CFAR detection of distributed targets in non-Gaussian disturbance
The subject of detection of spatially distributed targets in non-Gaussian noise with unknown statistics is addressed. At the design stage, in order to cope with the a priori uncertainty, we model noise returns as Gaussian vectors with the same structure of the covariance matrix, but possibly differe...
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Published in | IEEE transactions on aerospace and electronic systems Vol. 38; no. 2; pp. 612 - 621 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.04.2002
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
ISSN | 0018-9251 1557-9603 |
DOI | 10.1109/TAES.2002.1008990 |
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Abstract | The subject of detection of spatially distributed targets in non-Gaussian noise with unknown statistics is addressed. At the design stage, in order to cope with the a priori uncertainty, we model noise returns as Gaussian vectors with the same structure of the covariance matrix, but possibly different power levels (heterogeneous environment). We also assume that a set of secondary data, free of signal components, is available to estimate the correlation properties of the disturbance The proposed detector assumes no a priori knowledge about the spatial distribution of the target scatterers and ensures the constant false alarm rate (CFAR) property with respect to both the structure of the covariance matrix and the power levels. Finally, the performance assessment, conducted modeling the disturbance as a spherically invariant random process (SIRP), confirms its validity to operate in real radar scenarios. |
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AbstractList | The subject of detection of spatially distributed targets in non-Gaussian noise with unknown statistics is addressed. At the design stage, in order to cope with the a priori uncertainty, we model noise returns as Gaussian vectors with the same structure of the covariance matrix, but possibly different power levels (heterogeneous environment). We also assume that a set of secondary data, free of signal components, is available to estimate the correlation properties of the disturbance The proposed detector assumes no a priori knowledge about the spatial distribution of the target scatterers and ensures the constant false alarm rate (CFAR) property with respect to both the structure of the covariance matrix and the power levels. Finally, the performance assessment, conducted modeling the disturbance as a spherically invariant random process (SIRP), confirms its validity to operate in real radar scenarios The subject of detection of spatially distributed targets in non-Gaussian noise with unknown statistics is addressed. At the design stage, in order to cope with the a priori uncertainty, we model noise returns as Gaussian vectors with the same structure of the covariance matrix, but possibly different power levels (heterogeneous environment). We also assume that a set of secondary data, free of signal components, is available to estimate the correlation properties of the disturbance. The proposed detector assumes no a priori knowledge about the spatial distribution of the target scatterers and ensures the constant false alarm rate (CFAR) property with respect to both the structure of the covariance matrix and the power levels. Finally, the performance assessment, conducted modeling the disturbance as a spherically invariant random process (SIRP), confirms its validity to operate in real radar scenarios. The subject of detection of spatially distributed targets in non-Gaussian noise with unknown statistics is addressed. At the design stage, in order to cope with the a priori uncertainty, we model noise returns as Gaussian vectors with the same structure of the covariance matrix, but possibly different power levels (heterogeneous environment). We also assume that aset of secondary data, free of signal components, is available to estimate the correlation properties of the disturbance. The proposed detector assumes no a priori knowledge about the spatial distribution of the target scatterers and ensures the CFAR property with respect to both the structure of the covariance matrix and the power levels. Finally, the performance assessment, conducted modeling the disturbance as a spherically invariant random process (SIRP), confirms its validity to operate in real radar scenarios. (Author) |
Author | De Maio, A. Ricci, G. Conte, E. |
Author_xml | – sequence: 1 givenname: E. surname: Conte fullname: Conte, E. organization: Dipt. di Ingegneria Elettronica e delle Telecomunicazioni, Universiti degli Studi di Napoli "Federico II", Naples, Italy – sequence: 2 givenname: A. surname: De Maio fullname: De Maio, A. organization: Dipt. di Ingegneria Elettronica e delle Telecomunicazioni, Universiti degli Studi di Napoli "Federico II", Naples, Italy – sequence: 3 givenname: G. surname: Ricci fullname: Ricci, G. |
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References | ref13 ref12 ref15 ref14 ref20 ref11 ref10 ref2 ref1 ref17 gerlach (ref9) 2000 conte (ref21) 1991; 138 ref19 ref18 ref8 ref7 ref4 ref6 van trees (ref16) 1968 moon (ref3) 1991; 138 kelly (ref5) 1989 |
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SubjectTerms | Aircraft components Clutter Constant false alarm rate Covariance matrix Detectors Disturbances Energy resolution Gaussian noise Invariants Noise Non-Gaussian Radar detection Radar scattering Reflection Statistical distributions Working environment noise |
Title | CFAR detection of distributed targets in non-Gaussian disturbance |
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