Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background
This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bai, Er-wei [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on nuclear science - New York, NY : IEEE, 1963, 62(2015), 6, Seite 3274-3282 |
|---|---|
| Übergeordnetes Werk: |
volume:62 ; year:2015 ; number:6 ; pages:3274-3282 |
| Links: |
|---|
| DOI / URN: |
10.1109/TNS.2015.2497327 |
|---|
| Katalog-ID: |
OLC1966227698 |
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| 520 | |a This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such as simulated annealing, can be very slow and iterative algorithms such an Newton's method for maximization can be captured by local maxima while fast. Thus, the selection of the initial estimate is critical to how well they perform. This paper proposes a way to generate such an initial estimate using an averaging process that is shown to be asymptotically convergent to the maximum likelihood source estimate. This ensures that with a sufficiently large number of samples, the initial estimate is indeed within of the basin of attraction of such iterative algorithms. Analytical results are supported by numerical simulations based on a measured background data and synthetically injected source data. | ||
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| 650 | 4 | |a Maximum likelihood estimation | |
| 650 | 4 | |a Simulated annealing | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Econometrics | |
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| 700 | 1 | |a Raptis, Paul |4 oth | |
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| 700 | 1 | |a Mudumbai, Raghuraman |4 oth | |
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10.1109/TNS.2015.2497327 doi PQ20160430 (DE-627)OLC1966227698 (DE-599)GBVOLC1966227698 (PRQ)i571-c8dd7171dea333cf49f833030d72d1f027aebe044a4853dfa6081e27578860490 (KEY)0054996720150000062000603274maximumlikelihoodlocalizationofradioactivesourcesa DE-627 ger DE-627 rakwb eng 620 DNB Bai, Er-wei verfasserin aut Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such as simulated annealing, can be very slow and iterative algorithms such an Newton's method for maximization can be captured by local maxima while fast. Thus, the selection of the initial estimate is critical to how well they perform. This paper proposes a way to generate such an initial estimate using an averaging process that is shown to be asymptotically convergent to the maximum likelihood source estimate. This ensures that with a sufficiently large number of samples, the initial estimate is indeed within of the basin of attraction of such iterative algorithms. Analytical results are supported by numerical simulations based on a measured background data and synthetically injected source data. Numerical simulation Gamma ray detection Gamma-ray detection Parameter estimation Iterative methods Maximum likelihood estimation Simulated annealing Algorithms Econometrics Economic models Heifetz, Alexander oth Raptis, Paul oth Dasgupta, Soura oth Mudumbai, Raghuraman oth Enthalten in IEEE transactions on nuclear science New York, NY : IEEE, 1963 62(2015), 6, Seite 3274-3282 (DE-627)129547352 (DE-600)218510-6 (DE-576)014998238 0018-9499 nnns volume:62 year:2015 number:6 pages:3274-3282 http://dx.doi.org/10.1109/TNS.2015.2497327 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7348750 http://search.proquest.com/docview/1750085821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-PHA GBV_ILN_70 AR 62 2015 6 3274-3282 |
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10.1109/TNS.2015.2497327 doi PQ20160430 (DE-627)OLC1966227698 (DE-599)GBVOLC1966227698 (PRQ)i571-c8dd7171dea333cf49f833030d72d1f027aebe044a4853dfa6081e27578860490 (KEY)0054996720150000062000603274maximumlikelihoodlocalizationofradioactivesourcesa DE-627 ger DE-627 rakwb eng 620 DNB Bai, Er-wei verfasserin aut Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such as simulated annealing, can be very slow and iterative algorithms such an Newton's method for maximization can be captured by local maxima while fast. Thus, the selection of the initial estimate is critical to how well they perform. This paper proposes a way to generate such an initial estimate using an averaging process that is shown to be asymptotically convergent to the maximum likelihood source estimate. This ensures that with a sufficiently large number of samples, the initial estimate is indeed within of the basin of attraction of such iterative algorithms. Analytical results are supported by numerical simulations based on a measured background data and synthetically injected source data. Numerical simulation Gamma ray detection Gamma-ray detection Parameter estimation Iterative methods Maximum likelihood estimation Simulated annealing Algorithms Econometrics Economic models Heifetz, Alexander oth Raptis, Paul oth Dasgupta, Soura oth Mudumbai, Raghuraman oth Enthalten in IEEE transactions on nuclear science New York, NY : IEEE, 1963 62(2015), 6, Seite 3274-3282 (DE-627)129547352 (DE-600)218510-6 (DE-576)014998238 0018-9499 nnns volume:62 year:2015 number:6 pages:3274-3282 http://dx.doi.org/10.1109/TNS.2015.2497327 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7348750 http://search.proquest.com/docview/1750085821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-PHA GBV_ILN_70 AR 62 2015 6 3274-3282 |
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10.1109/TNS.2015.2497327 doi PQ20160430 (DE-627)OLC1966227698 (DE-599)GBVOLC1966227698 (PRQ)i571-c8dd7171dea333cf49f833030d72d1f027aebe044a4853dfa6081e27578860490 (KEY)0054996720150000062000603274maximumlikelihoodlocalizationofradioactivesourcesa DE-627 ger DE-627 rakwb eng 620 DNB Bai, Er-wei verfasserin aut Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such as simulated annealing, can be very slow and iterative algorithms such an Newton's method for maximization can be captured by local maxima while fast. Thus, the selection of the initial estimate is critical to how well they perform. This paper proposes a way to generate such an initial estimate using an averaging process that is shown to be asymptotically convergent to the maximum likelihood source estimate. This ensures that with a sufficiently large number of samples, the initial estimate is indeed within of the basin of attraction of such iterative algorithms. Analytical results are supported by numerical simulations based on a measured background data and synthetically injected source data. Numerical simulation Gamma ray detection Gamma-ray detection Parameter estimation Iterative methods Maximum likelihood estimation Simulated annealing Algorithms Econometrics Economic models Heifetz, Alexander oth Raptis, Paul oth Dasgupta, Soura oth Mudumbai, Raghuraman oth Enthalten in IEEE transactions on nuclear science New York, NY : IEEE, 1963 62(2015), 6, Seite 3274-3282 (DE-627)129547352 (DE-600)218510-6 (DE-576)014998238 0018-9499 nnns volume:62 year:2015 number:6 pages:3274-3282 http://dx.doi.org/10.1109/TNS.2015.2497327 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7348750 http://search.proquest.com/docview/1750085821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-PHA GBV_ILN_70 AR 62 2015 6 3274-3282 |
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10.1109/TNS.2015.2497327 doi PQ20160430 (DE-627)OLC1966227698 (DE-599)GBVOLC1966227698 (PRQ)i571-c8dd7171dea333cf49f833030d72d1f027aebe044a4853dfa6081e27578860490 (KEY)0054996720150000062000603274maximumlikelihoodlocalizationofradioactivesourcesa DE-627 ger DE-627 rakwb eng 620 DNB Bai, Er-wei verfasserin aut Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such as simulated annealing, can be very slow and iterative algorithms such an Newton's method for maximization can be captured by local maxima while fast. Thus, the selection of the initial estimate is critical to how well they perform. This paper proposes a way to generate such an initial estimate using an averaging process that is shown to be asymptotically convergent to the maximum likelihood source estimate. This ensures that with a sufficiently large number of samples, the initial estimate is indeed within of the basin of attraction of such iterative algorithms. Analytical results are supported by numerical simulations based on a measured background data and synthetically injected source data. Numerical simulation Gamma ray detection Gamma-ray detection Parameter estimation Iterative methods Maximum likelihood estimation Simulated annealing Algorithms Econometrics Economic models Heifetz, Alexander oth Raptis, Paul oth Dasgupta, Soura oth Mudumbai, Raghuraman oth Enthalten in IEEE transactions on nuclear science New York, NY : IEEE, 1963 62(2015), 6, Seite 3274-3282 (DE-627)129547352 (DE-600)218510-6 (DE-576)014998238 0018-9499 nnns volume:62 year:2015 number:6 pages:3274-3282 http://dx.doi.org/10.1109/TNS.2015.2497327 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7348750 http://search.proquest.com/docview/1750085821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-PHA GBV_ILN_70 AR 62 2015 6 3274-3282 |
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10.1109/TNS.2015.2497327 doi PQ20160430 (DE-627)OLC1966227698 (DE-599)GBVOLC1966227698 (PRQ)i571-c8dd7171dea333cf49f833030d72d1f027aebe044a4853dfa6081e27578860490 (KEY)0054996720150000062000603274maximumlikelihoodlocalizationofradioactivesourcesa DE-627 ger DE-627 rakwb eng 620 DNB Bai, Er-wei verfasserin aut Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such as simulated annealing, can be very slow and iterative algorithms such an Newton's method for maximization can be captured by local maxima while fast. Thus, the selection of the initial estimate is critical to how well they perform. This paper proposes a way to generate such an initial estimate using an averaging process that is shown to be asymptotically convergent to the maximum likelihood source estimate. This ensures that with a sufficiently large number of samples, the initial estimate is indeed within of the basin of attraction of such iterative algorithms. Analytical results are supported by numerical simulations based on a measured background data and synthetically injected source data. Numerical simulation Gamma ray detection Gamma-ray detection Parameter estimation Iterative methods Maximum likelihood estimation Simulated annealing Algorithms Econometrics Economic models Heifetz, Alexander oth Raptis, Paul oth Dasgupta, Soura oth Mudumbai, Raghuraman oth Enthalten in IEEE transactions on nuclear science New York, NY : IEEE, 1963 62(2015), 6, Seite 3274-3282 (DE-627)129547352 (DE-600)218510-6 (DE-576)014998238 0018-9499 nnns volume:62 year:2015 number:6 pages:3274-3282 http://dx.doi.org/10.1109/TNS.2015.2497327 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7348750 http://search.proquest.com/docview/1750085821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-PHA GBV_ILN_70 AR 62 2015 6 3274-3282 |
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Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background |
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| title_full |
Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background |
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Bai, Er-wei |
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IEEE transactions on nuclear science |
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IEEE transactions on nuclear science |
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eng |
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600 - Technology |
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2015 |
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Bai, Er-wei |
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10.1109/TNS.2015.2497327 |
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620 |
| title_sort |
maximum likelihood localization of radioactive sources against a highly fluctuating background |
| title_auth |
Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background |
| abstract |
This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such as simulated annealing, can be very slow and iterative algorithms such an Newton's method for maximization can be captured by local maxima while fast. Thus, the selection of the initial estimate is critical to how well they perform. This paper proposes a way to generate such an initial estimate using an averaging process that is shown to be asymptotically convergent to the maximum likelihood source estimate. This ensures that with a sufficiently large number of samples, the initial estimate is indeed within of the basin of attraction of such iterative algorithms. Analytical results are supported by numerical simulations based on a measured background data and synthetically injected source data. |
| abstractGer |
This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such as simulated annealing, can be very slow and iterative algorithms such an Newton's method for maximization can be captured by local maxima while fast. Thus, the selection of the initial estimate is critical to how well they perform. This paper proposes a way to generate such an initial estimate using an averaging process that is shown to be asymptotically convergent to the maximum likelihood source estimate. This ensures that with a sufficiently large number of samples, the initial estimate is indeed within of the basin of attraction of such iterative algorithms. Analytical results are supported by numerical simulations based on a measured background data and synthetically injected source data. |
| abstract_unstemmed |
This paper considers the use of maximum likelihood estimation to localize a stationary source from total gamma ray counts, in an open area setting with a highly fluctuating background. As this turns out to be a highly nonconcave maximization, convergence rates of global convergent algorithms, such as simulated annealing, can be very slow and iterative algorithms such an Newton's method for maximization can be captured by local maxima while fast. Thus, the selection of the initial estimate is critical to how well they perform. This paper proposes a way to generate such an initial estimate using an averaging process that is shown to be asymptotically convergent to the maximum likelihood source estimate. This ensures that with a sufficiently large number of samples, the initial estimate is indeed within of the basin of attraction of such iterative algorithms. Analytical results are supported by numerical simulations based on a measured background data and synthetically injected source data. |
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6 |
| title_short |
Maximum Likelihood Localization of Radioactive Sources Against a Highly Fluctuating Background |
| url |
http://dx.doi.org/10.1109/TNS.2015.2497327 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7348750 http://search.proquest.com/docview/1750085821 |
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| author2 |
Heifetz, Alexander Raptis, Paul Dasgupta, Soura Mudumbai, Raghuraman |
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Heifetz, Alexander Raptis, Paul Dasgupta, Soura Mudumbai, Raghuraman |
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10.1109/TNS.2015.2497327 |
| up_date |
2024-07-03T20:51:09.943Z |
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