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- W2378664271 abstract "Signal parameter estimation is a crucial issue in SAR/ISAR imaging,especially for superimposed chirp signal with single degree of freedom.A new method of parameter estimation based on sparse signal representation is presented,which expands signal on a set of over-complete basis.The method is analyzed and validated for performance through simulation,with three popular signal sparse representation algorithms compared,including BP,FOCUSS and Sparse Bayesian Learning.The result shows that Sparse Bayesian Learning performs better in sparse components than the other two algorithms,which can estimate parameter more efficiently." @default.
- W2378664271 created "2016-06-24" @default.
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- W2378664271 date "2008-01-01" @default.
- W2378664271 modified "2023-09-28" @default.
- W2378664271 title "A New Method for Parameter Estimation of Chirp Signal Based on Sparse Signal Representation" @default.
- W2378664271 hasPublicationYear "2008" @default.
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