Methods of Computer-Aided Analysis of Non-Gaussian Noise and Application to Robust Adaptive Detection. Part 2.
We present a methodology for the modeling of certain non-stationary and non-gaussian random time series data with application to weak signal detection. Some components of the noise, which give it its nongaussian characteristics, can be individually modeled, synthesized and subtracted to provide a ga...
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ftdtic:ADA148879 2023-05-15T15:04:08+02:00 Methods of Computer-Aided Analysis of Non-Gaussian Noise and Application to Robust Adaptive Detection. Part 2. Kirsteins,I Tufts,D W RHODE ISLAND UNIV KINGSTON DEPT OF ELECTRICAL ENGINEERING 1984-10 text/html http://www.dtic.mil/docs/citations/ADA148879 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA148879 en eng http://www.dtic.mil/docs/citations/ADA148879 Availability: Document partially illegible. DTIC AND NTIS Statistics and Probability Cybernetics *TIME SERIES ANALYSIS *SIGNALS *COMPUTER AIDED DIAGNOSIS ALGORITHMS METHODOLOGY DETECTION MULTIVARIATE ANALYSIS MATRICES(MATHEMATICS) ESTIMATES ADAPTIVE SYSTEMS QUANTIZATION COVARIANCE LOW STRENGTH ACOUSTIC DATA MATRIX DISPLAYS Nongaussian noise Robust procedures Text 1984 ftdtic 2016-02-20T23:36:51Z We present a methodology for the modeling of certain non-stationary and non-gaussian random time series data with application to weak signal detection. Some components of the noise, which give it its nongaussian characteristics, can be individually modeled, synthesized and subtracted to provide a gaussian residual. Further, it is shown that this process can also be carried out when signals are present. The proposed methodology is applied to some Arctic Acoustic data using a combination of adaptive differential quantization and adaptive signal estimation algorithms based on singular-value-decomposition of a data matrix which we have developed. The combination of adaptive differential quantization with low-rank approximations to data matrices or estimated covariance matrices is believed to be a new and effective method for multivariable, robust, adaptive detection. Text Arctic Defense Technical Information Center: DTIC Technical Reports database Arctic |
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Defense Technical Information Center: DTIC Technical Reports database |
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English |
topic |
Statistics and Probability Cybernetics *TIME SERIES ANALYSIS *SIGNALS *COMPUTER AIDED DIAGNOSIS ALGORITHMS METHODOLOGY DETECTION MULTIVARIATE ANALYSIS MATRICES(MATHEMATICS) ESTIMATES ADAPTIVE SYSTEMS QUANTIZATION COVARIANCE LOW STRENGTH ACOUSTIC DATA MATRIX DISPLAYS Nongaussian noise Robust procedures |
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Statistics and Probability Cybernetics *TIME SERIES ANALYSIS *SIGNALS *COMPUTER AIDED DIAGNOSIS ALGORITHMS METHODOLOGY DETECTION MULTIVARIATE ANALYSIS MATRICES(MATHEMATICS) ESTIMATES ADAPTIVE SYSTEMS QUANTIZATION COVARIANCE LOW STRENGTH ACOUSTIC DATA MATRIX DISPLAYS Nongaussian noise Robust procedures Kirsteins,I Tufts,D W Methods of Computer-Aided Analysis of Non-Gaussian Noise and Application to Robust Adaptive Detection. Part 2. |
topic_facet |
Statistics and Probability Cybernetics *TIME SERIES ANALYSIS *SIGNALS *COMPUTER AIDED DIAGNOSIS ALGORITHMS METHODOLOGY DETECTION MULTIVARIATE ANALYSIS MATRICES(MATHEMATICS) ESTIMATES ADAPTIVE SYSTEMS QUANTIZATION COVARIANCE LOW STRENGTH ACOUSTIC DATA MATRIX DISPLAYS Nongaussian noise Robust procedures |
description |
We present a methodology for the modeling of certain non-stationary and non-gaussian random time series data with application to weak signal detection. Some components of the noise, which give it its nongaussian characteristics, can be individually modeled, synthesized and subtracted to provide a gaussian residual. Further, it is shown that this process can also be carried out when signals are present. The proposed methodology is applied to some Arctic Acoustic data using a combination of adaptive differential quantization and adaptive signal estimation algorithms based on singular-value-decomposition of a data matrix which we have developed. The combination of adaptive differential quantization with low-rank approximations to data matrices or estimated covariance matrices is believed to be a new and effective method for multivariable, robust, adaptive detection. |
author2 |
RHODE ISLAND UNIV KINGSTON DEPT OF ELECTRICAL ENGINEERING |
format |
Text |
author |
Kirsteins,I Tufts,D W |
author_facet |
Kirsteins,I Tufts,D W |
author_sort |
Kirsteins,I |
title |
Methods of Computer-Aided Analysis of Non-Gaussian Noise and Application to Robust Adaptive Detection. Part 2. |
title_short |
Methods of Computer-Aided Analysis of Non-Gaussian Noise and Application to Robust Adaptive Detection. Part 2. |
title_full |
Methods of Computer-Aided Analysis of Non-Gaussian Noise and Application to Robust Adaptive Detection. Part 2. |
title_fullStr |
Methods of Computer-Aided Analysis of Non-Gaussian Noise and Application to Robust Adaptive Detection. Part 2. |
title_full_unstemmed |
Methods of Computer-Aided Analysis of Non-Gaussian Noise and Application to Robust Adaptive Detection. Part 2. |
title_sort |
methods of computer-aided analysis of non-gaussian noise and application to robust adaptive detection. part 2. |
publishDate |
1984 |
url |
http://www.dtic.mil/docs/citations/ADA148879 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA148879 |
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Arctic |
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Arctic |
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Arctic |
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Arctic |
op_source |
DTIC AND NTIS |
op_relation |
http://www.dtic.mil/docs/citations/ADA148879 |
op_rights |
Availability: Document partially illegible. |
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1766335943771422720 |