source separation/classification of non-orthogonal, non-stationary noisy signals impinging on an array of sensors. We propose a solution to the problem when the Contaminating Noise is temporally and spatially correlated. The observations are projected onto a nested set of multiresolution spaces prior to classical eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the level of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the correlated signal sources in the context of `best basis' selection. The technique shows robustness to source non-stationarity as well as anisotropic properties of the channel characteristics under no constraints on the array design. We illustrate the high performance of the technique on simulated and experimental multichannel neurophysiological data measurements.

C Verges - One of the best experts on this subject based on the ideXlab platform.

  • the correct estimate of the probability of false detection of the matched filter in weak signal detection problems ii further results with application to a set of alma and atca data
    Astronomy and Astrophysics, 2017
    Co-Authors: R Vio, C Verges, P Andreani
    Abstract:

    The matched filter (MF) is one of the most popular and reliable techniques to the detect signals of known structure and amplitude smaller than the level of the Contaminating Noise. Under the assumption of stationary Gaussian Noise, MF maximizes the probability of detection subject to a constant probability of false detection or false alarm (PFA). This property relies upon a priori knowledge of the position of the searched signals, which is usually not available. Recently, it has been shown that when applied in its standard form, MF may severely underestimate the PFA. As a consequence the statistical significance of features that belong to Noise is overestimated and the resulting detections are actually spurious. For this reason, an alternative method of computing the PFA has been proposed that is based on the probability density function (PDF) of the peaks of an isotropic Gaussian random field. In this paper we further develop this method. In particular, we discuss the statistical meaning of the PFA and show that, although useful as a preliminary step in a detection procedure, it is not able to quantify the actual reliability of a specific detection. For this reason, a new quantity is introduced called the specific probability of false alarm (SPFA), which is able to carry out this computation. We show how this method works in targeted simulations and apply it to a few interferometric maps taken with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact Array (ATCA). We select a few potential new point sources and assign an accurate detection reliability to these sources.

  • the correct estimate of the probability of false detection of the matched filter in the detection of weak signals ii further results with application to a set of alma and atca data
    arXiv: Instrumentation and Methods for Astrophysics, 2017
    Co-Authors: R Vio, C Verges, P Andreani
    Abstract:

    The matched filter (MF) is one of the most popular and reliable techniques to the detect signals of known structure and amplitude smaller than the level of the Contaminating Noise. Under the assumption of stationary Gaussian Noise, MF maximizes the probability of detection subject to a constant probability of false detection or false alarm (PFA). This property relies upon a priori knowledge of the position of the searched signals, which is usually not available. Recently, it has been shown that when applied in its standard form, MF may severely underestimate the PFA. As a consequence the statistical significance of features that belong to Noise is overestimated and the resulting detections are actually spurious. For this reason, an alternative method of computing the PFA has been proposed that is based on the probability density function (PDF) of the peaks of an isotropic Gaussian random field. In this paper we further develop this method. In particular, we discuss the statistical meaning of the PFA and show that, although useful as a preliminary step in a detection procedure, it is not able to quantify the actual reliability of a specific detection. For this reason, a new quantity is introduced called the specific probability of false alarm (SPFA), which is able to carry out this computation. We show how this method works in targeted simulations and apply it to a few interferometric maps taken with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact Array (ATCA). We select a few potential new point sources and assign an accurate detection reliability to these sources.

  • The correct estimate of the probability of false detection of the matched filter in weak-signal detection problems
    'EDP Sciences', 2017
    Co-Authors: R Vio, C Verges, P Andreani
    Abstract:

    The matched filter (MF) is one of the most popular and reliable techniques to the detect signals of known structure and amplitude smaller than the level of the Contaminating Noise. Under the assumption of stationary Gaussian Noise, MF maximizes the probability of detection subject to a constant probability of false detection or false alarm (PFA). This property relies upon a priori knowledge of the position of the searched signals, which is usually not available. Recently, it has been shown that when applied in its standard form, MF may severely underestimate the PFA. As a consequence the statistical significance of features that belong to Noise is overestimated and the resulting detections are actually spurious. For this reason, an alternative method of computing the PFA has been proposed that is based on the probability density function (PDF) of the peaks of an isotropic Gaussian random field. In this paper we further develop this method. In particular, we discuss the statistical meaning of the PFA and show that, although useful as a preliminary step in a detection procedure, it is not able to quantify the actual reliability of a specific detection. For this reason, a new quantity is introduced called the specific probability of false alarm (SPFA), which is able to carry out this computation. We show how this method works in targeted simulations and apply it to a few interferometric maps taken with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact Array (ATCA). We select a few potential new point sources and assign an accurate detection reliability to these sources