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Sparse Semi-Parametric Chirp Estimator

In English

Författare

Summary, in English

In this work, we present a method for estimating the parameters detailing an unknown number of linear chirp signals, using an iterative sparse reconstruction framework. The proposed method is initiated by a re-weighted Lasso approach, and then use an iterative relaxation-based refining step to allow for high resolution estimates. The resulting estimates are found to be statistically efficient, achieving the Cramér-Rao lower bound. Numerical simulations illustrate the achievable performance, offering a notable improvement as compared to other recent approaches.

Avdelning/ar

Publiceringsår

2015-04-27

Språk

Engelska

Sidor

1236-1240

Publikation/Tidskrift/Serie

Signals, Systems and Computers, 2014 48th Asilomar Conference on

Dokumenttyp

Konferensbidrag

Förlag

IEEE - Institute of Electrical and Electronics Engineers Inc.

Ämne

  • Probability Theory and Statistics
  • Signal Processing

Conference name

48th Asilomar Conference on Signals, Systems and Computers, 2014

Conference date

2014-11-02 - 2014-11-05

Conference place

Pacific Grove, California, United States

Status

Published

Forskningsgrupp

  • Statistical Signal Processing
  • Statistical Signal Processing Group
  • Biomedical Modelling and Computation

ISBN/ISSN/Övrigt

  • ISBN: 978-1-4799-8295-0