Abstract
Classical linear prediction methods based on leastsquare estimation yields radar images with high side lobes and many spurious scattering centers while singular value decomposition (SVD) truncation used to address these issues decreases the dynamic range of the image. So, radar images provided by these methods are not appropriate for classification purposes. In this work, sparsity constraints are induced on the prediction coefficients. The classification results demonstrate that the proposed sparse linear prediction methods give better accuracy rates compared to Multiple Signal Classification (MUSIC) method conventionally used for limited bandwidthobservation angle data. Classification performances of proposed methods are also investigated in case of the missing backscattered data. It is shown that the proposed methods are not affected from the missing data unlike the MUSIC method whose performance decreases with the increase in the percentage of the missing data.
Original language | English |
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Title of host publication | 2016 24th European Signal Processing Conference, EUSIPCO 2016 |
Publisher | European Signal Processing Conference, EUSIPCO |
Pages | 1936-1940 |
Number of pages | 5 |
ISBN (Electronic) | 9780992862657 |
DOIs | |
Publication status | Published - 28 Nov 2016 |
Event | 24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary Duration: 28 Aug 2016 → 2 Sept 2016 |
Publication series
Name | European Signal Processing Conference |
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Volume | 2016-November |
ISSN (Print) | 2219-5491 |
Conference
Conference | 24th European Signal Processing Conference, EUSIPCO 2016 |
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Country/Territory | Hungary |
City | Budapest |
Period | 28/08/16 → 2/09/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- BPDN
- BPDN with regularization term
- LASSO
- Missing data, classification
- Radar imaging
- Sparse linear prediction